All the News about AI Bots Products and Techno | dydu dydu expert in conversational intelligence Wed, 27 Nov 2024 15:15:39 +0000 en-US hourly 1 https://uat.dydu.ai/wp-content/uploads/2021/05/sans_titre__6_-removebg-preview-52x52.png All the News about AI Bots Products and Techno | dydu 32 32 What does the future hold for LLMs? https://uat.dydu.ai/en/what-does-the-future-hold-for-llms/?utm_source=rss&utm_medium=rss&utm_campaign=what-does-the-future-hold-for-llms Wed, 27 Nov 2024 14:20:21 +0000 https://uat.dydu.ai/?p=1236501

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Focus on generative AI” series - Part 3

In the second part of our “Generative AI Focus” series, we looked at how companies can integrate language models effectively, without thinking that LLMs will solve every problem… Indeed, although they offer impressive capabilities, language models need to be used for targeted tasks, where their added value is real.

This article explores the limits and current uses of generative AI, examining the technical, ecological, economic and legal constraints, as well as the challenges that arise. Find out how to integrate these technologies responsibly, so as to maximize their potential without going overboard by (re)discovering Part 2: Limits, uses and challenges of generative AI.

 

The current market for generative AI

LLM suppliers

The generative AI market is booming, with major players such as Microsoft, Amazon, Meta and Google investing large sums in the development of these technologies.  

  • Microsoft has taken a notable lead by investing heavily in OpenAI, the creator of language models such as GPT-4. With an estimated commitment of over $13 billion, Microsoft is now integrating OpenAI’s capabilities into its flagship products, including the Office suite (via Copilot) and its Azure cloud platform, thus strengthening its position in generative AI.
  • Amazon, meanwhile, has announced a strategic partnership with Anthropic, a key player in the sector, through an investment of up to $4 billion*. This agreement will enable Amazon to strengthen its cloud offering with solutions based on advanced AI models, while supporting the development of Claude, Anthropic’s model.
  • Meta et Google, two other US stalwarts, are also investing billions in this technology. Meta is developing its own open source models, such as LLaMA, to democratize access to generative AI, while Google is betting on Bard and its Vertex AI platform to rival solutions from OpenAI and Microsoft.  

It is striking to note that these four dominant players are all based in the USA, reflecting American hegemony in this technological field. This situation raises questions about digital sovereignty and the ability of other regions, such as Europe, to position themselves in the face of these behemoths.

Wordpress

GPT 🇺🇸

(Version 3.5, 4, 4o)

💰 Microsoft : 13M$

SharePoint

Claude 🇺🇸

(Version 2, 2.1, 3, 3.5)

💰 Amazon : 4M$

Shopify

Llama 🇺🇸

(Version 2, 3, 3.1)

logo-prestashop

Gemini 🇺🇸

(Version 1.0, 1.5)

Wordpress

Grok 🇺🇸

Wordpress

Mistral 🇫🇷

(Version : Mixtral)

SharePoint

Ernie 🇨🇳

Shopify

AliceMind 🇨🇳

logo-prestashop

Yandex 🇷🇺

Apple, meanwhile, is distinguishing itself with its strategy: we no longer speak of “Artificial Intelligence” but rather of “Apple Intelligence”*. Currently available on iOS 18, i.e. on Iphones 15 Pro minimum*, they use so-called MLMs (Medium Language Models) like Ferret*, OpenELM*, and DCLM-7B*, to offer compact AI solutions focused on specific use cases. Unlike large LLMs, these models are adapted to directly execute localized tasks on Apple devices. This approach minimizes dependence on remote servers for simple queries, while leaving more complex tasks to OpenAI* models where necessary.

GPU chips, the lifeblood of the war

Another key factor in the market is the use of GPU chips, essential for driving LLMs. Nvidia* dominates this segment, having briefly surpassed the market capitalization of Apple and Microsoft in 2024*. Competitors are responding to this demand, and some customers, such as Microsoft, are developing their own AI chips* .

To give an idea of the importance of GPU chips, Llama 3.1, the first LLM model to be released on the market, required 16,000 H100 GPU chips to drive it, at a total cost of around €500 million.

Market size

Estimated at $5.2 billion* in 2024, the AI market is up +7.5% on 2023. As a result, the size of the market is colossal, and could even reach 100 billion by 2028*. Yet, contrary to appearances, profitability remains a challenge: OpenAI and Anthropic, for example, are still posting substantial annual losses* . The early days of Microsoft Copilot also illustrated the financial challenges of generative AI, with early versions whose costs exceeded the revenues generated*. At the same time, some sectors, notably consulting, are taking full advantage. For example, IBM recorded a $1 million* gain in its consulting business thanks to generative AI, a sign of the positive impact of these technologies for companies that integrate them strategically.

The Future of LLMs

Bubble or no bubble?

As investment pours in, one question persists: will AI solve problems to the tune of the $1,000 billion being invested? The “Magnificent 7” – Apple, Nvidia, Alphabet, Meta, Microsoft, Amazon and Tesla – are spending billions on AI projects, despite a significant gap between operating costs and current revenues. According to the Gartner Hype Curve, we’ve reached the peak of the bubble, but these giants still have solid balance sheets to support the future growth and profitability of AI.

Source : Courbe du hype de Gartner

Other avenues of exploration around LLM models

Creating high-performance language models is becoming increasingly complex and costly. As a result, attention is now turning towards smaller, more specialized solutions, rather than continuing to develop ever-larger LLMs.

Here are a few areas of innovation to consider:

  1. Compact models: SLMs* (Small Langage Models) and MLMs* (Medium Langage Models) are being explored, to offer targeted performance while reducing costs. Companies such as Apple (with the integration of models in iPhones*) and Microsoft (with phi3*), are turning their attention to this type of model.
  2. Specialized models: Focused on specific tasks to deliver greater efficiency and profitability, these models are designed for specific sectors. Example:
    – Meditron*, designed by Meta, to help healthcare professionals make clinical decisions and diagnoses.
    – Florence2*, for image recognition.
    – Spreadcheet*, for data processing in spreadsheets.
  3. Multi-modal models*: Capable of processing text, image and sound, these models enable richer applications, such as medical image analysis with text descriptions, or audio interpretation for security.
  4. Enhanced code generation*: LLMs evolve to produce more accurate and optimized code, accelerating software development and supporting technical teams.
  5. Enhanced security and confidentiality*: New approaches are emerging, such as data decentralization and advanced encryption, to guarantee confidentiality and reduce the risk of misuse of data processed by models.
  6. Customization*:Tailoring models to specific business needs makes them more relevant and effective for targeted applications.

Google & the generative AI industry

The online search industry is in the throes of change, under the growing influence of generative AI. Google is still the world leader, with over 90%* market share, but faces growing competition from alternative engines (DuckDuckGo, Baidu, Yandex… ). The rise of LLM, notably with ChatGPT, has changed the industry, even prompting Bing to integrate this technology to strengthen its position. Despite persistently high advertising revenues, Google is seeing a decline in profits on its partner sites, a sign of a possible shift towards AI-based search technologies. Faced with this revolution, investment in generative AI has exploded, with $13 billion invested by 2023, redefining the online search landscape and forcing the giants to adapt.

 

Dydu and generative AI

By combining Dydu’s expertise with the capabilities of LLMs, our solutions succeed in overcoming some of the current limitations of these models, such as the reliability of responses, the lack of ability to ask for clarification, or processing costs and times. This synergy maximizes their potential while meeting operational and strategic requirements. At Dydu, we believe in a hybrid approach, where the knowledge base management of a bot is combined with the text generation power of an LLM.

Our uses include :

  • Knowledge base creation: we use language models to generate knowledge bases more rapidly, from data bases, to facilitate their enrichment and continuous updating.
  • Parallel NLU: Natural Language Understanding (NLU) is a key technology for understanding and analyzing user intentions. We combine Dydu’s algorithms with the capabilities of LLMs, to improve understanding of complex or ambiguous queries, while increasing the accuracy of responses.
  • Visit RAG method: this method consists of enriching text generation by LLMs by connecting them to specific databases or sources of information. Before producing an answer, the model retrieves reliable and relevant data from a dedicated knowledge base.

This enables us to provide our users with more precise, up-to-date and relevant answers.

Through our positioning, we encourage our employees to use these technologies wisely, taking care to limit their carbon footprint and maximize their economic efficiency for our customers.

As the LLM market evolves, we remain attentive to the ecological and financial challenges of generative AI. With the targeted integration of generative AI, we ensure that we respond precisely and individually to the specific needs of each of our customers.

 

👉 To rediscover the first parts of the “Focus Generative AI” series:

 

(Monitoring and report by Mathieu Changeat)

 

 

 

 

]]> Discovering the fundamentals of LLM https://uat.dydu.ai/en/discovering-the-fundamentals-of-llm/?utm_source=rss&utm_medium=rss&utm_campaign=discovering-the-fundamentals-of-llm Fri, 15 Nov 2024 15:48:08 +0000 https://uat.dydu.ai/?p=1236400

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Focus on generative AI series - Part 1

Generative AI has been a major topic since October 2022 and the release of ChatGPT 3.5 in October 2022. At Dydu, as an expert in conversational AI since 2009, we have followed the development of generative AI closely. In the course of our discussions with our customers, partners and prospects, we have found that the subject remains relatively complex to grasp. What’s more, the ecosystem is evolving very rapidly, which makes the task even more difficult. The aim of this series of articles entitled ‘Focus on generative AI’ is to review the fundamentals of LLMs, deconstruct certain preconceived ideas and provide some food for thought on how to use them.

What is an LLM?

LLMs (Large Language Models) are machine learning models capable of understanding and generating texts in human language. They work by analysing huge datasets of text to learn to predict the words that will come next in a sentence. They then generate coherent responses based on these probabilities. An LLM systematically proposesthe most likelytoken according to its context, and this context is enriched with each new token added. This addition is called ‘inference’, the process of which we will see below.

These models use neural networks to detect relationships between words and sentences, enabling them to produce coherent, relevant texts. You can think of an LLM as an ultra-intelligent writing assistant, drawing on accumulated data from books, articles and online forums to provide relevant, well-constructed answers.

How does LLM work?

Through a process called ‘inference’, the model analyses sequences of words and uses probabilities to predict what will happen next. It continually enriches the context with each added word or phrase, improving the accuracy of its answers.

Let’s take a simple example. If you give the model the start of a sentence such as:

👉 “The sun rises in the east and…”. The LLM will draw on its vast knowledge to predict plausible completions such as ‘ sets in the west ’.

It is therefore important to provide the input prompt (initial context) with as much relevant information as possible, this is prompt engineering.

Source : The Philosophical Ledger

 

The 6 key stages in learning an LLM

1 – Gathering, cleaning and splitting data: The first step in LLM training is to collect textual data from a variety of sources, such as books, press articles, online forums, etc. Once this data has been collected, it is cleaned to eliminate errors, inappropriate content and biases. Once this data has been collected, it is cleaned to eliminate errors, inappropriate content and bias. Finally, the data is broken down into small units (called tokens) that the model will analyse.

For example, GPT-3.5 was trained with 570 GB of text. This represents millions of sentences, analysed to understand the structure and relationships between words.

2 – Initial model configuration (transformed architecture): Once the data is ready, the model is configured. It is based on a specific architecture called transform, designed to process large quantities of textual information quickly and efficiently. At this stage, the size of the model, the number of processing layers and other technical parameters are also defined.

3 – Model training: This is the longest and most expensive stage. The LLM is trained to predict the next word in a sentence based on the previous words. The aim is to minimise errors over time. This process is called supervised learning : the model learns from the correct examples it is given.

Myth #1

Contrary to what you might think, an LLM like ChatGPT does not learn from the questions asked by users. Once the model has been trained, it is frozen, unless it is re-trained with new data.

4 – Checking the model: Once the training is complete, the model is tested. It is presented with new data that it has never seen before to check its ability to generalise and provide correct answers. Performance measures are used to ensure that the model works well before it is made available to users.

5 – Using the model: Once validated, the model can be deployed in different applications. For example, it can be used in chatbots, to generate content, or to provide answers to questions. The model does not improve at this stage: it simply uses what it has learned during its initial training.

6 – Improving the model: If necessary, the model can be re-trained or adjusted. This is called fine-tuning. This makes it possible to refine its responses in specific contexts or to add new data. But once deployed, it does not automatically learn from day-to-day interactions with users.

A few key concepts to help you understand LLM!

Hallucinations: One of the main limitations of LLMs is the phenomenon of ‘hallucinations’, where the model produces incorrect or misleading answers, but in a very convincing way. These errors can be problematic, particularly in professional contexts where accuracy is crucial.

Myth #2

Hallucinations can be avoided, but the reality is that they are inevitable due to their probability-based architecture, lack of deep understanding and the limitations of training data.

Retrieval Augmented Generation (RAG) Semantic indexing: This refers to textual content from a document database that is indexed and then ‘queried’ by a user question. This creates a prompt giving context to the LLM derived from the documents and the user’s question.

Source : @aaurimas_gr

Fine tuning: This involves retraining an LLM that has already been trained with additional context information to specify certain responses.

Source : SuperAnnotate

Optimising the performance of generative AI

The performance of an LLM can be adjusted using several parameters:

  • Temperature: The temperature is an adjustable value between 0 and 1 that can be used to manage the degree of creativity of the model. The higher the temperature (close to 1), the more varied and creative the responses.
  • Max New Tokens: This parameter controls the maximum length of the responses generated.
  • Top P and Top K: These values influence the diversity of words selected by the model to form a response, ensuring a balance between quality and variety.
  • Context window: to generate a response, an LLM needs context. The context window is the maximum size of this context. The larger the context window, the higher the quality of the response, but the longer the generation time.

To conclude, this introduction to generative AI offers a clear overview of its potential and how it works. But it is only the beginning. In the next sections, we will explore the current uses and challenges of generative AI, in particular the limits of LLMs and their role in our digital tools. Finally, we will analyse the market for LLMs and their future in a rapidly evolving technological sector. A not-to-be-missed sequel to anticipate future developments!

👉 Stay tuned for the rest of the ‘Generative AI Focus’ series:

 

 

 

 

 

(Watch and report by Mathieu Changeat)

]]> Limits, uses and challenges of generative AI https://uat.dydu.ai/en/limits-uses-and-challenges-of-generative-ai/?utm_source=rss&utm_medium=rss&utm_campaign=limits-uses-and-challenges-of-generative-ai Fri, 15 Nov 2024 15:06:54 +0000 https://uat.dydu.ai/?p=1236379

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Focus on generative AI series - Part 2

In the first part of the series, we covered the basics of LLMs. We saw that language models are AIs trained on huge text databases to interpret human language and generate coherent responses. There are 6 key stages in building an LLM: data collection and processing, initial configuration, intensive training, verification and improvement. We also covered important concepts such as ‘tokens’, which are units of text used to feed the language model, and ‘fine-tuning’, which involves re-training an already-trained LLM to incorporate additional information. We also took the time to deconstruct a number of preconceived ideas:

  1. Contrary to popular belief, an LLM like ChatGPT does not learn from the questions asked by users.
  2. Hallucinations cannot be avoided due to its probability-based architecture, lack of deep understanding and the limitations of training data.

For more details, we invite you to (re)read part one: Discovering the fundamentals of LLM!

The limits of LLMs

Once a generative AI model has been trained, it becomes static and can no longer be modified, except through the process of fine-tuning, a specific adjustment to adapt the LLM’s responses. To maintain the relevance of its information, ChatGPT, for example, is connected to the Internet and carries out searches in real time via Bing. This enables it to enrich its context and provide up-to-date answers, without requiring a complete overhaul of its initial training.

On the other hand, LLMs do not always respond reliably. A test conducted on StackOverflow revealed that ChatGPT was wrong 54% of the time when answering programming questions. In addition, a study carried out by the University of Hong Kong in 2023 found that 36% of the answers generated by LLMs were incorrect. This shows that, although these models perform well for certain tasks, they cannot always be considered reliable sources, particularly for advanced or complex queries.

Current uses of LLMs

What you can do with an LLM VS what is useful

 

We can really distinguish between what is POSSIBLE to do with LLMs VS what is USEFUL. Do we really need to ask ChatGPT for the weather forecast when a dedicated application can do the job? It’s possible, but is it useful?

We have identified 6 cases where the use of an LLM adds considerable value in several areas:

  • Personal assistants: Large language models power tools such as Microsoft Copilot and Siri, which help users answer questions, manage daily tasks and access information quickly and efficiently.
  • Automatic summaries: These models can summarise long texts, which is particularly useful for processing legal documents, studies or reports in sectors such as finance, research or health.
  • Machine translation: Increasingly powerful, LLMs are used for machine translation, facilitating communications on an international scale.
  • Legal assistant: Larges Languages Models are used to assist legal professionals in analysing and drafting complex legal documents, researching case law and checking compliance. These models are also used to provide quick answers to common legal questions.
  • Virtual therapists (medicine, psychology, etc.) : These can also be used in the field of mental and physical health, as virtual assistants for therapists or as support for patients. They can be used to carry out initial analyses, suggest stress management exercises or provide treatment reminders.
  • Code writing: Tools like GitHub Copilot enable developers to write code faster and improve productivity by providing intelligent suggestions.Although LLMs can generate a variety of texts, summarise lengthy information or even produce advice, the usefulness of an LLM lies in its suitability for practical purposes rather than solely in the scope of its capabilities.

Although LLMs can generate a variety of texts, summarise lengthy information or even produce advice, the usefulness of an LLM lies in its suitability for practical purposes rather than solely in the scope of its capabilities.

Have the consequences of AI on employment been studied?

These technological innovations are redefining many professions, sometimes giving rise to concerns that they may be replacing individuals. Questions are being asked about their limits and their true effectiveness in certain contexts. Some companies are sounding the alarm about the impact of AI on specialised professions. Translation companies*, for example, fear that automation will compromise the accuracy and nuance of their work. Lawyers* are also raising concerns about the reliability of the legal answers generated, and therapists* are worried about the limited empathy of AI models. Indeed, in development, studies* show that the code produced by LLMs often remains at a beginner’s level, still requiring human intervention for complex tasks.

Are LLMs being put to good use?

Increasingly present in businesses, the use of generative AI is not always essential or effective. While attractive for automating tasks, generating content, or improving customer service, LLMs can be overkill for some uses, especially if advanced analysis or personalisation is not critical. Many decision-makers fear that they will miss out on the technological shift* towards AI, but for the best ethical use of these technologies, it is important to assess their real impact, by comparing their advantages with less costly and resource-intensive solutions.

Myth #3

LLMs will solve all the problems.

Models are effective for specific tasks, but they are not a universal solution. For certain needs, simpler, targeted tools are often better suited, less costly and quicker to deploy.

 

LLM challenges

Although LLMs offer undeniable advantages, they face 4 major constraints to their smooth operation: technical, ecological, economic and legal.

Technical constraints:

LLMs impose significant technical constraints, due to their need for colossal computing power to operate efficiently. For example, Microsoft plans to acquire 1.8 million GPU chips by 2024* to support these intensive computing requirements.

Training data must be plentiful and meet certain criteria:

  • They must be qualitative* and come from reliable sources (Wikipedia, press, scientific articles, blogs, etc.).
  • Respect copyright* and personal data*.
  • Be as unbiased as possible*.
  • Multilingual*: Llama 3, for example, has been trained on only 5% of non-English languages.
  • Non-poisoned*: the source data was not manipulated before the models were trained.

Given the scarcity of data available online, a phenomenon known as the ‘data wall’ is emerging. This refers to the difficulty of accessing sufficiently varied, complete and high-quality data sets to train language models effectively. Instead of ‘feeding’ on personalised, unbiased data, free from manipulation, language models risk being fed more and more on synthetic data produced by other LLMs, which can be detrimental to their accuracy and quality.

Ecological:

Recent advances in language models raise important ecological questions, particularly with regard to their energy consumption. According to projections, by 2030, the energy required to run these systems could account for up to 25%* of the total energy supply in the United States, an amount equivalent to that consumed by an entire country*. To cope with this explosion in energy demand, companies like Microsoft are looking at innovative solutions, including the construction of small-scale nuclear reactors*. Discussions are also emerging around harnessing nuclear fusion* to power these technologies, as indicated by reports of OpenAI’s interest in acquiring fusion energy from Helion.

At the same time, the water consumption required to generate and operate LLMs is also a major concern. In 2022, Microsoft consumed nearly 6.4 billion litres of water, a volume equivalent to around 8,700 Olympic swimming pools*. Each generation of content from a Large Language Model requires millions of litres of water, which represents around half a litre of water per interaction with a service like ChatGPT*. These figures illustrate not only the ecological footprint of these technologies, but also the urgent need to find sustainable solutions to minimise their environmental impact.

Economic :

Beyond the technical and ecological challenges, LLMs also involve high economic costs. Electricity consumption has risen sharply*, largely due to the growing needs associated with AI, resulting in higher energy costs for businesses and consumers.

On the other hand, IT resources are also important. For example, the daily operation of ChatGPT requires around $700,000 a day in server resources*, a cost that makes it difficult to make these tools profitable on a large scale. This figure highlights the importance of a robust and expensive infrastructure to ensure a reliable service.

The growing shortage of engineers specialising in AI* further accentuates these economic challenges. With salaries rising to attract talent*, companies must navigate an increasingly competitive labour market, making the implementation and development of these technologies even more expensive. Taken together, these factors contribute to a complex economic dynamic that requires particular attention from players in the sector.

 

Legal :

LLMs also raise legal issues, particularly in terms of personal data protection. On the one hand, compliance with the General Data Protection Regulation (GDPR) is crucial, especially as the majority of generative AI providers are based in the US*. These models may have been trained on personal data*, and the prompts we submit may also be used to refine their learning*. This raises questions about the use of language models with sensitive data and the need to guarantee user confidentiality.

On the other hand, the AI ACT*, which came into force on 1 August 2024, aims to ensure that artificial intelligence systems respect the fundamental rights of European citizens*. This text imposes requirements such as maximum transparency, the updating of technical documents, compliance with European copyright law, and the obligation to provide summaries of the content used for training. It also requires tests to be carried out to guarantee the security of the systems deployed. These measures reflect a growing trend, with similar legislative frameworks being prepared in other countries*, underlining the importance of a consistent regulatory approach to the use of LLMs on a global scale.

In addition, companies must ensure that the data collected and used to train models complies with these legal requirements, or face sanctions.

In conclusion, Large Language Models have transformed our approach to AI and have become indispensable tools in a variety of sectors. However, it is important to recognise their limitations and address the challenges associated with their use. Whether it’s overcoming energy constraints, managing biases or improving the reliability of answers, LLMs require constant attention and careful management if they are to be used to best effect. For example, some departments have equipped themselves with LLMs for tasks where simpler, more cost-effective tools might suffice. This quest for rapid modernisation is also fuelled by the need to improve the user experience, sometimes to the detriment of a full assessment of the usefulness or reliability of language models. To maximise their use, it is therefore important for companies to carefully assess the specific needs of their business and ensure that LLMs are tailored to the intended use cases, taking into account their limitations and their environmental and economic cost.

 

 

👉 Stay tuned for the rest of the ‘Generative AI Focus’ series:

  • Part 3: What future for LLMs? We analyse the LLM market, the outlook for the future and our positioning as experts

 

 

(Monitoring report by Mathieu Changeat)

]]> 5 callbot use cases for town halls! https://uat.dydu.ai/en/5-callbot-use-cases-for-town-halls/?utm_source=rss&utm_medium=rss&utm_campaign=5-callbot-use-cases-for-town-halls Mon, 30 Sep 2024 08:25:29 +0000 https://uat.dydu.ai/?p=1236113

The digitalisation of municipal services is no longer an option, but a necessity in order to meet citizens’ growing expectations in terms of efficiency and responsiveness. Callbots are proving to be powerful tools for improving the quality of public service management .

1. Welcoming and guiding citizens

One of the first benefits of callbots is their ability to take on certain time-consuming tasks for municipal staff. These telephone agents can play a crucial role in guiding citizens to the appropriate services according to their needs. Dydu bots are capable of:

👉 Answer incoming calls: in order to identify the nature of the request (administrative procedures, housing application, local events etc.).
👉 Direct the caller to the relevant department.
👉 Provide a first level of information if necessary.

This process not only reduces the workload for reception staff, but also improves the experience for citizens by providing them with a quick and accurate response.

2. Managing administrative appointments

Administrative appointments can sometimes be complex to manage, whether they concerncivil status, permits or housing applications.

Callbots can :

👉 Manage appointment booking: by asking citizens the right questions to identify their specific needs.
👉 Interact with citizens to offer them an appointment slot.
👉 Send automatic reminders for appointments or reminders for missing documents on a file.
👉 Manage cancellations or modifications.

This system considerably reduces the workload of agents, while offering citizens a 24/7 service.

3. Answers to frequently asked questions

Town halls receive a large number of repetitive questions, and it is possible to programme your callbot so that they can respond instantly on :

  • opening times
  • administrative procedures
  • town planning and housing
  • transport and mobility
  • social assistance and personal services
  • local events

In other words, you can personalise it as you like!

This frees up time for municipal staff to concentrate on more complex tasks. At Dydu, our voice assistants are able to handle over 80% of citizens’ frequently asked questions, reducing the number of calls and emails that council departments have to deal with manually. Our town hall bots (callbot or chatbot) are built with a pre-written knowledge base that can be used as a support and that you can customise and adapt to your needs. Here’s a concrete example with a customer testimonial from Pleaux town hall!

4. Reporting and monitoring incidents

Citizens regularly report incidents such as noise pollution, damage to public thoroughfares or malfunctions in urban facilities.

Voice agents can simplify this process by enabling citizens to:

👉 Report these problems directly via an automated call or chat.
👉 Provide automatic updates on the progress of their report.
👉 Notify citizens when their report has been taken into account, is being processed, or has been resolved.

In this way, you improve the transparency of your services to ensure citizen satisfaction.

5. Satisfaction surveys and citizen participation

Telephone assistants don’t just respond to requests, they can also play a part in:

👉 Assist citizens with their procedures step by step.
👉 Invite citizens to take part in a short satisfaction survey or give their opinion on a local project.
👉 Gather opinions and feedback.
👉 Notify them of new services available.
👉 Issue alerts in the event of major events such as roadworks, water orelectricity cuts, or weather warnings…

Not only does this allow you tooffer a quality of service, it also allows you to measure the quality of the services provided. At the same time, you are encouraging active citizen participation. Dydu bots are effective in carrying out these types of actions and give town halls the opportunity to collect valuable data to improve their services.

Integrating a callbot into your municipal service is an opportunity to modernise it, making it more efficient while reducing your costs by 30%. To find out more about Dydu bots, request your callbot demo!

]]> Reinvent your customer service with callbots! https://uat.dydu.ai/en/reinvent-your-customer-service-with-callbots/?utm_source=rss&utm_medium=rss&utm_campaign=reinvent-your-customer-service-with-callbots Mon, 02 Sep 2024 07:30:00 +0000 https://uat.dydu.ai/?p=1235993

In an environment where technology is advancing at lightning speed and consumer expectations continue to rise, it’s imperative that companies adopt innovative solutions to improve their customer service. Among these solutions, callbots stand out for their ability to revolutionize customer interactions. The French customer relations observatory (Observatoire de la Relation Client) states that 55%* of French people use the telephone to contact their support service. These conversational agents, powered by artificial intelligence (AI), offer continuous assistance, available 24/7, enabling companies to respond to web users’ needs efficiently and, above all, without interruption. Discover how these virtual agents can transform your customer relationships, simplifying processes, generating business and improving satisfaction on both sides of the phone!

Reduce costs, simplify processes

Callbots have a significant impact on reducing operational costs by automating repetitive interactions with users. According to a Capgemini study on conversational interfaces, 76%* of companies report tangible benefits from implementing voice assistants. In particular, this translates into a reduction in customer service costs of over 20%*. By automating responses to frequently asked questions, and directing more complex calls to human advisors, voice assistants reduce staffing requirements while maintaining quality service.

Thanks to their ability to handle simple requests quickly and efficiently, they enable companies to benefit from a reduction in call-backs, which are often costly and time-consuming for businesses. By reducing these redundant interactions, callbots optimize employee time while ensuring greater customer satisfaction. What’s more, reducing the number of calls helps improve employee morale and reduce turnover.

Generate business, pre-qualify calls

By being available 24/7, bots give companies the chance to never miss an opportunity, even outside office hours. They can handle inquiries, provide information on products and services, and even initiate sales, all without human intervention. According to a study by Accenture, companies that take advantage of AI, including callbots, could increase profitability by 38%* by 2035 by increasing productivity.

What’s more, they are an excellent tool in call pre-qualification. Voice assistants can gather relevant information from users before transferring the call to a human advisor, enabling sales teams to focus on high-value opportunities. One of our customers using this tool reports that their bot fully handles 25% of calls and pre-qualifies 60% of the remainder, enabling advisors to shorten application processing time. In this way, intelligent pre-qualification optimizes the use of human resources, improves team efficiency and can also increase conversion rates.

Improving customer satisfaction and the employee experience

One of the most notable benefits of callbots is their ability to eliminate waiting times, which is crucial to delivering a positive user experience. With new consumer expectations on the rise, responsiveness is also key. According to AMARC (Association pour le Management de la Réclamation Client), a response time of less than a few hours translates into 10% perceived tension, while a delay of over a day generates 40% tension. Thanks to bots, Internet users no longer have to wait for answers to their simple questions, which reduces their frustration and improves their overall satisfaction. A Forrester survey shows that 73% of consumers say that valuing their time is the most important thing a company can do to provide good service. On the employee side, these virtual agents can play a supportive role, providing real-time information or taking over time-consuming tasks. This enables them to concentrate on more complex and rewarding interactions, while reducing the stress associated with managing repetitive requests.

Our customer MACSF has implemented a callbot to extend the support department’s opening hours and pre-qualify policyholder requests. The bot prequalifies over 80% of incoming requests, generating a better user experience, an increase in the number of subscriptions and a smoother request processing process.

Today, virtual assistants are essential tools for any company seeking to optimize its customer service and improve the employee experience. By reducing costs, generating qualified leads, and offering a better user experience, these technologies pave the way for more agile and efficient management of customer and employee relations.

👉 To find out more, discover our Callbot Dydu or book your demo!

]]> Tourism: changes in the sector and innovative strategies for optimizing customer service in 2024 https://uat.dydu.ai/en/tourism-changes-in-the-sector-and-innovative-strategies-for-optimizing-customer-service-in-2024/?utm_source=rss&utm_medium=rss&utm_campaign=tourism-changes-in-the-sector-and-innovative-strategies-for-optimizing-customer-service-in-2024 Mon, 22 Jul 2024 10:04:26 +0000 https://uat.dydu.ai/?p=1235649

Since the COVID-19 crisis, the tourism sector in France has experienced a dynamic recovery, marked by a significant increase in the number of travelers and a diversification of tourism offerings. Against this backdrop, optimizing customer service has become a key priority. High-quality customer service is crucial to meeting consumers’ growing expectations, delivering a personalized experience and strengthening customer loyalty.

The tourism market in France

Post-pandemic growth

According to the French Ministry of Europe and Foreign Affairs*, France recorded a 15% increase in international tourist arrivals in 2023 compared to the previous year. Popular tourist regions such as Paris, the Côte d’Azur and the Alps continue to attract large numbers of visitors. The Comité Régional du Tourisme Paris Île-de-France reports that Paris welcomed over 30 million visitors in 2023, a significant increase on previous years.

Investments in infrastructure, including improvements in transport and accommodation, as well as technological innovation, have played a key role in revitalizing the sector. Destination promotion initiatives and international events, such as the Paris 2024 Olympic Games, have also boosted tourist inflows, reinforcing France’s position as one of the world’s tourism leaders.

France continues to innovate to improve the tourism experience, notably through the digitization of services and the adoption of new technologies. These efforts aim to offer visitors a more enriching and personalized experience, while ensuring more efficient management of tourist flows.

The challenges facing the sector in 2024

New consumer expectations

Consumer expectations in the French tourism sector have changed considerably. Travelers now seek more personalized and authentic experiences, favoring tailor-made stays that reflect their preferences and values. Sustainability has also become a major criterion, with a growing demand for options that respect the environment and local cultures.

Furthermore, speed and ease of access to information are essential, with consumers expecting immediate answers and 24/7 support thanks to digital technologies, including conversational robots and multi-channel platforms.

A Deloitte study reveals that 73%* of travelers expect quick and accurate answers to their queries, while 85% prefer companies offering a personalized experience. These changes are forcing the tourism industry to rethink its offers and services in order to respond effectively to these new demands.

The adoption of digital technologies

Technological innovations are playing a crucial role in the revitalization of tourism in France. Emerging technologies such as artificial intelligence (AI), chatbots, and augmented reality (AR) are increasingly being integrated into tourism services. They enrich tourist visits by offering immersive, interactive experiences.

60%* of current tourism companies in France, use chatbots to improve customer experience, customer engagement, manage bookings and provide real-time information. AI-based customer relationship management (CRM) platforms enable in-depth analysis of customer data, enabling companies to better understand and anticipate travelers’ needs.

The growing impact of chatbots and AI in customer service

Optimizing customer relations

Intelligent chatbots and AI are designed to understand and answer customer queries naturally and efficiently. Here are the main ways in which these technologies optimize inquiries and bookings:

  • Instant answers and 24/7 availability: One of the greatest benefits of chatbots is their ability to provide instant answers and be available 24/7. Whether you have questions about cancellation policies, available equipment or payment options, chatbots can respond immediately without human intervention (or with Livechat option). This constant availability dramatically improves customer satisfaction, eliminates frustrating waiting times and offers continuous service at any time of the day or night.
  • Personalized interactions: Thanks to AI, chatbots can offer advanced personalization of interactions. By analyzing each customer’s preferences and interaction history, chatbots can offer tailor-made recommendations. For example, a customer searching for a hotel via Booking.com* will receive suggestions based on their previous searches and bookings. This personalization not only improves the user experience, but also increases the likelihood of conversion and customer satisfaction.
  • Booking automation: Chatbots can also automate the booking process. Through an intuitive conversational interface, customers can make reservations seamlessly. Chatbots guide users every step of the way, from selecting dates and service options to finalizing payment. This automation simplifies the booking process, reduces human error and frees up time for human agents, who can then concentrate on more complex, value-added tasks.

Industry statistics and forecasts

Chatbots and artificial intelligence (AI) are transforming the customer service landscape in many fields combined, including tourism. Adoption of chatbots is booming. According to a report by Grand View Research*, the size of the global chatbot market is expected to reach 9.4 billion USD by 2024. This market is growing at an annual rate of 24. 3% from 2019 to 2024. This rapid growth is fueled by increased demand for automated, efficient customer service solutions. Companies are increasingly recognizing the added value of chatbots in terms of responsiveness and availability.

One of the major benefits of chatbots is their ability to reduce operational costs. According to an IBM study*, companies can save up to 30% on customer support costs by automating repetitive tasks. Chatbots reduce the need for human staff to handle basic queries, enabling agents to focus on more complex, high value-added issues. This optimization of human and technological resources enables companies to improve efficiency while making substantial savings.

As these technologies continue to evolve, their role in transforming customer service will only increase, offering significant benefits for both companies and their customers.

Discover a real-life case study with our customer testimonial 👉 Goelia, a residential rental company, vacation club and travel agency.

]]> Taking action: what are the steps to deploying an HR chatbot quickly? https://uat.dydu.ai/en/taking-action-what-are-the-steps-to-deploying-an-hr-chatbot-quickly/?utm_source=rss&utm_medium=rss&utm_campaign=taking-action-what-are-the-steps-to-deploying-an-hr-chatbot-quickly Tue, 14 May 2024 08:00:00 +0000 https://uat.dydu.ai/?p=1235205

Creating an HR chatbot may seem complex at first, but by following a few simple steps, you can quickly bring your project to fruition.

Steps to deploy an HR chatbot quickly :

👉 Defining the chatbot’s scope and objectives

The first step is to identify and frame the issues to be resolved, as well as establishing the main objectives and use cases. For example, knowing whether the chatbot will be primarily aimed at answering employees’ questions or directing them to the right internal resources.


👉 Selecting the right chatbot platform


It’s important to choose a chatbot platform that meets your specific HR needs. At Dydu, we offer comprehensive conversational agent solutions specifically designed for HR departments.


👉 Creating chatbot content and scripts

For the chatbot to be able to respond, it is mandatory to create a knowledge base for it. Developing the chatbot’s content and scripts is therefore a mandatory step. With Dydu’s HR expert chatbot and its pre-written knowledge base, you can configure your bot much more quickly, adapting it to your internal use cases.


👉 Internal chatbot testing

Before deploying the chatbot to employees, be sure to test it internally to check that it works properly and is easy to use.

👉 Chatbot deployment and communication

Once the chatbot is ready, deploy it and inform your employees of its existence. Show them how the chatbot can benefit them and how to get the most out of it.

For example, if your organization uses Teams as its communication platform, you can deploy your chatbot directly on this instant messenger for quick and easy employee adoption.

👉 Performance monitoring and continuous improvement

Monitor chatbot performance and gather feedback from employees. Use this information to make ongoing improvements.

To find out more about setting up an HR expert bot, take a look at our HR white paper!

]]> HR Chatbots: make life easier for your employees! https://uat.dydu.ai/en/hr-chatbots-make-life-easier-for-your-employees/?utm_source=rss&utm_medium=rss&utm_campaign=hr-chatbots-make-life-easier-for-your-employees Fri, 03 May 2024 13:04:41 +0000 https://uat.dydu.ai/?p=1235194

Are your HR teams overworked? Opt for an HR expert chatbot, a tool for answering simple, recurring questions from employees so that the HR department can focus on special cases.

How can an HR chatbot answer employees’ questions?

HR chatbots represent an innovative solution for responding to the needs and queries of employees within companies. Thanks to their ability to handle queries instantly and provide precise answers, chatbots offer effective assistance on a variety of topics.

Whether for questions relating to leave, benefits, company policy, or even more specific requests such as document retrieval or technical problem resolution, HR chatbots simplify employees’ daily lives. Their 24/7 availability also guarantees access to the information employees need at any time, whatever their time zone or location.

Your ally in liberating Human Resources

Understanding the HR chatbot and how it works at Dydu

Dydu, an expert in this field for over 15 years, offers a complete solution including a pre-written knowledge base on the main HR themes.

With a first ready-to-use version in 2020, we deployed HR expert chatbots, with the aim of enabling companies to set up their internal chatbot quickly. This solution has evolved to include an even more extensive knowledge base, comprising over 300 pre-filled questions and answers covering 20 HR topics.

The pre-written knowledge base enables companies to rapidly deploy their HR chatbot. It offers a wide range of prepared questions, covering topics from administration and benefits to leave management, employment contracts and training. This database of 110 groups of different formulations enables the chatbot to understand and respond to different expressions of the same question. For example, it can recognize different formulations for requests relating to health insurance, leave and other HR aspects, guaranteeing a smooth and efficient user experience.

It is estimated that 60%* of business leaders plan to enhance their HR departments with more AI and automation over the next 5 years; integrating an HR chatbot with a pre-written knowledge base represents a strategic solution to meet these growing needs.

The benefits of expert HR chatbots:

🚀Simplified communication: instant answers to common employee questions, freeing up HR time to focus on more important tasks.

🚀Fast request processing: thanks to natural language processing software, the chatbot quickly processes employee requests without human intervention.

🚀Cost savings: By taking over time-consuming support tasks, the chatbot saves costs while optimizing HR operations.

🚀Accessible 24/7: the chatbot offers employees the opportunity to ask questions at any time, boosting their satisfaction and well-being at work.

🚀 Personalized responses: with an SSO login, our HR bots can offer tailored responses to each employee based on their history, preferences and profile, providing a personalized experience.

The case of PwC: A concrete illustration of the effectiveness of the HR chatbot

In 2019, PwC, a global leader in consulting, auditing and legal expertise, implemented Alex, an HR chatbot developed in partnership with Dydu, to meet the needs of its 6,000 employees.

With an in-depth knowledge base comprising over 2,800 items on a dozen themes, Alex handles an average of over 15,000 interactions per month, with a 97% comprehension rate. This solution enables PwC to offer instant, high-quality assistance to its employees, helping to improve their experience within the company.

In a period of transition to widespread teleworking, Alex has proved particularly useful, with its adaptability and relevance in changing contexts. The ongoing commitment of the team in charge of managing Alex ensures that the chatbot remains a reliable and effective resource for PwC employees, boosting the efficiency of internal operations and overall employee satisfaction.

To find out more about the PwC case study, check out their video testimonial!
To find out more about our HR expert chatbots, please visit our page.

]]> How to Adapt to the Challenges of AI in 2024 ? https://uat.dydu.ai/en/how-to-adapt-to-the-challenges-of-ai-in-2024/?utm_source=rss&utm_medium=rss&utm_campaign=how-to-adapt-to-the-challenges-of-ai-in-2024 https://uat.dydu.ai/en/how-to-adapt-to-the-challenges-of-ai-in-2024/#respond Mon, 08 Apr 2024 08:39:00 +0000 https://uat.dydu.ai/?p=1235102

Read our interview with Mathieu Changeat, co-founder of Dydu, as he discusses the challenges of AI and the strategy adopted by Dydu.

How is generative AI influencing Dydu’s conversational AI solutions in 2024?

Generative AI has been around for several years, but it gained a lot of traction with the emergence of ChatGPT at the end of 2022. The chatbot seemed to understand everything and provide surprisingly realistic answers.Many studies have since revealed biases, errors, and omissions in ChatGPT responses and language models in general. But first impressions stick. Today, our clients and prospects expect to spend less time than before setting up their chatbot. We’re therefore integrating LLMs into our solution to help our chatbots and callbots understand our users’ questions even better, with little administrative work, while still guaranteeing reliable answers.

How does Dydu ensure data security?

We designed our integrations to use any LLM: hosted in the cloud (GPT in Microsoft Azure, Mixtral at mistral.ai or vercel.ai, etc.) or hosted on OVH servers maintained by Dydu (Llama2).

Our minimum requirements include:

  • A GDPR-compliant cloud solution (not yet the case for Open AI)
  • Europe-based servers 

When Dydu provides the hosting, we ensure GDPR compliance and servers in France, even for health data (HDS). No data leaves the Dydu infrastructure, nor is it used to train hosted models.

Can you skip the knowledge base construction process?

Language models, such as GPT4, have been known to “hallucinate,” i.e., provide made-up, and therefore false, answers, even when trained with a thousand billion parameters. We recommend a hybrid approach. Part of the bot’s knowledge is created manually and managed by the knowledge base in the Bot Management System. The other part draws on the client’s existing intranet or website documents. These documents also help to better understand the manually created knowledge questions, based on the language model’s understanding, while providing an answer from the knowledge tree.

They should not be used to answer all questions, though. We recommend managing the following topics in the Dydu knowledge base: 

  • Of a sensitive nature
  • Requiring escalation to another channel depending on the theme in question
  • Requiring connection to an API (Application Programming Interface) on the client’s IS

There are also financial and environmental factors to consider – a request sent to a language model uses greater energy resources and is more costly than one sent to a Dydu chatbot. 

LLMs are currently unsuitable for callbots. They take several seconds to provide an answer, which then has to be vocalized, making the final conversational experience unnatural.

Why do LLMs not always answer the same question in the same way?

The model is based on neural networks using stochastic methods, which means it can produce slightly different results each time. What’s more, the answer may depend on the context of the conversation and the exact wording of the question. Answer variability is therefore normal with this type of model. 

What would you recommend to companies looking to adapt to the challenges of AI?

Remain cautious about the use cases you want to implement and start with tests. I think AI can be very useful for various day-to-day tasks, but don’t overlook biases and imperfections or human know-how.

]]> https://uat.dydu.ai/en/how-to-adapt-to-the-challenges-of-ai-in-2024/feed/ 0 Chatbots and Customer Relations: The Art of Digital Balance https://uat.dydu.ai/en/chatbots-and-customer-relations-the-art-of-digital-balance/?utm_source=rss&utm_medium=rss&utm_campaign=chatbots-and-customer-relations-the-art-of-digital-balance https://uat.dydu.ai/en/chatbots-and-customer-relations-the-art-of-digital-balance/#respond Fri, 16 Feb 2024 11:29:10 +0000 https://uat.dydu.ai/?p=1234837

In the constantly evolving digital age, customer relations are being redefined through the dynamic integration of chatbots. These intelligent conversational agents, powered by artificial intelligence, are transforming customer relationship management. Join us as we explore the significant role chatbots are playing in this changing landscape, as well as the benefits, evolutions, and prospects of this digital revolution.

Using Chatbots to Boost Customer Relations

The Evolving Customer Relationship in the Age of Digital Transformation

Digital transformation has reshaped the way companies interact with their customers, opening up a wide range of possibilities. Customer relations chatbots  have become key assets to support this change. From simple information searches to order tracking, claim management, or after-sales issues, chatbots provide instant 24/7 support on websites and mobile apps. Their constant availability is perfectly in line with the age of instant gratification, meeting customers’ expectations for immediate and personalized answers about products and services. 

Companies can identify the most frequently asked questions and delegate them to their chatbot, enabling customers and prospects to get quick answers that meet their needs. Chatbots can also be interconnected with internal software such as CRM, to create a fully personalized experience. What’s more, integration with livechat technology enables to hand-off to a human operator if necessary, while retaining the dialog history. Phone (callbots) or voice (voicebots) versions enhance accessibility, ensuring an optimal customer experience. 

The Central Role of Chatbots in Customer Satisfaction

Chatbots play a key role in meeting new customer expectations. An estimated 67%* of consumers in the world have interacted with a chatbot over the last 12 months. Chatbots provide instant answers by using customers’ favorite communication channels, such as instant messaging systems. Artificial intelligence also enables chatbots to understand customers’ natural language, analyze their needs, and provide personalized responses. 

This kind of customization has a significant impact. It makes customers feel understood and cared for as individuals, boosting satisfaction. Chatbots are precious allies in the quest for more efficient customer relationship management. They reduce customer waiting times, improve the quality of responses, and optimize the handling of requests. Adopting a chatbot helps build a smoother customer journey, improves satisfaction, and fosters loyalty. Chatbots are particularly well suited to sectors such as e-commerce or banking and insurance, among others. These tools have become a must-have for companies seeking to optimize their customer relationship management.

The Benefits of Chatbots for Businesses and Customers

Benefits for Companies

Chatbots offer customers a wide range of benefits that enhance their experience. But what exactly are these benefits?

  • Increased responsiveness: chatbots are available 24/7 and respond instantly to customer questions, eliminating waiting times.
  • Personalization: API connections and CRM tools enable chatbots to provide tailored answers, integrating specific data. This combination creates more personalized interactions, for a one-to-one customer experience.
  • Easy to use: chatbots use natural language which simplifies interactions.

Benefits for Companies

Chatbots also offer companies significant benefits:

  • Task automation: chatbots automate repetitive tasks, freeing up employee time.
  • Cost reduction: by automating frequent requests, companies can reduce operating costs
  • Improved productivity: chatbots help human agents by providing contextual information, making them more efficient.

A Combined Future for Chatbots and Customer Relations?

The Evolution of Chatbots Towards More Human and Personalized Interactions

Chatbots are constantly evolving to offer ever more human and personalized interactions. The integration of artificial intelligence into these systems enables a deeper understanding of natural language, providing more authentic exchanges with customers. Some chatbots can even detect emotional nuances in conversations and adapt their responses to the customer’s emotional state. The aim is to create interactions where customers feel genuinely cared for in an empathetic way, thus strengthening customer relationship management.

Integrating Chatbots with Other Communication Channels and Advances in Artificial Intelligence 

The future of chatbots lies in their ability to integrate seamlessly with other communication channels. For example, a customer can start a conversation with a chatbot on a website, then continue their discussion on a messaging app. Advances in natural language understanding, personalized recommendations, and the anticipation of customer needs enable chatbots to manage the customer relationship more effectively. This combination of seamless integration and continuous improvement in artificial intelligence makes chatbots a key part of the customer relationship. 

To find out more about how chatbots and artificial intelligence are revolutionizing the customer relationship, download Kiamo’s complete white paper, where you’ll also find an interview with Samir Dilmi, Chief Revenue Officer at Dydu, as he shares key insights on this topic. 

1Chatbot recruitment: the benefits of conversing with applicants 

]]> https://uat.dydu.ai/en/chatbots-and-customer-relations-the-art-of-digital-balance/feed/ 0 How are chatbots transforming the e-commerce sector during peak periods ? https://uat.dydu.ai/en/how-are-chatbots-transforming-the-e-commerce-sector-during-peak-periods/?utm_source=rss&utm_medium=rss&utm_campaign=how-are-chatbots-transforming-the-e-commerce-sector-during-peak-periods https://uat.dydu.ai/en/how-are-chatbots-transforming-the-e-commerce-sector-during-peak-periods/#respond Fri, 16 Feb 2024 11:06:11 +0000 https://uat.dydu.ai/?p=1234825

E-commerce chatbots: an essential digital revolution

How do chatbots work?

Chatbots, or conversational agents, play an increasingly important role, particularly in e-commerce. Based on conversational AI, they are designed to simulate human conversations and interact with users in an automated way, 24/7. For a long time, chatbots were thought of as “that little box at the bottom of the screen” on a website. Today, they’ve been adapted to consumer behavior and integrated into mobile apps or messaging platforms.

Chatbots use natural language to automatically interact with users by answering questions or helping consumers find the right product. These automated conversations are not only made up of “pre-programmed” answers; on the contrary, they are increasingly based on algorithms powered by generative AI, enabling chatbots to improve as they interact with users.

The importance of chatbots in e-commerce

Today, chatbots play a central role in e-commerce, providing personalized assistance throughout the purchasing process. They simplify the management of frequently asked questions, enabling customer service teams to focus on more complex tasks. Chatbots are powered by artificial intelligence and provide recommendations based on the analysis of user behavior, revolutionizing the customer experience. They are no longer simply assistants but expert guides that support users from the research stage to the actual purchase.

Chatbots during peak sales periods: an optimized customer experience

Specific strategies for Christmas shopping

Chatbots are turning into real virtual assistants, deploying key strategies to optimize the shopping experience, such as :

  • Personalized adaptation: by analyzing a consumer’s purchase history and past interactions, chatbots can provide unique recommendations tailored to each user’s tastes.
  • Impact on sales: this targeted approach boosts sales by suggesting perfectly adapted products, creating a personalized purchasing experience.
  • Order tracking support: chatbots provide transparent order tracking and reply instantly to delivery status queries, offering an optimal customer experience.
  • 24/7 availability: users can access chatbots anytime, guaranteeing instant assistance and seamless communication, even during busy periods.

Chatbots during sales periods: maximizing efficiency

Chatbots are becoming key allies for online businesses during sales periods. They can help optimize efficiency by promoting special offers and deals. Let’s take a look at how these conversational agents enhance the shopping experience:

  • Traffic optimization: chatbots absorb any fluctuations in traffic, ensuring a more seamless user experience, even during busy periods.
  • Personalized promotions: if the chatbot is linked to your CRM, it can analyze your user data and make personalized suggestions, maximizing its impact during peak sales periods. 

Benefits, challenges, and future of e-commerce chatbots

Chatbots present significant benefits for online businesses, including:

  • An improved customer relationship: chatbots are available anytime and give quick answers, boosting consumer satisfaction and loyalty.
  • Increased conversion rates: effective chatbots guide consumers, remove barriers to purchase, and foster informed decisions.
  • Reduced customer support costs: automated handling of frequently asked questions enables employees to focus on more complex tasks while reducing costs.
  • A personalized customer experience: chatbots analyze past behavior to provide personalized recommendations
  • Data collection and customer engagement analysis: chatbot integration enables you to collect valuable data to improve your products, services, and strategies.

Best practices for developing e-commerce chatbots

If you want to leverage the benefits of chatbots in e-commerce, make sure to implement these best practices:

  • Define the chatbot’s objectives to guide the development process according to your company’s specific needs. 
  • Choose the right platform. We recommend basing your choice on integration, ease of development, and cost.
  • Once your chatbot has been developed, train it to improve its understanding and responses. Extensive testing and adjustments based on user feedback are necessary to produce the best possible version of the chatbot. Once it’s up and running, remember to carry out regular performance monitoring to ensure continuous improvement. 

Challenges and solutions for e-commerce chatbots

Dealing with sometimes complex customer requests can be a challenge and requires the application of advanced machine-learning techniques, as well as constant collaboration with the technical team. In some situations, the chatbot may not be able to provide a satisfactory response, in which case you can integrate a livechat. But what is a livechat? Livechat enables companies to establish a real-time connection between customers or employees and competent advisors. This interactive solution, via a chatbox, allows you to respond to complex requests in greater depth and boosts user satisfaction. Finally, remember to prioritize privacy and data security, with robust security measures and clear transparency on confidentiality.

]]> https://uat.dydu.ai/en/how-are-chatbots-transforming-the-e-commerce-sector-during-peak-periods/feed/ 0 Understanding and Adopting Generative AI in the Business World https://uat.dydu.ai/en/understanding-and-adopting-generative-ai-in-the-business-world/?utm_source=rss&utm_medium=rss&utm_campaign=understanding-and-adopting-generative-ai-in-the-business-world https://uat.dydu.ai/en/understanding-and-adopting-generative-ai-in-the-business-world/#respond Fri, 22 Dec 2023 15:42:09 +0000 https://uat.dydu.ai/?p=1234472

In a constantly evolving technological world, Generative Artificial Intelligence is emerging as a driver of change for companies, leading to greater efficiency (timesaving) and innovation (opening up new possibilities). According to a GrandViewResearch study, 83% of companies say AI is their priority for the coming years. Let’s take a look at how you can successfully implement AI in your organization.

An immersive exploration of generative AI

Definition of generative AI

Generative Artificial Intelligence is a specialized branch of AI, capable of autonomously creating data, content, or other artistic elements. This approach differs from traditional AI, which focuses on specific tasks such as classification, forecasting, or problem-solving. Generative AI aims to produce new data that bears similarities to that generated by human beings, whether text, images, music, or other forms of artistic expression.

The dynamic evolution of generative AI

In its quest for innovation, generative AI is constantly evolving. From pioneers such as ChatGPT, which marked a major turning point in 2022, to promising new players such as Midjourney or the French company Mistral, the technology is always evolving and changing. Industry giants such as Open AI’s ChatGPT have played a leading role in refining and scaling the capabilities of generative AI. These advances have paved the way for more diverse and sophisticated applications, particularly in content creation and the development of more intelligent chatbots. The generative AI ecosystem is constantly shifting, with Midjourney, for example, bringing new perspectives and innovations. These emerging players are helping to define new frontiers, enriching the generative AI landscape with innovative applications and ever more advanced features. Chatbots are the embodiment of this technology’s dynamic evolution. These virtual conversational agents are increasingly sophisticated, adapting to the nuances of natural language and providing a richer, more personalized conversational experience.

How is generative AI impacting businesses?

Discover the different ways companies use generative AI

Generative AI is not just an emerging technology… It is a strategic asset for businesses, offering a range of possibilities beyond mere technological curiosity. According to Leptitdigital, AI in retail is expected to reach $24.1 billion globally by 2028*. Companies can leverage this technology in various ways, from content creation to contract management, shaping new models of efficiency and innovation.

Here are a few examples of how businesses are deploying generative AI and the tools they’re using:

  • Creative content creation:  automated production of articles, scripts, and multimedia content, with models such as GPT-4, Boomy, and ChatGPT.
  • Graphic design: generation of logos, user interfaces, and visual elements for brands, using tools such as Looka. 
  • SEO optimization: optimized content creation for search engines, improving online visibility, such as Jasper.
  • Contract management: generation of compliant contracts and automated risk analysis with tools such as Oblige.

Opportunities and challenges for businesses

The practical benefits of generative AI for companies

Integrating generative AI, including chatbots, is more than just a trend. It leads to increased productivity and reduced costs and unleashes employees’ creative potential, making your business more competitive in a dynamic market.

  • Increased productivity:
    • Automation of repetitive tasks, enabling employees to focus on high-added-value projects
    • Rapid generation of content and customer responses, speeding up operational processes
  • Cost reduction:
    • Reduced labor costs through automation 
    • Optimized processes, reducing inefficiency and errors 
  • Unleased creative potential:
    • No more routine tasks, stimulating innovation and creativity

Legitimate concerns

Although there are undeniable benefits to integrating generative AI, it does raise some legitimate concerns, such as the threat of Deep Fakes, the possibility of unauthorized use of protected content generating legal risks, and the imperative need for ethical programming to avoid bias and misuse. Regulatory initiatives are already taking shape, particularly within the European Union, which adopted a draft regulation on artificial intelligence in June 2023. These measures aim to regulate the use of this technology*, requiring suppliers to be more transparent to ensure responsible generative AI use. These concerns highlight the growing importance of reconciling technological innovation with sound ethical and legal principles to ensure a sustainable future for generative AI in business. 

1 https://www.leptidigital.fr/intelligence-artificielle-ia/ia-statistiques-40982/

2 https://www.lemonde.fr/international/article/2023/06/14/le-parlement-europeen-adopte-a-une-large-majorite-le-projet-de-regulation-de-l-intelligence-artificielle_6177602_3210.html

]]> https://uat.dydu.ai/en/understanding-and-adopting-generative-ai-in-the-business-world/feed/ 0 Generative AI and Chatbots: A Revolutionary Synergy https://uat.dydu.ai/en/generative-ai-and-chatbots-a-revolutionary-synergy/?utm_source=rss&utm_medium=rss&utm_campaign=generative-ai-and-chatbots-a-revolutionary-synergy https://uat.dydu.ai/en/generative-ai-and-chatbots-a-revolutionary-synergy/#respond Fri, 20 Oct 2023 13:10:34 +0000 https://uat.dydu.ai/?p=1233981

Recent advances in generative AI and Large Language Models (LLM) have opened up exciting new perspectives for chatbots. LLMs, such as ChatGPT, are now at the heart of the evolution of chatbots and artificial intelligence. These advances have transformed chatbots into augmented advisors with enhanced intelligence, making them significantly more effective. This article will also look at the experience of our 10-year-old client Stellantis’ (ex-PSA), seeking to improve its IT chatbot’s answers with generative AI.

Understanding the relationship between generative AI and chatbots

The rise of artificial intelligence has paved the way for some remarkable innovations, including generative AI, which plays a central role in the development of chatbots. Chatbots, also known as “virtual assistants,” have evolved considerably with this technology.

Chatbots are computer programs designed to automate communication and interaction with users. They are often used to answer customer questions, provide information, or perform specific tasks without human intervention. However, with the integration of generative AI, they have become much more than just computer programs.

LLMs (Large Language Models), such as GPT-3.5, are remarkably capable of understanding and generating human language and are at the heart of this transformation. They turn chatbots into “augmented advisors,” endowing them with a deeper understanding of user needs and enabling them to learn continuously and provide more relevant answers. Chatbots powered by Generative AI can adapt their response to the context of the conversation, significantly boosting their effectiveness. Generative AI enriches chatbots with an adaptive capacity to help them meet ever-changing user needs, enabling them to respond with more natural, human, and relevant answers. Combining generative AI and chatbot technology is a big step forward in user experience and opens up new perspectives in automated communication.

In response to strong demand from our clients (including Stellantis) and prospects, Dydu has decided to integrate generative AI into its conversational AI solution while complying with GDPR and data protection requirements. This initiative clearly illustrates the growing importance of generative AI in enhancing chatbots and customer engagement.

Stellantis and the integration of generative AI in its Dydu chatbot

Faced with an ever-changing business environment, Stellantis, a global automotive leader, has made a strategic decision to invest in technological innovations, such as generative AI. The main aim is to optimize interactions with employees via its Dydu chatbot.

The current test phase reflects this drive for modernization. This phase targets employees who have recently joined Stellantis and are not yet familiar with the Microsoft suite. Ensuring new employees benefit from a smooth and efficient onboarding process is essential.

But what initially prompted Stellantis to implement a chatbot? Stellantis was looking to significantly reduce local support costs. This decision came after a recent internal reorganization, which significantly reduced the number of support teams. By implementing a Dydu chatbot, Stellantis can provide 24/7 support while optimizing resources.

After identifying new needs in 2023, Stellantis has decided to test generative AI. The key objectives are to ensure easy access to SharePoint documentation, facilitate knowledge base management, and, as an international group, provide multilingual support by integrating new languages.

All the above reflects Stellantis’ proactive vision of technological innovation. The group’s aim is clear: to be at the cutting edge, to provide employees with an optimal user experience, and to ensure operational efficiency every time they interact with the digital tools at their disposal.

Generative AI in action: use cases and associated benefits

Integrating generative AI via LLMs into our solution opens up new use cases that enhance the user experience :

  • Broadened scope of response: LLMs enable the Dydu chatbot to draw on existing document databases, meaning that it can answer more user questions.
  • Detection of misunderstood questions: generative AI identifies any questions beyond the bot’s comprehension. This capability paves the way for targeted improvements to ensure a more satisfying user experience.
  • Language and automated translation: another powerful feature enables the bot to detect the user’s language and translate content accordingly, facilitating multilingual exchanges.
  • Enhanced Natural Language Understanding (NLU) of the Dydu solution: integrating LLMs with the Dydu solution also makes it possible to generate text automatically by adding phrases and matching groups to the knowledge base. The aim is to continually enhance the user experience and improve the chatbot’s answers.

Integrating generative AI is a significant step forward in improving chatbot efficiencyand the overall user experience and can help companies optimize their customer interactions.

If this article has sparked your interest, check out our 12 October webinar for more information. Click here to find out more about Stellantis (ex-PSA)’s experience 👇

Watch webinar replay (french only)
]]> https://uat.dydu.ai/en/generative-ai-and-chatbots-a-revolutionary-synergy/feed/ 0 Voice: The Preferred Channel for Customer Support https://uat.dydu.ai/en/voice-the-preferred-channel-for-customer-support/?utm_source=rss&utm_medium=rss&utm_campaign=voice-the-preferred-channel-for-customer-support https://uat.dydu.ai/en/voice-the-preferred-channel-for-customer-support/#respond Fri, 07 Jul 2023 10:39:06 +0000 https://uat.dydu.ai/?p=1233256

A number of studies show that voice and phone remain popular customer support channels. Human touch is an essential part of the customer relationship, but some customer services struggle with the large number of requests, mainly due to the multiplication of channels, which lead to longer wait times and frustration.

Automated voice solutions can help relieve the load and improve customer satisfaction. But with all the different options available, which solution should you choose? IVR (Interactive Voice Response), callbots, and voicebots are today’s most popular solutions. For many decision-makers, they are all the same, but their use and satisfaction levels differ.

Here is a list of pros and cons to help you choose the right solution. 

Interactive Voice Response (IVR):

IVR is the most widespread voice technology on the market. In simple terms, you interact with IVR via a configurable voice menu. You can answer by pressing a button on your keypad (e.g., to pay your bill, press 3) or by speaking (e.g., for a question about your bill, say “bill.”)  IVR uses a funnel-like system to finetune the caller’s request (e.g., to check your bill press 1, to pay your bill press 2, to speak to an advisor press 3). 

The pros:

  • Reduced costs: less receptionists, and prequalified calls redirected to agents 
  • Availability: 24/7 for information-based questions (e.g., opening hours, phone line activation, etc.)
  • Better processing of requests: calls are less likely to be redirected from one department to another because they have been prequalified

The cons:

  • Although IVR is practical, users tend to think of it as a human substitute that holds less value
  • Users often encounter difficulties qualifying their requests and instead press random numbers to get through to a “human” agent as quickly as possible
  • If the scenario and prequalifying questions have not been properly thought out, customers can become irritated finally speaking to someone about their “urgent” problem
  • Using keywords or buttons on a keypad is not very natural and does not create a seamless journey

Voicebots:

There are several schools of thought on the differences between voicebots and callbots. Both use the same technology. They are voice assistants that process spoken requests by converting them into text and submitting them to a server: STT (Speech to Text) and TTS (Text to Speech) for the answer. This process only takes a few milliseconds and creates a smooth conversation for end-users. 

For the purpose of this article, let’s define voicebots as the ability to interact by voice within a chatbot (whether on a website, app, or messaging system) or with a connected object (such as Google and Alexa).

According to the 2022 Digital Barometer, 40% of French people use connected objects, and 27% have a connected speaker.

Voice is fast becoming a key part of customer relations, and voicebots, unlike IVR, enable natural interactions thanks to increasingly powerful NLP models (natural language processing).

The pros:

  • Speed: talking is 3 times faster than typing on a keyboard
  • Multitasking: users can talk to the speaker while browsing the website, checking their account, etc., or doing something else.
  • Inclusion: voice is a simple interface that anyone can use and is accessible to people with disabilities or visual impairments.

The cons:

Similarly to text chatbots, voicebots need knowledge. Once you have added all the most common queries to your knowledge base, you need to update it regularly to ensure that your bot is effective and provides a seamless experience. This will minimize answers such as, “I’m sorry, I didn’t understand your question” or “Sorry, I don’t know the answer to that question.”

Callbots:

Callbots, like voicebots, process requests using voice and provide a seamless experience. Callbots are available by phone, like IVR, and help streamline the number of incoming calls for customer services. But the similarities end there.

60% of customers prefer the phone when contacting customer services.*

Callbots combine the benefits of IVR and voicebots to offer the best possible phone experience. The bot immediately interacts with callers like a receptionist: “Hello, how can I help you?” Customers can use their own words, and the bot will understand the request and context (not just keywords). The bot can also adapt its speech pace to match the customer’s, or to diffuse a tricky situation, for example. Callbots understand dates of birth, full addresses, order references, and any other variables. You can also connect them to omnichannel platforms at customer support centers. 

The pros:

  • Customer satisfaction: no more wait times and quick processing
  • Reduced costs: facilitates processing and reduces callback rate (savings of up to 40% observed)*
  • Business generator: processing of calls during non-working hours and prequalification of requests
  • Improved employee experience: valued expertise and more efficient management of incoming calls

The cons:

Callbots are effective when their role is well-defined from the beginning (scheduling appointments, qualifying queries, taking out contracts, etc.). They can handle requests from start to finish or put the user in touch with an operator but, to fully benefit from its capabilities, the scenario has to be well-defined.

Feel free to read this article to find out more about callbots: Callbots: Key features for a seamless experience.

Whichever voice solution you choose, make sure you clearly define your goals, take the time to set up different scenarios and check the interconnections with your current tools. If you follow these three criteria, you’ll soon see results, and increase your customers and teams’ satisfaction.

*https://uat.dydu.ai/produits/callbot/

]]> https://uat.dydu.ai/en/voice-the-preferred-channel-for-customer-support/feed/ 0 Callbots: Key Features for a Seamless Experience https://uat.dydu.ai/en/callbots-key-features-for-a-seamless-experience/?utm_source=rss&utm_medium=rss&utm_campaign=callbots-key-features-for-a-seamless-experience https://uat.dydu.ai/en/callbots-key-features-for-a-seamless-experience/#respond Fri, 19 May 2023 12:35:11 +0000 https://uat.dydu.ai/?p=1233023
visuel callbot

Are you overwhelmed by the number of incoming calls? Do you want to improve your customer experience by eliminating wait times and providing immediate answers?
Callbots, conversational agents available by phone 24/7, allow you to do just that while reducing your costs by 30%!


But to provide a seamless customer experience and support your advisors, you must ensure that your callbot has certain essential features.

Natural Language Processing (NLP)

The first key feature is a powerful Natural Language Processing (NLP) algorithm. The more powerful the algorithm, the smoother the conversation.

The callbot must be able to understand the caller’s requests and answer instantly, to ensure a quick and seamless exchange.

It should also adapt to the user’s speaking rate and vocabulary. For example, the callbot should know that ‘that’s fine’ means the same thing as ‘OK.’ Our callbot solution can also ‘mimic’ the caller’s speaking rate and handle pauses in the conversation.

Finally, a callbot should be able to manage time slots, even without specific data. For example, if you call to make an appointment ‘next week in the morning,’ the callbot should be able to suggest several time slots that fit your request, even if you haven’t specified a date or time.

At Dydu, we’ve been developing and optimizing our algorithms for more than 13 years. As such, we can provide:

  • 90% transcription accuracy
  • understanding rate > 90% 
  • response time < 1 sec

The more characteristics of human language the callbot adopts, the more natural and seamless the experience will be, thus creating an enriched customer experience.

Interconnection with your tools for personalized responses and prequalified requests

Callbots can be used for many purposes, especially when interconnected with your tools, such as your CRM, ticketing or appointment scheduling tool, SMS/email platform, etc.

Making a bank appointment

Once the customer has requested an appointment with their banker, the callbot can ask questions to identify (and possibly authenticate) the caller. Once identified, the callbot can determine who the customer’s banker is and suggest available times. Once the appointment has been made, it can also send a confirmation SMS and email to the customer.

Making a claim

As when scheduling a bank appointment, the callbot can start by identifying the customer with some questions. It can then register the claim by creating an incident ticket and reference number using your ticketing tool. The customer can also check on the status of their claim via the callbot.

Callbots have lots of use cases depending on your company’s sector of activity.

Connecting your callbot to your work tools presents several benefits:

  • authentication and/or identification
  • personalization of responses
  • end-to-end processing of requests by callbot
  • prequalification of requests for more complex cases

You can interconnect our solution with various tools and platforms.

Live agent escalation

You can further enrich the callbot customer experience by escalating to a live agent, when necessary, to complete the customer’s request.
A callbot cannot handle all use cases; some need to be processed by a human rather than an automated system (e.g.: signing up to a policy or terminating a contract).
In this case, the bot will prequalify the customer’s request before transferring them to a live agent. The advisor can access their conversation with the bot to process the request more quickly.

Pairing your callbot with a customer service escalation system has many benefits. It enables to:

  • process more incoming calls
  • ensure your advisors only handle high-added-value requests
  • significantly reduce request processing times and enrich your customer experience
  • reduce call processing costs

A callbot can be a powerful ally to handle your incoming calls while improving your customer experience with end-to-end support.

Are you thinking about implementing a callbot in your company? Feel free to reach out for a demo of our solution. We will help you identify the different use cases for your business, and how to improve your customer and employee experience.

]]> https://uat.dydu.ai/en/callbots-key-features-for-a-seamless-experience/feed/ 0 ChatGPT VS Dydu: Differences and Complementarities  https://uat.dydu.ai/en/chatgpt-vs-dydu-differences-and-complementarities/?utm_source=rss&utm_medium=rss&utm_campaign=chatgpt-vs-dydu-differences-and-complementarities https://uat.dydu.ai/en/chatgpt-vs-dydu-differences-and-complementarities/#respond Tue, 18 Apr 2023 08:46:00 +0000 https://uat.dydu.ai/?p=1233267
visuel chatGPT

The launch of chatGPT has sparked a lot of talk about AI in recent months, whether in the news or everyday conversations. 

Chatbots and self-care tools have been at the heart of the customer relationship for several years now:

  • 96% of consumers have heard about chatbots and know what they are
  • 62% of consumers prefer to find answers themselves
  • 40% of savings on customer support costs with bots

And this trend could well accelerate with the adoption of GPT!
But before we go any further, here’s a quick disclaimer…
GPT is constantly evolving. This article may be out of date tomorrow. But the work we’re doing at Dydu to integrate GPT into our solution is still ongoing. So, stay tuned!

ChatGPT: Why all the hype?

There’s nothing new about chatbots, so why is there so much buzz around Open AI’s ChatGPT?

If you’ve ever had a conversation with chatGPT, you were probably impressed by:

  • the feeling of being understood by a chatbot and the very natural nature of the conversation
  • its ability to write poems, press articles, or even legal documents with precision and/or creativity
  • the bot’s quick answers

The bot can express itself perfectly, whether in English or French. 

ChatGPT also has the ability to remember past conversations and provide contextualized answers.

In other words, it’s (almost) like talking to your best friend! ChatGPT has positively changed people’s perception of chatbots. Which is great news!

Exemple de conversation avec chatGPT

Example of a conversation with chatGPT:

Can you explain the benefits of a chatbot as if I was 10-years old?

Of course! A chatbot is a computer program designed to talk to people in a friendly manner. You can ask questions to which it will try to reply. Here are a few advantages to using a chatbot:

  1. Speed: chatbots can answer your questions instantly.
  2. Availability: you can talk to a chatbot any time of day or night, even when nobody else is available.
  3. Non-judgmental: you can ask a chatbot anything you want without worrying that it will judge or poke fun at you.
  4. Alert: unlike humans, chatbots never get tired of answering questions.
  5. Fun: some chatbots are designed to be funny or to tell jokes.

In other words, chatbots can provide quick and reliable answers, while being available all the time and entertaining.

What are the differences between chatGPT and Dydu’s chatbot solutions?

Before we talk about the differences between chatGPT and Dydu, let’s take a look at the similarities. Both specialize in natural language processing (NLP): 

ChatGPT uses its own language model, GPT.

  • Dydu also owns its own NLP component, which is at the heart of its chatbot solutions

But the two solutions don’t offer the same user experience.

Learning mode

ChatGPT and Dydu take a different approach to processing language and learning.

ChatGPT uses unsupervised learning, i.e., it learns from its interactions with users. It is non-deterministic (it can give different answers to the same question) and non-explainable.

It is risky for brands to use because you can’t control what it says. ChatGPT could make false claims or be racist, sexist, etc., and seriously damage a brand’s image or create a bad buzz.

Example of a false answer provided by chatGPT :

How many calories are there in a horse egg?

Humans don’t often consume horse eggs, so there is very little nutritional data available. However, generally speaking, horse eggs have a similar nutritional value to chicken eggs in terms of protein, fat, and calories.

There are, on average, approximately 70-80 calories, 6 grams of protein, and 5 grams of fat in a chicken egg. Horse eggs may have a similar nutritional value, but it depends on the size of the egg.

It is worth noting that eating horse eggs is not common practice in most cultures and could even be considered unsafe. If you’re unsure whether it’s safe or not to eat horse eggs, make sure to speak to a health professional or nutritionist before including them in your diet.

Dydu has opted for supervised learning. You can use our back-office to choose whether or not to add new knowledge articles and to manage the bot’s answers. This gives companies total control over what they say to their customers.

Bot management via a back office

Unlike Dydu, ChatGPT does not offer the option of managing the chatbot via a back-office.

The Dydu back office is one of our solution’s much-appreciated primary assets. It is intuitive and enables to:

  • view dialog history
  • analyze the relevance of the bot’s answers and customer satisfaction
  • enrich the knowledge base and therefore manage the content
  • check use and dialog analytics

None of these features exist in Open AI’s product. Our tool and solutions are much better suited to company needs for customers or employees.

Interconnections with tools and platforms

Unlike chatGPT, our software can interconnect with the main tools on the market, integrate easily with third-party channels or apps, and perform mass processing. It can retrieve data from your CRM, HRIS, ticketing tools, and other databases to personalize its answers.

We are developing more and more interconnections, in line with our clients’ needs, to:

  • easily deploy our chatbots where users are (Microsoft Teams, Facebook Messenger, Instagram, etc.)
  • improve the user experience by providing personalized answers and performing actions directly within the bot through company tool integration

Data security

Unlike chatGPT, our Dydu solution is GDPR compliant. Data security and GDPR compliance is extremely important for a solution like Dydu with CAC40 clients in the banking, telecoms, public sector, etc.
We host our data in France in an encrypted manner. We know where our data goes and how it is used. The way ChatGPT uses data is currently the subject of debate in Europe, prompting the CNIL to open an investigation.

How Dydu is integrating GPT into its solutions

We’re currently working on several POCs with some of our clients to leverage GPT and other language models, such as Meta’s LLaMA, within the Dydu engine.

The combination of both solutions could further improve the customer experience.

Information search

Integrating language models such as GPT with the Dydu solution is ideal for answering less frequent questions – i.e., those not covered by the knowledge base.

How? By using GPT with a pre-existing document base. If the Dydu bot can’t find an answer in its knowledge base, it can search the document base and answer more questions.

Accelerated Dydu bot training

Another interesting area to explore is automatically enriching the matching phrases in the knowledge base with GPT’s generative capabilities.

If you provide one matching phrase for each knowledge article, GPT can offer alternatives and speed up our engine’s learning process. All you need to do is check that the phrases suggested by GPT are relevant.

On 12 April, the Dydu co-founders discussed all these points in more detail in a ChatGPT webinar.

[ Only in french ]

Watch chatGPT webinar replay
]]> https://uat.dydu.ai/en/chatgpt-vs-dydu-differences-and-complementarities/feed/ 0 How Does Dydu Make Multichannel Chatbot Integration Easier? https://uat.dydu.ai/en/how-does-dydu-make-multichannel-chatbot-integration-easier/?utm_source=rss&utm_medium=rss&utm_campaign=how-does-dydu-make-multichannel-chatbot-integration-easier Tue, 28 Feb 2023 10:17:26 +0000 https://uat.dydu.ai/?p=1232386

If you want your chatbot to be as efficient as possible, you need to integrate it with your users’ most popular platforms. After WordPress, SharePoint, ServiceNow and Teams, you can now easily deploy your chatbots on new channels such as Facebook, Instagram and WhatsApp. The more visible and present a conversational robot is on your communication channels, the more likely your users are to use it as their go-to point of contact.

Simply create one chatbot and integrate it with several channels at the same time. This is known as a multichannel chatbot. 

How to Integrate a Chatbot with a Website

Integrating a chatbot with a website or intranet is quick and easy. Add your Dydu bot’s JavaScript (a single line of HTML code) to your webpages. The script will then load all the files needed for the chatbot to work properly from the website’s host server.

If your website was created on CMS WordPress and SharePoint, you don’t need to modify your website’s code. 43% of websites are created on WordPress, so Dydu has developed a WordPress plugin to install, to deploy your bot in a few clicks. As for SharePoint Online, a CMS used by more than 200,000 organisations worldwide for their intranet and team site, Dydu can provide an extension to be installed in your SharePoint app catalogue.

You can publish your virtual assistant on your entire website, or on one or more specific pages, depending on your chatbot’s scope. Dydu chatbots are responsive: they adapt to the screen size, whether the end user is on a website (mobile, tablet or desktop) or an app.

How to Integrate a Chatbot With Mobile and Desktop Applications

Integration of a Chatbot With a Mobile App

Mobile apps are an excellent way to reach users on their phones. You can integrate a bot with an existing mobile app, just like a website. We enable our clients, such as Total Direct Énergie, Société Générale and SNCF Connect, to do this themselves. Users can open the chatbot by clicking on the avatar at the bottom right-hand of the screen, and then close it once the conversation is over to return to the app content.

Sobot, le chatbot de la Société Générale sur application mobile
Sobot, Société Générale’s mobile app chatbot

Integration of a Chatbot with Software

Our clients can easily add their bot to ServiceNow software. This task automation solution is the market leader in ITSM (IT service management). It is an ideal channel to deploy an IT chatbot on. Simply go to your ServiceNow portal and add the chatbot as a widget. Widgets are reusable components provided by ServiceNow which enable you to add features to your portal.

A lot of our clients and prospects use ServiceNow, but you can also deploy Dydu bots on other desktop apps, as long as they allow you to add JavaScript.

How to Integrate a Chatbot With Instant Messaging

To add a bot on an instant messaging system and still benefit from Dydu’s front-end features, you often need to deploy a specific connector. You therefore have to use the features offered by the instant messengers. You can’t customize the design of a bot on Teams, Messenger, Slack or WhatsApp. You can however configure certain elements such as the bot’s name, avatar or text formatting.

A Chatbot on Microsoft Teams

Microsoft Teams, a collaborative communication app, has 270 million active monthly users. This is the most used solution by our clients! Thanks to our Teams connector, most of our bot solution features are available on Teams: answer formatting, redirecting to other knowledge articles, use of variables, decision trees, etc. This includes 100% of our answer formatting features (bold, colour, bullet points, links, tables, images, side panel, etc.)!

The chatbot is displayed differently than on a website. On Teams, the user searches for the bot’s name and starts a conversation as they would with a colleague. As for the bot, it can manage individual and group conversations. In a group chat, it only responds when a user mention it.

To deploy your bot on Microsoft Teams, go to your Azure portal and Teams app catalogue, and configure your so-called “bot application”. 

A Chatbot on Facebook Messenger

Today, chatbots are widely used on Facebook Messenger. Modules to create bots have been around for a while, but they are rather simplistic and offer limited features. We’ve developed a connector, like for Teams, to ensure that our bots work on Messenger with a maximum of Dydu features. However, we are still dependent on Messenger in terms of content display.

The bot is linked to a Facebook page. Users can access it by clicking on the “message” button on the brand’s page, or directly via Messenger if they’ve had a past conversation.

To create a bot on Facebook Messenger, simply create a bot application connected to your chatbot on the Dydu Bot Management System (BMS) and make this bot app available on your Messenger page.

A Chatbot on Instagram

A lot of people use Instagram to follow brands. According to Instagram’s internal data and an Ipsos survey, 90% of Instagram users follow a company, and 2 in 3 people use the social network to interact with brands. A lot of businesses deploy bots on this platform to automatically answer their followers’ questions and recommend products.

A Chatbot on WhatsApp

According to Hootsuite*, WhatsApp is the third most used social network platform after Facebook and YouTube. Although the app is mainly used to communication with friends and family, WhatsApp can also be used by businesses, to highlight products in a catalogue and improve customer support with live chat agents and/or a bot.

How to Manage a Multichannel Chatbot

Quick and Easy Deployment from our Solution Back-Office

To help our clients create their own bots, Dydu has also developed a module for them to deploy bots on different platforms. This module, called “channels menu” is available in our BMS and allows to integrate a bot in a few clicks with a website, Microsoft Teams and, shortly, other channels managed by Dydu.

Different Views per Channel

You can integrate your chatbot with different channels, but all your knowledge articles and dialogues are managed in the Dydu BMS. This Bot Management System provides an overview of your bot’s performance and dialogues, as well as a different view for each channel. This means that you can see which channel each dialogue has taken place on. Which is very useful if your flow has to take a different direction depending on the channel used (specific response according to the formatting / features available on each channel). 

Statistics will also soon be available for each channel (number of dialogues, interactions, qualification rate, etc.).

So, to make your bot as useful and efficient as possible, integrate it wherever your users are! Do you want to know our best practices to encourage your employees or customers to use your chatbot? Check out our article on how to encourage bot adoption.* Hootsuite “Digital 2022” study

]]> Digital banking: supporting customers with conversational agents https://uat.dydu.ai/en/the-digital-banking-relationship-supporting-customers-with-conversational-agents/?utm_source=rss&utm_medium=rss&utm_campaign=the-digital-banking-relationship-supporting-customers-with-conversational-agents https://uat.dydu.ai/en/the-digital-banking-relationship-supporting-customers-with-conversational-agents/#respond Tue, 13 Dec 2022 15:38:06 +0000 https://uat.dydu.ai/?p=1231039
visuel article relation bancaire

The health crisis has accelerated the digitalization of practices throughout the world, and the banking sector is no exception. For day-to-day interactions with their bank, customers are gradually moving away from traditional communication channels (phone, branch appointments, etc.), to digital ones. This change in customer habits has had a major impact on the relationship between banks and their customers.

Accessibility and responsiveness are key expectations

Customers expect impeccable customer service, whether on a bank’s website, or via its mobile app. They want quick and relevant answers, with minimum effort, regardless of the channel. According to a recent Deloitte study, immediate access to information and easy-to-carry-out transactions are key criteria for banking customers.

Yet banks often struggle to meet these demands in a satisfactory manner. The same study suggests that, in France, one in two people don’t get the answer they need when they first contact their bank for a claim. And nearly 7% of customers surveyed in a Onepoint and Harris Interactive study reported that their bank does not respond at all*.

So, how can banks be more available for customers without putting too much pressure on their customer service or banking advisors?

Virtual bank advisors available 24/7

Conversational AI banking solutions offer continuous support, whether to meet the needs of individual customers or business clients. Conversational agents dedicated to banking are available 24/7 and manage recurring requests automatically. They provide users with instant information and support when needed, thus creating a smoother customer journey.

With these accessible and easy-to-use tools, users maintain their autonomy on all digital channels: they can get information about their account, carry out day-to-day bank transactions and request guidance for certain procedures.

But that’s not all! With a solid understanding of natural language and transactional capabilities, these conversational agents can also help users securely carry out targeted actions such as searching for a transaction, checking the status of a transfer, downloading documents or subscribing to a service – straight from the dialog box.

An augmented customer experience

Banking bots are interconnected with banks’ internal software to provide users with personalized answers and a tailored customer experience. For example, clients can use these virtual agents to check their card limit but also increase that limit to make an exceptional purchase.

Requests requiring human intervention can be escalated to a livechat operator or call center. This handoff can be based on various criteria throughout the conversation, such as a sensitive topic, a misunderstanding or user inactivity. The livechat operator receives the history of the conversation which helps them contextualize the request and provide a better level of help. They can also call on the bot’s knowledge base to support their answers.

Banking bots save customer support teams a considerable amount of time: they pre-filter recurring requests where human intervention adds little value and allow operators to focus on high-impact ones.

Top 5 banking bot use cases

  1. Account information: check balance, download bank statements, change card limit, check overdraft
  2. Common banking actions: get bank details, make a transfer, order a checkbook, add a payee
  3. Assistance with procedures: moving house, stop or dispute a payment
  4. Customer account management: technical difficulties, forgotten or lost password and username, update contact details
  5. Branch relationship: find a branch nearby, contact your banking advisor

*Deloitte study, Banking and Customer Relationship – 11th edition
**One point and Harris Interactive study – The Customer Relationship in the New Post-Covid Paradigm

Request a demo
]]> https://uat.dydu.ai/en/the-digital-banking-relationship-supporting-customers-with-conversational-agents/feed/ 0 Dydu Is Improving its Metabot, a Feature to Call on and Manage Several Chatbots ! https://uat.dydu.ai/en/dydu-is-improving-its-metabot-a-feature-to-call-on-and-manage-several-chatbots/?utm_source=rss&utm_medium=rss&utm_campaign=dydu-is-improving-its-metabot-a-feature-to-call-on-and-manage-several-chatbots Fri, 04 Nov 2022 13:32:26 +0000 https://uat.dydu.ai/?p=1194876

It’s not unusual for companies to have several chatbots. For example, a HR, IT and legal bot for internal support, or a pre- and after-sales one for customer service. To improve the user experience, dydu decided to deploy a metabot feature, enabling users to interact with several bots at the same time via a single interface.

What Is a Metabot?

Metabot is a feature that enables users to call on several bots at the same time through a single chatbox. It then determines the best answer for the end-user. It takes on the form of a parent bot, to which several child bots are attached.

A metabot provides a single entry point for users, whatever their query. The knowledge managers, on the other hand, are only responsible for their own bot(s). The feature is deployed by activating the metabot option in the dydu solution.

Why Implement a Metabot?

Improve Your User Experience

A metabot prevents customers, employees and users wasting their time looking for the right chatbot. They only need to interact with one bot, whatever their question may be. The risk of having several bots without a metabot is that the user interacts with the wrong one and gets frustrated when it doesn’t understand their query.

Take for example an organisation with two internal chatbots, one for HR and one for IT. An employee notices that the number of remaining leave days in the HRIS is wrong. They’re not sure whether to talk to HR or helpdesk. With a metabot, it’s easy: they can simply ask the chatbot! Everyone saves time and gains in productivity.

Improve Access Rights Management

Each child bot has its own environment and knowledge manager (usually an operational employee). The bots’ knowledge bases are therefore managed separately, without impacting each other. Managers can only perform actions on the bot(s) they have access rights to.

Each bot also has its own dialogues. Knowledge managers can continually improve their bots (reading and correcting conversations and misunderstood sentences, audits and suggestions) without worrying about the others.

A metabot manager oversees the management of all the child bots. 

A Global and Specific Chatbot View

Metabot managers need to be able to monitor and analyse performance without accessing each child bot. They have an overall view of the parent and child bots in terms of analytics and learning modules. This enables them to help knowledge managers achieve greater consistency and to arbitrate when two subjects cross over.

Dydu deployed the first version of its metabot in 2015. We have made several improvements to our new version in order to enrich the end-user experience for multi-bot companies and make bot management even easier.

]]> Dydu Launches a Turnkey E-commerce Chatbot for Online Shops https://uat.dydu.ai/en/dydu-launches-a-turnkey-e-commerce-chatbot-for-online-shops/?utm_source=rss&utm_medium=rss&utm_campaign=dydu-launches-a-turnkey-e-commerce-chatbot-for-online-shops Fri, 14 Oct 2022 12:40:16 +0000 https://uat.dydu.ai/?p=1144486

The e-commerce market is booming. Brands must provide a clear and simple shopping experience and ensure good customer relationship management to maintain their competitive edge. To meet consumer demands, more and more brands are turning to automation solutions, such as chatbots. There is nothing new about conversational agents, but over the last 10 years, they’ve proved their worth by automating simple and recurring requests.

According to KPMG’s latest Customer Experience Excellence study, “customers in 2021 are looking for technological performance, in order to obtain instant responses, a personalised customer relationship, (…) secure exchanges and data confidentiality.” 

Dydu wanted to make it quicker and easier for brands to create and deploy an e-commerce chatbot, with a pre-configured and trained solution. 

Support Customers Throughout Their Journey 

Our e-commerce chatbot improves the shopping experience by providing customers with immediate assistance. It helps remove any barriers to purchase, boosts customer satisfaction and strengthens brand trust, while relieving advisers of repetitive and time-consuming questions.

An e-commerce bot reassures users before they make a purchase. It provides information about the brand and website, items for sale, or the buying process itself. During this phase, the bot can answer practical questions about logins, payment terms or delivery options. After the purchase, it can help track buyers’ orders and provide an after-sales-service. It can also be proactive and highlight special offers, private sales or loyalty programmes.

Our e-commerce chatbot can therefore encourage conversations and help convert sales.

A Turnkey Solution for a Quick and Easy Implementation 

More than 200 pre-configured knowledge articles

Our e-commerce chatbot can provide customers and future buyers with all kinds of information. Its knowledge base includes more than 200 articles (question and answer pairs), on 16 recurring e-commerce themes: purchase and payment, orders, delivery, refunds, returns and exchanges, customer account, special offers, loyalty programme, customer service, etc.

The bot has already been trained to understand lots of common queries and comes with pre-written answers that can be customised.

You can also improve the end-user experience by adding rich content to the answers, such as images, links, Google maps or videos. 

The bot uses dydu’s natural language processing solution. It can understand full sentences and different registers. Users can be spontaneous and phrase requests in their own words. The chatbot also recognises “e-commerce jargon”, as well as synonyms or words with similar meanings. For example, “return label” will be understood to mean the same thing as “return slip” or “prepaid sticker”. 

Tracking Orders With the Bot

Tracking orders is an important part of the shopping experience. Which is why dydu has developed an API to help buyers track their packages, using the chatbot. All they have to do is enter their tracking number and the carrier’s name to check their order’s status and location in real time. The API works with more than 900 carriers worldwide, including La Poste / Colissimo, GLS, DPD, UPS and FedEx.

This service is exclusive to Dydu chatbots. It reassures users and helps build loyalty.

Taking Things Further

Our e-commerce chatbot can be connected to other software within the company, such as the IS or CRM. The chatbot can then personalise its answers, based on the user’s identity (first name, location, order history). It can also perform certain tasks such as issuing an invoice, changing the user’s password or postal address, or opening a claim.

The e-commerce chatbot can also be coupled with our livechat solution to manage more complex cases that require a human hand off. According to our latest chatbot observatory, 73% of professionals who have set up a chatbot have interconnected it with livechat technology. This hybrid chatbot / livechat model adds a human touch to the customer relationship and handles repetitive questions in a more efficient manner. Simply define when a human hand off is needed and transfer the data and dialogue history to the livechat agent. This could be executed after the bot misunderstands a request several times (number to be defined), or in “critical” situations which can be detected with key words, such as “claim”.

Want to find out more about Dydu’s e-commerce chatbot ? Contact us for a demo.

]]> Client Testimonial:  The French Development Agency Looks Back at the Implementation of their Chatbots https://uat.dydu.ai/en/client-testimonial-the-french-development-agency-looks-back-at-the-implementation-of-their-chatbots/?utm_source=rss&utm_medium=rss&utm_campaign=client-testimonial-the-french-development-agency-looks-back-at-the-implementation-of-their-chatbots Tue, 04 Oct 2022 10:10:53 +0000 https://uat.dydu.ai/?p=1122940

The French Development Agency – Agence Française de Développement (AFD) – called on Dydu to deploy two HR conversational robots within their company: an internal chatbot for AFD employees, and a recruitment chatbot for applicants. Meng-Han Chiang, Head of HR Digital Transformation at AFD, looks back at the implementation of these two chatbot projects.

Can you describe AFD and your role?

The Agence Française de Développement (AFD) funds, supports and accelerates the transition towards a fairer and more sustainable world. Climate, biodiversity, peace, education, urbanism, health, governance… Our teams carry out over 4,000 projects in the French Overseas Territories and 115 countries. We contribute to the commitment of France and French people to the Sustainable Development Goals (SDG).

AFD has a network of 85 branches and 17 regional directorates in the world. We have more than 2,600 employees and 80 different nationalities in 5 continents.

I am Meng-Han Chiang, Head of HR Digital Transformation at AFD. We conduct digital HR projects.

What problems were you trying to solve by implementing a HR chatbot and a recruitment chatbot?

We noticed that employees were asking our HR managers a lot of recurring questions, to which they were providing the same answers. We wanted to provide a tool that could answer simple and recurring questions, to help my colleagues focusing on specific cases. Our 85 branches operate in different time zones, so it was important to opt for a tool that was available 24/7.

How did the project come about?

We began to look into chatbots in 2018, the year I joined AFD. At the time, AFD’s innovation hub was launching an entrepreneurial programme which enabled us to push new projects and test our ideas on employees. We put together a small HR team to take part in the programme’s first edition. We tested different tools on user groups, such as interactive FAQs and chatbots. Following these tests, we realised that chatbots were better suited to our needs. Our executive committee then approved the project for a pilot phase. After conducting several POCs with different editors, we decided to launch our chatbot projects with Dydu.

What were the different chatbot implementation stages?

We started with the recruitment chatbot. Recruitment was our primary focus because there was a limited number of recurring questions, and we could cover a fairly large population. We receive 3,000 applications per month, so we wanted to improve our employer brand through the chatbot, and to use this digital tool as a lever for social interaction. For example, our chatbot shares job videos on applicants to promote careers at AFD.

The HR department’s scope is very wide, so we defined themes for the internal chatbot, according to the different HR areas of expertise. We first worked on appraisal interviews, for which our HR policy is the same for all employees. We then addressed internal mobility and training.

We launched both chatbots in October 2020. The recruitment chatbot on AFD’s careers site and the HR chatbot on our 3 HRIS tools: career management, training and e-training. We communicated on the launch of our internal bot with infographics and chatbot presentation videos via our HR newsletter.

Why did you choose Dydu?

We met several chatbot builders and chose Dydu because the solution offered the most advanced features. In particular analytics, anonymous dialogues and the possibility of creating decision trees allow us to provide an answer according to the employee’s situation.

With Dydu, we can manage two separate chatbots in one environment. In the Dydu back office, I can manage our recruitment chatbot’s knowledge articles, dialogues and analytics internally. The other editors did not offer this type of all-in-one functionality.

What do you want to achieve?

With this chatbot project, we want to make information easily accessible to everyone, lighten our HR management teams’ workload and strengthen AFD’s employer brand.

We set ourselves goals and indicators to reach within 1 year:

  • Identify the most popular 20% of knowledge articles in each HR field to answer 80% of recurring questions.
  • Provide information at the right time.
  • Free up time for our HR teams, so that they can focus on more added-value tasks.
  • Achieve an 80% user satisfaction rate with the answers provided by the bot.

Overall, we have achieved these goals. We are continuing to feed the bot and hope that it will free up time for HR employees in the long run, when it has become an expert on all HR topics and deployed on all our HRIS platforms.

What benefits have you seen since implementing the solution?

Since implementing our recruitment chatbot, we can answer most applicants’ questions, whereas we struggled to do so without the tool. We can only gain from this new service. It also helps develop our employer brand. Our recruitment chatbot has 1,200 dialogues per month with a 96% qualification rate of interactions. A lot of our dialogues, therefore, have an excellent accuracy rate, and we are delighted.

Our internal chatbot is also a new internal support service. There are on average 200 dialogues per month, with an 86% accuracy rate. The questions are logically tied to major HR events, such as appraisals, internal mobility, etc. The rest of the year, the questions are often about training.

How do you manage the project?

Today, in run mode, I’m the only chatbot administrator. I spend an hour per week reading the dialogues to identify any trends (it’s an excellent tool to know what is preoccupying employees), improve the bots’ understanding and identify any new knowledge articles to create. Once every two months, we have a meeting with experts from our different HR teams to review the most frequently asked questions. We then decide together if we need to create a new knowledge article and which ones to highlight in the Top 3.

Which KPIs do you monitor to manage the bot’s activity?

We monitor several indicators in the Dydu back office:

  • Number of visitors per month
  • Percentage of visitors with a dialogue
  • Number of dialogues per month
  • Qualification rate per interaction
  • Number of knowledge articles that cover 80% of user questions
  • Customer satisfaction for the knowledge articles highlighted in the Top 3. If satisfaction is not sufficient (below 80%), we try to improve the knowledge article’s phrasing and add additional information to our answers.
  • Contact channel: this allows us to know which tools applicants use to communicate with the recruitment bot (Windows, Android, iPhone, or Mac) and to improve our user experience.
  • Platforms of use: this allows us to know which HRIS platforms employees use to interact with the internal HR bot.

I check these indicators regularly and share them with our HR experts during our steering committee.

What prospects or developments do you have planned for the project?

We plan on deploying the internal chatbot on other HR platforms and to integrate all HR knowledge. We also want to translate the knowledge base into English and possibly Spanish. With regards to translation, we will identify the most used knowledge articles and focus on them first.

Our IT department has also provided support from the start of this adventure and is very interested in the potential of a conversational robot.

]]> Dydu Extends its Bots Analytical Potential https://uat.dydu.ai/en/dydu-extends-its-bots-analytical-potential/?utm_source=rss&utm_medium=rss&utm_campaign=dydu-extends-its-bots-analytical-potential Fri, 29 Jul 2022 15:21:12 +0000 https://uat.dydu.ai/?p=989516

Standard Reports: a Key Part of the Solution

When using the Dydu solution, you can instantly view your bot’s key performance indicators at any time and over any period. Our BMS (Bot Management System) includes an entire section for this exact purpose, called Analytics.

It includes information about the number of visitors and the number of dialogues. More specifically, you can see the number of interactions per dialogue and also the quality of these interactions and dialogues. This is determined based on the bot’s understanding of the questions and its ability to provide a satisfactory answer. You can also immediately and visually identify your users’ areas of concern by looking at the most frequent topics discussed with the bot.

Dydu Bot Management System Analytics

In order to continually meet expectations, we conducted an exploratory study in Autumn 2020 with 10% of our clients, to fully understand their needs in terms of analysis, as well any gaps between their current use of reports and a potentially more advanced use of their data. We turned the expression of these needs into specifications, enabling our developers to provide a solution that is now available as standard in our software.

Before going into further detail, let’s take a look at who uses bot analytics and why.

Use of Analytics: Who Does What?

Content managers focus on the knowledge base (questions, answers, matching groups, modes of expression within the interactions and dialogues, etc.). They check the analytics and conversations to correct and enrich the bot, so as to answer even more questions correctly. Their objective is to ensure that the bot provides users with as much satisfaction as possible.

Project managers manage the bot’s different stakeholders within a governance framework. They are responsible for justifying the bot’s budget and measuring its ROI. They also work with the business or department to optimise internal processes and make optimisation recommendations. Finally, they often use analytics to manage, or even pay, one or more service providers. The project manager therefore uses analytics to achieve objectives – validation, areas for improvement -, and also communication (evangelism and building loyalty). Bots are also selfcare tools, so project managers often use analytics in a more global context: either by comparing the bot’s performance with other selfcare tools, or by consolidating them into a more complete user experience. 

According to our analysis, the 3 main analytics use cases are, on an equal footing: monitoring bot use, identifying and prioritising actions to carry out and internal communication.

For More In-Depth Analysis

The majority of our clients use existing dashboards, but some work with data outside the BMS, to carry out more in-depth analysis. For example, they use the dialogue content to identify their users’ characteristics (leads, customers, etc.) and expectations. Using the details of the interactions, they can optimise their journey and improve their conversion and satisfaction rates.
In addition to standard reports, some of our clients were therefore having to reprocess data as a result of scattered or missing KPIs.

To facilitate this work, Dydu completely reviewed the available data exports. They are now organised into 2 clearly identified areas: dialogues and interactions. The available fields in each of these areas have been significantly enriched. As such, there are now 22 fields for the dialogue export (vs. 6 previously). For example, we added critical elements to understand the bot’s use: contextual elements (channel, browser, etc.), as well as dialogue-specific elements, such as start and end time, length, number of interactions – overall, business, social -, requested and provided satisfaction. We have also add the type of dialogue, which allows to distinguish between production dialogues and test dialogues.

Moreover, we’ve added the position of the interaction to the interactions export. This data is particularly useful for analysing a churn funnel, for example, and to provide better answers earlier on. We have also inserted a Bot UUID field, which is essential for Metabots, to analyse each child bot’s interactions. We have also added a hierarchy (knowledge path in the base), which is particularly useful in rich and complex knowledge bases, to identify the most frequent and satisfactory (or not) themes or sub-themes.
These can be found in the BMS, in the Dialogue menu.
The Dialogue and Interaction exports are available now. We’re also working on a Knowledge export which will be available soon.

For More In-Depth Communication

The main benefit of these Excel exports is that they can be processed in any Business Intelligence tool and can therefore be used for all types of analysis and combined with data from different apps or sources.

Since these tools often have advanced data visualisation capabilities, it is easier to create powerful visuals in the company’s graphic identity. Project managers can thus communicate on bot-related performance and actions more easily, and with greater impact. 

Remember that exported data depends entirely on your bot’s data retention settings. If you have chosen to anonymise data after a certain period, not to retrieve the user’s IP address, configure the GDPR in a certain way… then you will only retrieve the data from the dialogues that you’ve chosen to keep. It is worth taking this into account when defining the frequency and dates of your exports.

]]> HR Chatbot: A Pre-Defined Knowledge Base to Quickly Deploy an Internal Chatbot https://uat.dydu.ai/en/hr-chatbot-a-pre-defined-knowledge-base-to-quickly-deploy-an-internal-chatbot/?utm_source=rss&utm_medium=rss&utm_campaign=hr-chatbot-a-pre-defined-knowledge-base-to-quickly-deploy-an-internal-chatbot Thu, 28 Jul 2022 16:23:40 +0000 https://uat.dydu.ai/?p=987829

For the past 10 years, alongside our customer relations chatbots, we have been deploying internal chatbots, particularly for HR departments. Drawing on this experience, we developed a HR Starter Pack in 2020, which was the first version of our ready-to-use chatbot. Today, we offer an even more complete version that includes a knowledge base with more than 300 articles (pre-defined questions and answers) and 110 matching groups, covering 20 HR topics. This knowledge base allows organisations to deploy their chatbot much faster and more efficiently from the start.

This article takes a look at the benefits of our HR chatbot and describes the content of our HR employee support chatbot.

Why Should You Implement a HR Chatbot? 

Employees have the same expectations as consumers. They want simple, precise and quick answers to questions about company life.
As for HR departments, they are at the heart of a number of issues that touch on employee everyday life. They are also subject to frequent changes in their environment (economic, regulatory and legal, etc.). Centralising and sharing information quickly has therefore become a constant challenge. HR departments cover a wide range of topics. This combined with growing employee demands, means that HR departments have to answer a large number of questions on a recurring basis. Automation can therefore provide a simple and effective solution.

For employees, chatbots are the guarantee of a single point of contact, which can be accessed 24/7, from any device, and which provide instant, simple and consistent answers to any of their questions.
The tool understands natural language, so it is particularly inclusive. Users can express themselves freely and without any effort. The bot understands different levels of language, interprets typos, doesn’t care about spelling mistakes and recognises all the acronyms it has been taught. The nature of the answer and time it takes for the bot to respond, is the same regardless of the user’s position within the organisation or particularities.

For the HR department, this is a great way of removing time-consuming and low-value-added tasks. This frees up time for more complex, new subjects or those requiring personalised support. Chatbots are also internal communication tools in their own right. They can deploy messages quickly and easily (alerts, reminders, important modifications such as site access rules that have changed a lot in recent months, etc.).

Finally, and more generally, implementing a bot for employees is positive for the company. 75% of HR managers believe that it enhances their image. And 73% think that it is an asset for the employer brand (*).

The Dydu Solution Owns its Own NLP

For more than 13 years, Dydu has provided natural language processing software that enables companies to deploy conversational robots and manage them autonomously. This solution includes:

  • a user interface (dialogue box) available on the environment of your choice (website, intranet, mobile app, desktop, internal messaging system)
  • a BMS (Bot Management System): this is the bot’s admin console. It is intuitive, easy-to-use and does not require any technical skills
  • Monitoring tools to continually monitor the bot’s performance and improvements

In addition to customer relations projects, Dydu soon began working on internal projects for HR departments, as well as IT, support and legal departments.

We have thus deployed HR department chatbots in many companies, in a variety of sectors, such as banking and insurance, energy, transport or public service. Over the past three years, we have developed and improved the content part of our HR employee support chatbot.

In addition to the Dydu software solution, you therefore benefit from a tried and tested chatbot knowledge base. Indeed, we provide pre-defined content that includes:

  • More than 300 predefined questions and answers, to personalise with elements linked to your business and company specificities.
  • 110 matching groups that have already been set up. This refers to all the different ways of expressing the same intent. For example, the chatbot understands the terms “company restaurant”, “cafeteria”, “canteen” or “self-service restaurant”.
  • 20 recurring themes such as administrative matters, employee benefits, leave and absences, employment contracts, yearly appraisals, training, expenses, management of arrivals and departures, health insurance, retirement, payroll, bonuses, employee savings, quality of life at work, health, HRIS, working hours, remote working and company life.
  • A social base that makes interactions more pleasant, with greetings and small talk, for example.
chatbot rh dydu

In other words, you benefit from a pre-trained chatbot with more than 150 use cases, a knowledge base that has already been approved by HR experts, ready to be deployed on your intranet, desktop or mobile, in SaaS or on-premise, as well as via your internal messaging system, such as Microsoft Teams.

Our Dydu experts will provide you with advice and recommendations, to help you finalise your answers. You can disable topics or questions that do not apply to your organisation, and also add more to enrich your knowledge base.

In addition to this base, depending on the level of service you want to your chatbot to provide, you can connect it to your HRIS and other internal software. This will enable you to offer greater customisation and to manage more complex use cases: request to view leave balance, book a training course, create or follow up a ticket, etc. You can also provide different answers to the same question according to specific technical or functional criteria (e.g., the type of device used – PC or mobile phone-, authentication, or a social criterion). Finally, you can use the bot in different languages. The Dydu knowledge base is in French. All you have to do is import and configure the new language(s) you have translated. 

Thanks to the Dydubox, you can also set up your chatbox and customise it according to your company’s graphic identity.

This predefined HR knowledge base is a real time-saver, both internally and in terms of support. This has a positive impact on implementation times and costs. Thanks to all the previous learnings, you can benefit from an efficient but from the launch, which will accelerate its adoption within your organisation.

Take a look at our Human Resources page.

(*) Chatbot Observatory – April 2020.

]]> Voxygen Testimonial: How Can Speech Synthesis Improve User Experience? https://uat.dydu.ai/en/voxygen-testimonial-how-can-speech-synthesis-improve-user-experience/?utm_source=rss&utm_medium=rss&utm_campaign=voxygen-testimonial-how-can-speech-synthesis-improve-user-experience Thu, 28 Jul 2022 16:05:12 +0000 https://uat.dydu.ai/?p=987706

Dydu works with technological partners, such as Voxygen to improve our callbots’ speech synthesis. Would you like to know how the creation of speech synthesis works? Understand the challenges of voice marketing? Read on to discover Christian Sassady’s experience, the sales manager at Voxygen.

Can you quickly describe your company?

Voxygen is a speech synthesis solution editor that uses artificial intelligence. We sell software solutions and create personalised voices for our clients. Voxygen was founded in 2011 as a spin-off from Orange Group. Our headquarters are in Brittany.

What use cases do your clients call on your services for? Which are the most represented sectors among your clients ?

Most of our projects are in customer relations and focus on call centre and IVR (Interactive Voice Servers) issues. We work with a wide range of use cases, such as one-off messages, emergency messages or dynamic self-care scenarios (IVR, callbots). We also work a lot with transport companies for customer announcements. We are best known for the digitalisation of Simone Hérault’s voice, who has been the voice of the French rail service, SNCF, voice for more than 35 years, and who has formed a genuine relationship with users through her voice. A digitalised voice enables a seamless omnichannel user experience.

There are more and more use cases linked to the development of voice assistants, connected speakers and mobile apps. The health and car industries are also using voice more. In the future, voice assistants will be installed in all vehicles. This is a service that all manufacturers are developing and will continue to do so in the years to come. For the health sector, speech synthesis is used for medical simulation, care and student/doctor training.

Can you describe Voxygen’s products and services?

We offer a speech synthesis software solution that converts text into speech, as well as a service that creates specific voices. We have a catalogue of standard voices but can also create personalised voices for clients with a strong voice identity, who want to optimise their customer experience. Our goal is not to create a big catalogue of digitalised voices available to everyone. Indeed, we firmly believe that all big companies will eventually have their own voice identity, just as they have a logo, slogan or graphic identity. We will therefore create personalised voices for them. They can then apply this voice identity to all their voice services and thus optimise the user’s customer experience.

Our voices are developed using artificial intelligence solutions and neural networks. These synthesised voices are then used by our clients and partners via our Text-to-Speech software. This software converts text into expressive speech that closely resembles a human voice.

Voxygen’s speech synthesis component is often interfaced with a more global product, which allows for a genuine man-machine interaction. An example of this is dydu and insurance provider MACSF’s callbot project. Dydu’s natural language processing software calls on Voxygen’s API to generate the callbot’s voice.

How do you create custom voices?

We have developed a comprehensive method for creating custom voices for our clients. There are several scenarios:

  • A client who has already identified a speaker (otherwise known as a “voice talent”) for their vocal identity. The SNCF is an example of this, as they already have lots of messages recorded by Simone.
  • A client who is starting from scratch. They haven’t identified a voice yet and need help for the entire project (from defining their needs to creating and delivering a digital voice).

For the second scenario, we run workshops with the client to understand their expectations. What style of voice are they looking for in relation to their use cases? What expressiveness? What brand values should the digital voice convey? Based on the client’s brief, we then work with recording studios to cast the voice and present it to the client.

Once we’ve identified the speaker, we spend several days in a recording studio reading a script (list of sentences). This script is prepared before the recording, based on Voxygen’s know-how in terms of linguistics and acoustics, as well as the client’s business data. The more contextualised the voice is, the more natural and smooth it will be for the client’s use cases. Our engineers oversee the recordings, and we create a kind of sound library for that voice.

The audio recordings are then processed to create the synthetic voice using AI technology and our tools. Once the voice has been created, it is delivered along with the software solution chosen by the client. The client can then use our solutions completely autonomously. When requests are sent to the solution, it delivers an audio message in real time, even if it isn’t one of the sentences recorded. Speech synthesis should be able to pronounce anything.

What’s your value proposition compared to players like Google?

Voxygen is a 100% French company. We’re still an SMB, which allows us to provide our clients and partners with responsive and flexible solutions. Players such as Google only work with the cloud, whereas we also offer on-premise or even embedded software for clients that request it.

The main difference is that we provide our clients with custom support when creating personalised voices. We also believe that we offer real added value in the quality of our voices and even more so in the creation of our personalised voices.

What are your next challenges in terms of product?

Our teams are always working on optimising the quality of our voices in French, as well as in other languages. We improve them using AI and neural technology. Clients are more and more demanding when it comes to the quality of voices. They want a fully automated, flexible and high-end service.

In the future, speech synthesis will make it possible to manage fully automated and personalised business use cases with user-specific data. Some of our clients have already deployed solutions for these types of uses.

What are your market prospects?

We’re mainly working on customer relations projects. But more and more use cases are emerging on mobile apps, connected speakers or home automation, for example. Companies can deploy their own voice identities on all their channels. Whether on a PC, smartphone or connected speaker such as Google Home or Alexa. Using the same voice everywhere will strengthen brand identity and provide customers with an optimised experience. It doesn’t make sense to use different voices on each channel.

]]> A Callbot for Your Company ? Business Opportunities and Technological Robustness https://uat.dydu.ai/en/a-callbot-for-your-company-business-opportunities-and-technological-robustness/?utm_source=rss&utm_medium=rss&utm_campaign=a-callbot-for-your-company-business-opportunities-and-technological-robustness Fri, 17 Jun 2022 15:50:25 +0000 https://uat.dydu.ai/?p=822022
visuel callbot

A callbot is a conversational voice robot that can be accessed via a telephone number. Phone remains the preferred channel for users (1st or 2nd place according to studies) and the automation of a certain number of use cases for companies with large volumes of calls, is justified by financial imperatives and user expectations. According to a Forrester study, 72% of consumers prefer to solve problems by themselves, rather than contact someone at customer service. Callbots improve the customer experience (reduced waiting times, simplified processes, optimised direction, etc.) and also improve operational efficiency. This has a positive impact on costs. Callbots are particularly widespread in the insurance, telephone, transport, e-commerce and tourism industries, whether at the beginning of the relationship (information request, order processing, tracking, etc.), or at the end for claims and after-sales-services. 

Like all our bots, callbots are based on natural language dialogue technology.  From a technical standpoint, they function like chatbots, with the addition of speech recognition (STT: Speech To Text) and speech synthesis (TTS: Text To Speech). In terms of telephony, they can connect to your existing IVR or replace it. Callbots understand user intents phrased in their own words. They can manage punctuation and pronunciation, as well as SSML tags (pauses, reading of numbers, dates and times, pronunciation instructions). Special developments also allow to manage speech interruptions, phrasings used only in the spoken language, etc. Callbots can also provide generic and personalised information, by easily interconnecting with your software (CRM, ticketing tool, booking system, etc.). They can then trigger actions such as opening a ticket or a claim and sending a confirmation email or SMS.  

There are lots of different specific use cases, such as making a claim, signing up to a contract, banking (checking balance, cancelling a card, ordering a cheque book, etc.). The elements used in conversational strategy apply to both callbots and chatbots. Let’s take a look at 2 examples involving making appointments via a callbot.

Date Management by the bot

This applies to lots of different sectors. For example, booking a ticket for a train or a show, a table at a restaurant, an appointment with a customer advisor, or enquiring about the time for a screening… Whether to get information about opening hours, service times, or even make an appointment, callbots use lots of different functionalities.

Ideally, the callbot is given a specific time and date. In a conversation – whether via text or voice -, users may be precise and unambiguous (except perhaps 15/09 or 09/15 depending on if they’re from the UK or the US… 😊 – or use vague expressions, such as “mid-month”. The algorithm has to understand and dynamically manage these expressions to make them specific. For example, tomorrow after the 15th is not tomorrow after the 16th… The software also needs to include pre-configured expressions such as “late morning” or “early in the week” to make suitable suggestions.

All of these considerations cannot, of course, be translated literally. Certain adaptations need to be made to ensure the same level of understanding in another language.

Dydu has worked on corporate concierge and transport project, which has enabled us to build a certain expertise on the matter. Today, all our clients can benefit our know-how.

Callbot Recognition of Proper Nouns and References

We’re always striving to perfect our solution to answer frequent requests. In the speech recognition field, callbots are often positioned at the beginning of the customer relationship, and more and more scenarios require them to qualify the calls: reason for calling, subject identification (order, package, legal entity or actual person, etc.), authentication, etc.
Although decision trees allow to easily handle requests, identification involves the understanding of proper nouns, booking or file numbers including letters and/or numbers, etc. The callbot has to be able to manage accents, background noise, pronunciation difficulties etc. Dydu has worked with CATIE (The Nouvelle-Aquitaine Digital Technology Centre) to improve the software’s spelling understanding.
Let’s return to our example. To confirm an appointment, you need to provide your name after choosing a time slot.

In most cases, the callbot is connected to a call centre. This means that, if necessary -i.e. if the callbot can’t handle the call from beginning to end -, the operator already has all the information they need to quickly and efficiently solve the caller’s problem.
Once again, conversational strategy and the autonomy to manage and optimise your solution makes all the difference. Give it a try!

]]> Dydu Launches the Dydubox, a New Interface to Configure Your Chatbox in Just a Few Clicks ! https://uat.dydu.ai/en/dydu-the-new-interface-to-configure-your-chatbox/?utm_source=rss&utm_medium=rss&utm_campaign=dydu-the-new-interface-to-configure-your-chatbox Tue, 30 Nov 2021 14:10:04 +0000 https://uat.dydu.ai/?p=738211

You can configure and customise the front-end of the bot, otherwise known as the chatbox. This new interface, called dydubox, will save our clients time and make them more autonomous. It enables to easily try out different chatbox designs, texts and features and to make changes whenever you like.

configuration chatbox

Customising Your Chatbox for the Web with Dydubox

When you first connect to our BMS (Bot Management System), you access an interface. This interface enables you to configure your chatbox design for a web integration (extranet or intranet). This is where most chatbots are deployed.

Simply select the website channel and you’ll be directed to the dydubox !

create a new configuration

Do you want to add a chatbot to Microsoft Teams ? No problem ! Read our article about the Teams connector.

Choosing a Design

It’s important for companies to deploy a chatbot that reflects their identity. This includes the way the bot speaks (tone) and the chatbox design. With the dydubox, you can choose your bot’s main colour. This colour is applied to several items, with different degrees of opacity (button and CTA, links, answer bubbles).

You can also upload logos or avatars to represent your bot. There are several types of avatars used at different points during the end-user journey:

  • The “teaser” avatar on your website or mobile app (usually bottom right). This is the button that encourages users to open the chatbox.
  • The avatar next to the bot’s chat bubbles, which is the same as the teaser avatar.
  • The avatar in the chatbox header which can be different depending on the bot’s level of understanding: neutral, rephrasing, sentence understood, sentence not understood.

In this first version, the 3 avatars are identical.

Choosing the Text

All the texts in the chatbox that are not managed in the bot’s knowledge base can be customised via a file (per bot language) that is exported and re-imported into the dydubox. This applies to the following texts:

  • GDPR disclaimer: before the conversation begins, the chatbot must inform the user of the collection and processing of their personal data and get their consent
  • onboarding: before the conversation and after the GDPR disclaimer, you can provide the user with some tips to maximise their use of the bot
  • Header title: this is the text in the chatbox’ top bar. You could, for example, enter the bot’s name or the phrase “need help?”
  • Banner: text box displayed when the user opens the chatbox for the first time. You could, for example, display important brand/company news or warn the user that the chatbot is still learning and ask for their patience

This is a non-exhaustive list and may evolve with our product developments.

Please note that the introduction phrase will now be managed in the knowledge base for greater flexibility and autonomy and to benefit from all the knowledge features (side bar, rich content, etc.).

interface chatbot

Choosing Features With the Dydubox

With the dydubox, you can also choose to enable or disable the features available in the chatbox:

  • Header: buttons to enlarge, reduce, make disappear, more options (export conversation by email, request to recover/delete conversation), different avatar depending on understanding
  • Possibility to add a “contact” tab
  • Window: banner, top knowledge, avatar, feedback and reasons for dissatisfaction, loader
  • Footer: display and choice of language by the user, character counter, auto-suggestion

These elements, along with many others, will be made available gradually.

Ease of Use and Accessibility

The dydubox was designed to be as easy to use as possible for company employees who administer our bots. The aim is to guarantee the best experience for all users.

Simply enable or disable items by checking or unchecking the boxes. You can define the user interface by selecting options in the drop-down menus (colours, fonts, etc.). And you can define your permanent text (GDPR, on-boarding, etc.) and update them whenever you like.

The dydu chatbox also complies with French RGAA accessibility standards in terms of content and navigation, and we will provide you with guidelines to ensure that your content does too.

]]> The bots dydu, Now Available in Microsoft Teams ! https://uat.dydu.ai/en/the-bots-dydu-now-accessible-in-microsoft-teams/?utm_source=rss&utm_medium=rss&utm_campaign=the-bots-dydu-now-accessible-in-microsoft-teams Fri, 12 Nov 2021 14:07:05 +0000 https://uat.dydu.ai/?p=732895
man typing on his computer keyboard

Dydu has launched a specific connector with the collaborative communication app Teams! The aim? To easily integrate  dydu chatbots into Teams.

Internal Bots on All Company Channels

Bugs, forgotten passwords, questions about how to use a software, a new agreement or new company guidelines, internal mobility… These are just some of the time-consuming queries that support departments have to deal with. More and more companies are therefore looking for ways to optimise and automate their support and request processing with a chatbot. Especially in IT and HR.

Internal bots make employees more autonomous when it comes to low added-value requests. They therefore help to relieve the pressure on the most frequently contacted departments. These tools already exist on company intranets and mobile apps. Now, they can easily be deployed on the Teams collaboration messaging system from Microsoft. The more a chatbot is present and visible on all company communication channels, the more likely it is to be used. It can then become a permanent contact point for all your employees!

A specific Connector Developed for Teams

In October 2020, Microsoft reported 115 million daily Microsoft Teams users in the world. Which is an increase of 50% compared to the 75 million reported 6 months previously. The collaboration software benefited from the accelerated rise of remote working due to the COVID-19 health crisis. A trend that analysts don’t expect to change any time soon…

The connector is an API that enables an application to communicate with another application and to transfer data. Teams is a completely independent channel from the dydu chatbox, so we had to develop a specific connector to ensure that most of the software’s front-end features worked. From a user standpoint, employees in Teams can search for the bot’s name and start a conversation as if they were talking to a colleague.

A Simplified Integration

With this connector, most dydu software features are also available in Teams:

  • Answer formatting features: bold, bullet points, rich content such as images, gifs, links, etc.
  • Answer features: alternative answers, redirection to other knowledge articles, use of variables to personalise answers, etc.
  • General sentences: rephrasing sentences, sentences for misunderstandings
  • Types of knowledge: simple knowledge articles, decision trees, complementary answers, etc.

Deploying a bot on Teams from the dydu software is easy. The bot manager chooses the channel of  choice in a few clicks (website, Teams and soon Slack and Facebook Messenger, which can already be connected via the BMS external messaging connection). They then enter their chatbot login and password, created beforehand in the company’s Microsoft Azure account. And finally, add the bot avatar and download the Zip file to submit it to their company’s Teams app catalogue. Once the administrator has approved the app, the chatbot is visible and can be used in Microsoft Teams by any company’s employees.

configuration teams

Our teams support our clients as they implement their bot in Teams. We provide implementation advice and documentation to connect the dydu API to Teams in Microsoft Azure. We also specify the list of dydu features available for this channel.

Want to know more about our internal conversational bots? Check out our page about bots for your employees.

]]> Top 10 Key Conversational Bot Capabilities https://uat.dydu.ai/en/top-10-key-conversational-bot-capabilities/?utm_source=rss&utm_medium=rss&utm_campaign=top-10-key-conversational-bot-capabilities Fri, 12 Nov 2021 11:43:20 +0000 https://uat.dydu.ai/?p=730491
computer

There are a lot of players on the French and global bot market (chatbots, callbots, voicebots). There are more or less recent pure-players. More and more, various big software editors add conversational components to their solutions. There are also integrators who offer “tailor-made” solutions.

But not all products are equal. Below is an overview to help you find the best fit for you, depending on your objectives and user experience requirements. This article illustrates the richness of our own conversational strategy.It also provides some guidelines for studying the various solutions on offer.

NLP, the Essential Engine for an AI Bot

Dydu has always had its own linguistic component, which means that we don’t rely on an external player for natural language processing. This enables us to be completely independent. Our linguistic component has a particularly good handle on French and can manage ten or so different languages.

A Simplified Explanation

Our linguistic component calculates the distance between the user’s request and all the phrases in the knowledge base. The knowledge base gathers all the chatbot’s questions, answers and alternative phrases. When this distance (difference) is low, the chatbot can provide an answer to the question. This distance is calculated with a matching score. Depending on the score, the bot either replies immediately, suggests another way to phrase the question, or informs the user that it doesn’t understand. This provides far more relevant answers than a keywords-based system.

This mode of operation also makes it possible to quickly identify any requests what needs to be added to the knowledge base, without having to constantly check the rest. In order to avoid a combinatorial explosion and to minimise the user’s workload, the software has a tree structure of possible phrases, facilitated by matching groups (groups of synonymous or similar words or expressions).

More Specifically

Dydu’s NLP corrects spelling mistakes, identifies lemmas, spots synonyms and hypernyms, and “stems” words. It calculates the distance between all the words to understand the sentence’s general meaning. It divides the user’s request into intents and entities. This enables it to match the user’s questions to content that already exists in the knowledge base, and to answer the overall question. This avoids intermediate choices at each stage and the risk of wrong answers.

This algorithm is at the heart of our solution’s performance, but a number of easy-to-manage features also help to build a real conversational strategy. They all contribute to making the conversation as natural as possible.

Must-Have Conversational Bot Capabilities

  • Providing a series of answers on the same subject
  • Suggesting alternative answers
  • Explaining
  • Anticipating the user’s question
  • Asking the right questions to provide the right answer
  • Clarifying an answer with additional information or a suggested action
  • Providing personalised answers
  • Knowing how to handle digressions
  • Guessing an answer based on the previous questions
  • Being polite 🙂

Providing a Series of Answers on the Same Subject

In a real-life conversation, you don’t have to constantly repeat the initial subject. This may seem obvious, but a lot of language recognition engines need the topic to be explicit in every question. Dydu’s software manages “bounce conditions” which allows users to ask several questions on the same topic. The user doesn’t have to repeat what their question is about each time. For example, if a user asks: “is health insurance mandatory?”, the answer will be “yes […]”. The next question will probably be: “how much does it cost?”. The bot will understand “how much does health insurance cost”, without the user having to specify it again.

Suggesting Alternative Answers

If a user asks the same question twice, it’s usually because the first answer wasn’t satisfactory. Being able to answer again using an alternative sentence, an image or a practical example particularly useful in a conversation.

Explaining

Some answers are complicated. Just like in real life, sometimes a diagram is better than a long speech. An image or video can be displayed in an open sidebar as part of the answer. You can also provide answers in the form of step actions. This is particularly useful for questions on how to proceed with something.

Anticipating the User’s Question

People often spontaneously finish each other’s sentences, not to interrupt them but to show that they’re listening and interested in what the other person is saying.

The matching feature begins to work as soon as the user starts typing in the bot’s text box. The bot can use an autosuggest feature to dynamically suggest pre-exiting questions. This feature is more sophisticated than autocomplete which is on most mobile phones. You can configure the number and way these suggestions are displayed to optimise conversations. The user can then simply click on their question instead of having to type it in full. This mechanism is based on dydu’s NLP engine. Meaning, once again, that it does not use keywords. This allows to make suggestions using the brand’s vocabulary, even though the user initially typed their own words. For example “customer account” vs “online agency”.

Asking the Right Questions to Provide the Right Answer

In some cases, the bot might need to retrieve certain information to provide the right answer. After retrieving all information spontaneously provided by the user during the conversation, the chatbot will dynamically ask for any missing information (slot filling). This applies for example to a ticket or event search, when it’s important to know the place(s), date(s), time(s) etc.

Clarifying an Answer with Additional Information or a Suggested Action

Depending on the information provided during the conversation with the end-user, it can be useful and relevant to provide additional information. The bot can recognise behaviour and predefined expressions used during the conversation and trigger a specific action. For example, the bot could give an answer and then provide a redirection URL. Or, to give another example, you could set up an automatic escalation to a human operator if the bot is unable to provide an answer more than 2 times during a conversation, or if the words “cancel” or “change my contract” have been used. Each bot administrator can decide what makes the most sense for their company.

Providing Personalised Answers

This contributes to your bot’s perceived value and is probably the area that the dydu engine performs the best. The connection via API with other information systems enables the bot to enrich the dialogue with user-specific information. If the bot is connected to a CRM, it can identify an existing customer with specific promotional codes, for example. For banking, the bot could provide the user with their bank balance, after authentication. If the bot is connected to a HRIS, an employee could check their exact number of leave days and not just how many days off company employees get.

Bots work best when they provide short and precise answers, so you could provide an answer in the form of a decision tree.

Decision trees allow to put the user’s question into context. The bot will ask a series of sub-questions to provide a precise final answer. For example, in France, if you ask the council chatbot about obtaining a civil status certificate, it will ask you to choose between a birth, death and marriage certificate. It will then ask if the person requesting the certificate was born in France or abroad. Only then will the bot explain how to obtain a birth certificate for a French citizen.

Understanding the user context is key to provide a specific and non-generic answer.

Using variables enables the chatbot to retrieve user data during the conversation. And to use it to provide an answer.

These variables can be retrieved from an explicit user statement. Or a transfer of the chatbot’s context (via its webpage, geolocation or telephone number in the case of a callbot). Or data from an API. It could be the user’s name, age, type of contract or any other information that the user may have provided during the conversation, or that has been retrieved via authentication (and therefore accepted by the user within the framework of GDPR).

Knowing How to Handle Digressions

When a conversation “enters” a decision tree, the user can still ask a question on another topic. The bot can answer that question and roll out the end of the decision tree afterwards.

Here is an example:

  • User :  “I want to receive my invoices by email.”
  • Bot :  “To subscribe to our paperless invoice service, I need your customer reference number. Could you please enter it here ?” (The decision tree is expecting the user to type their customer reference number, but instead they call on another knowledge article)
  • User : “Where can I find my customer reference number?”
  • Bot :  “You can find your customer reference number at the top of your invoices, to the right.”
  • User : “1234ABCD”

And so, the initial decision tree continues…

Guessing an Answer Based on Past Questions

Another of our native features allows to better understand the user’s question without creating a decision tree, based on the questions already asked.

Imagine a HR knowledge base includes the following articles :

  • What’s the XX app for?
  • How do I install the XX app?
  • What’s the YY app for?
  • How do I install the YY app?

The conversation might look something like this :

conversation chatbot

If the engine only takes into account the last question (How do I install it?) and does not look at the context, it cannot determine whether the knowledge article about installing the XX or YY app is needed. But by taking past questions into account, the bot can provide the user with an answer to the question “How do I install the XX app?”.

Being Polite 🙂

“Hello”, “goodbye”, “thank you”, “tell me a joke”… Small talk is a key part of conversational experience. Not all bots come with a standard social base, which takes a very long time to create from scratch.

To recap, dydu’s technology is able to contextualise and discuss several topics at the same time, which optimises your user interactions. And that’s exactly what users are looking for: precise, quick and correct answers to their questions, however they ask them. At dydu, we are continually improving our technology and always put ourselves in the user’s shoes !

]]> Chatbots : Essential Functionalities for Optimal Understanding https://uat.dydu.ai/en/chatbots-essential-functionalities-for-optimal-understanding/?utm_source=rss&utm_medium=rss&utm_campaign=chatbots-essential-functionalities-for-optimal-understanding Wed, 03 Feb 2021 19:33:30 +0000 https://uat.dydu.ai/?p=596732
NLP machine learning chatbot

You’re interested in implementing a chatbot to improve user satisfaction and increase productivity, but you don’t know which software to choose among all the publishers on the market… You’re concerned about overly limited FAQs, or chatbots that don’t understand what’s being asked of them… Sound familiar?

The level of success for a conversational robot mainly depends on the quality of content, and the bot’s ability to understand questions and retrieve the right answers from the knowledge base. This article provides advice about which technology and functionalities to prioritise, in order to make your chatbot project a success.

The NLP Algorithm, your Chatbot’s Engine

The technology behind chatbots, called natural language processing, is essential for it to work properly. This technology is based on an NLP algorithm (Natural Language Processing), that identifies the user’s question and matches it with the suitable answer.

There are several natural language understanding methods – distance calculation, syntactic analysis, and keyword matching. To find out more about how natural language processing works and the benefits of each method, read this article. 

All technologies are not equal, and it is important to find out which method is used, the algorithm and matcher’s performance (by testing), the connection with external matchers, and to identify whether the publisher owns the technology or uses a third party software.

Essential Functionalities

  • Politeness and Small Talk

Your bot should be able to hold a smooth and natural conversation with your users. Like a human, it should therefore understand and use key phrases (hello, please, thank you, etc.) and small talk (compliments, jokes, etc.). To save time and to avoid creating it yourself, ensure that your bot’s knowledge base includes a social base.

NOA, the Paris and Ile-de-France Préfecture chatbot :

joke nlp chatbot dydu
  • Formulations

There are lots of different ways to ask the same question. You chatbot needs to be able to understand them all. Matching groups (groups of words and phrases that mean the same thing) and formulations allow to add these different words and phrases under a same knowledge article.

  • Activity Monitoring and Improvements

You’ve created and tested your knowledge base and deployed your bot on the channel of your choice. But that’s not the end. It’s important to regularly monitor your bot’s activity, to continually improve it.

There are several functionalities to help you with this. Firstly, analytics, which indicate, among other things, the quality of your interactions and dialogues (the bot’s level of understanding of the questions and its ability to provide the right answers) and user satisfaction with regards to the answer given. Quality alerts are also useful to check the quality of content according to predetermined and customisable criteria (broken links, long answer time, etc. ). Other functionalities can help continually improve your base. Analysis of failed dialogues or misunderstood sentences enables you to add new phrases to an existing knowledge article, or to create a new one. You can also identify similar knowledge articles in your base and choose to group them together or make them more distinguishable. 

Are you interested in creating a chatbot? Feel free to contact us. 

]]> How a virtual assistant can provide Microsoft 365 support for your employees ? https://uat.dydu.ai/en/chatbot-microsoft-office-365/?utm_source=rss&utm_medium=rss&utm_campaign=chatbot-microsoft-office-365 Wed, 18 Nov 2020 12:06:00 +0000 https://uat.dydu.ai/?p=571599
Zen chatbot dydu Ai3 microsoft 360

Over the past few years, remote working has become more and more widespread, increasing sharply since the beginning of the health crisis. Along with the many advantages this presents for employees and businesses, a number of new challenges have also emerged. How can you improve efficiency and teamwork when your employees are isolated? How can you ensure that your employees have mastered their working tools? How can you relieve IT departments of their increased workload?

This article will explain how a chatbot solution can help IT departments meet these new challenges, and also take a look at Zen, the first chatbot dedicated to Microsoft 365 created by Ai3 in collaboration with dydu. Ai3 is a consulting company and innovative solutions integrator, specialising in Microsoft solutions. It recently became part of the Talan group.

Chatbots, an essential tool for IT Departments

Over the past few years, more and more large and medium-sized companies have implemented internal chatbots, primarily to relieve pressure on the departments that receive the most requests, such as the Human Resources, IT or legal departments. 

Among dydu’s 160 bots in production, almost 30 are IT/helpdesk chatbots that manage several hundred to several thousand dialogues per month, depending on the client company’s size. IT bots are far more than a passing trend – there are real benefits for both the end users and businesses that implement them.

  • An Improved Customer Experience

Password management, software or application support, equipment orders, account management… Chatbots can handle all the simple questions relating to IT and office applications and pass any more complex requests over to the support teams.

Conversational robots provide instant answers, 24/7, on any channel – intranet, desktops, mobile apps, messaging platforms… They can act as an IT department’s single point of contact for questions and are fast and accessible. The answers are all created and approved by the IT department, so they are reliable and consistent, and can also redirect to existing content such as documents, intranet pages or external links, and provide step-by-steps, tutorial videos and explanatory images directly in the chatbox or in the associated sidebar.

IT chatbots are particularly useful because IT requests are often urgent and need to be resolved so that employees can get back to work. This tool not only provides immediate answers, but also 24/7 assistance, which is a real asset, particularly for employees working staggered hours. Virtual assistants help users complete simple tasks by themselves, through existing company documentation – tutorials, troubleshooting guides, step-by-step instructions, etc.

  • Optimised Operations

Implementing an IT chatbot significantly reduces the number of incoming tickets and improves the request management process. IT chatbots reply to all the simple and frequent requests, thereby reducing the number of support requests and enabling IT managers to focus on their primary mission – to improve the company’s IT system, thereby improving work efficiency.

By encouraging employees to be more autonomous, the IT department’s productivity is increased and costs relating to support are reduced. Chatbots are particularly effective during IT migrations, which usually generate a lot of questions. They can also provide general information in advance by displaying version upgrades, potential interruptions or future benefits.

Zen, the first chatbot dedicated to Microsoft 365

As a support for Microsoft solutions, Ai3 decided to create a chatbot to answer questions about the use of applications in the Microsoft 365 suite – Excel, Outlook, Teams, OneDrive, SharePoint, Skype, Yammer, Kaizala, etc. Ai3 used dydu’s technology to deploy Zen.

dydu ai3 partenariat chatbot informatique helpdesk
  • A Comprehensive and Scalable Knowledge Base

Knowledge articles:  Zen covers all the usual themes, such as messaging, collaborative work and office tools. The knowledge base includes more than 600 knowledge articles that are regularly updated by SoYouz, Ai3’s digital transformation consulting agency. 

Matching groups: The bot also includes matching groups, that group together several ways of saying the same thing (for example, Excel: worksheet, spreadsheet, XLS, Microsoft Excel, etc.). Matching groups facilitate the bot’s understanding of a question. The knowledge articles and matching groups are based on more than fifteen tools from the Office 365 suite. 

The social base: In addition to IT knowledge, the bot has a social base, meaning it recognises forms of address and ways of speaking that make the dialogue smooth and natural. This applies to all dydu bots.

All our company clients share the same knowledge base, meaning that their chatbots are always up to date, without any actions required on the client’s part. Dialogues are monitored, and when a knowledge article is added, it is added for all clients. It is however possible to personalise about ten specific knowledge articles (IT support contacts details for example).

All knowledge articles are in both French and English. It’s easy to switch from one language to another with a button in the chatbox.

  • A Turnkey Solution

Chatbots are very easy to set up – companies are provided with a code to deploy the chatbot on the channel of their choice (webpage, intranet, support site, etc.). All the client has to do is personalise ten or so knowledge articles and integrate the code on their chosen channel(s).

  • Advanced Features

Chatbots answer questions in a text format, but they can also provide rich content, such as links, images, GIFs, step-by-steps or videos in the chatbox or sidebar for better readability. They can also ask other questions on the same subject to provide further information. If the bot misunderstands a request, it calculates the probability of the question and suggests reworded alternatives.

Want to know more about the Zen chatbot? Feel free to reach our team !

]]> Customer Relationship – the benefits of a chatbot-RPA alliance https://uat.dydu.ai/en/customer-relationship-the-benefits-of-a-chatbot-rpa-alliance/?utm_source=rss&utm_medium=rss&utm_campaign=customer-relationship-the-benefits-of-a-chatbot-rpa-alliance Mon, 16 Nov 2020 12:06:48 +0000 https://uat.dydu.ai/?p=571617
customer service RPA chatbot dydu devoteam ui path

How can you improve your customer experience and strengthen support without increasing the workload for your customer services? By encouraging customers to be autonomous with selfcare solutions!

FAQs, chatbots, community forums… Over the past few years, these tools have become part of customer services, enabling users to find an answer to their question quickly, and to solve problems themselves. Selfcare solutions can reduce the number of incoming requests by 30%, thereby improving your customer service department’s productivity and lowering costs. Meanwhile your customer experience is also improved because the customer’s question is answered quicker than it normally would be, exactly when they need it, even late at night or during the weekend.

Certain tools, such as chatbots, by directly performing actions. This is made possible by several methods, including the combination of chatbots and RPA (Robotic Process Automation).

This article explores the benefits of this solution for customer services, particularly in the tourism industry.

Chatbots and RPA – from information to action

Chatbots can provide users with generic answers, contextualised ones (depending on whether the user is using a computer or telephone, their location, or other variables), or even personalised ones (if they have been authenticated).

To enable customers to perform actions from the bot, such as checking their latest bill, filing an insurance claim, editing information on their customer account, or changing a reservation, the company needs to connect the chatbot to its tools (CRM, IS, reservation management tool, etc.). Several methods can be used for this:

  • Connecting the bot and software via a dedicated API (Application Programming Interface)
  • Coupling the chatbot with RPA (Robotic Process Automation) technology, which is also connected to the company’s various tools

“RPA is the technology that allows anyone today to configure computer software, or a “robot” to emulate and integrate the actions of a human interacting within digital systems to execute a business process. RPA robots utilize the user interface to capture data and manipulate applications just like humans do. They interpret, trigger responses and communicate with other systems in order to perform a wide variety of repetitive tasks. Only substantially better, an RPA software robot never sleeps, makes no mistakes and costs a lot less than an employee.” UI Path 

Chatbots understand the user’s request and simulate human conversations, whereas RPA automates collection, analysis and information writing operations, in order to edit documents, forms and web pages without human intervention, even if human supervision remains important.

The benefits of automating processes via a chatbot

The main benefit for users is the availability of the tool and the time it saves. Chatbots handle the entire request immediately, even outside working hours. As for companies, they can extend and refocus their human operators’ productivity, generate lower costs, and reduce the risk of errors. 

The advantage of RPA technology is that it serves as a single interface for the bot. Companies don’t have to deploy a dedicated API for each application / software they wish to connect to the bot.

Automating processes via a chatbot is beneficial for customer services that receive a lot of requests, such as companies in the banking, insurance, travel, logistics, consumer goods, telecommunications and healthcare sectors.

Use cases in the tourism industry

Since last March, a lot of people have had to change or cancel their reservations. A train ticket, a holiday rental, a plane ticket to go abroad, or any kind of booking for an in-person event… Employees in the tourism sector have therefore had to cancel or change reservations multiple times. Their job consists in answering customer questions, cancelling the initial booking, sending an email confirming the cancellation, and sending a credit note or refund. In the event of a modified booking, they have to reserve a new time or date and send the customer their new reservation details. These repetitive and time-consuming tasks coincide with a difficult economic period for the industry, which has led to partial or total unemployment.

Devoteam, specialising in innovative technology consulting, UI Path, a robotic process automation company, and dydu have combined their expertise to automate reservation modifications and cancellations by associating a chatbot (dydu technology) with RPA (UI Path technology), integrated to the client’s CRM.

A search for the customer’s booking can therefore be carried out using their name and reference number, entered into the chatbot (or any other relevant identifier). The robot then edits the contract, changing the dates or cancelling the reservation, saves the new contract and sends it by email. The chatbot then informs the customer of an incoming email.

This is an unprecedented solution for the tourism industry, enabling players to respond instantly to the growing number of customer requests and relieve pressure on their operational teams. This tool was initially designed for hotels, but could also apply and be extended to restaurants, airports, transport companies, etc. With the aim of offering an ever more qualitative customer experience, while addressing the industry’s current economic and social constraints.

]]> Dydu Modernises its chatbot software https://uat.dydu.ai/en/dydu-modernises-its-chatbot-software/?utm_source=rss&utm_medium=rss&utm_campaign=dydu-modernises-its-chatbot-software Thu, 22 Oct 2020 12:04:34 +0000 https://uat.dydu.ai/?p=559653
software chatbot redesign editor

Simpler, clearer and more user friendly… The new version of dydu’s Bot Management System, Atria, makes creating and managing conversational robots easier than ever. This update is significant both in terms of ergonomics and technology.

We have completely redesigned our software’s user interface to improve the user experience and make it even easier and more intuitive to access all our features. In addition to this change of design, we have modernised our software, to make it even more powerful and scalable, without compromising security. 

New Ergonomics

The new interface has modernised our software readability, as it is even clearer (design and colours), more intuitive (complete drop-down menus) and more customisable. Bot managers can easily browse through the tool’s features and pin those that they use regularly. The icons and new names also make it easier to navigate and understand the features. 

Bot Management System redesign

Features for Bot Users

Quality of user experience

Your robot is civilised. Thanks to the social knowledge base that we share with all our clients, your bot never gets angry, and is polite and didactic. This 700-knowledge-article base, and the features listed below, were designed to support our conversation algorithm, in order to guarantee the best possible user experience:

  • Customisable welcome features with an on-boarding screen and welcome message, delayed responses (loader or split bubble), chatbox drag & drop for optimal readability whatever the context of use 
  • Enriched content with the possibility to add rich content, images, videos, hypertext links etc. to your answers. You can also use sidebars or the “step actions” feature
  • 10 default languages generated by the bot to choose from (one or more, including French, English, German, Italian, Spanish, Romanian, Portuguese, Dutch, Polish, Finnish)
  • Anticipation of user needs with an information banner and the possibility to highlight top knowledge articles, contextualised or not 
  • Autosuggestion to pre-empt sentence endings, drawing on existing knowledge
  • User guidance to carry out necessary action sequences with behavioural targeting (push rules) or slot filling (for example to book a ticket)

Personalised answers

Users will appreciate your bot’s services all the more if it can provide personalised answers, whether in a customer account space or within the framework of a company app. 

Bot administrators can easily activate certain features to make this possible:

  • Accurate answers, even with different contexts, thanks to the decision trees, use of variables or context conditions
  • Recognition of information spaces requiring differentiated answers
  • Shortcuts to appropriate actions with additional answers – for example, the word “terminate” immediately redirects to an agent
  • Complete personalisation with user authentication – using the OIDC protocol – via the dialogue box (chatbox) and connection to the company’s IS via APIs or web services (CRM, SIRH, etc.)

Features for the Bot Manager

Optimised BMS use for professionals

Bot administrators may have very different jobs and skills, depending on the bot’s use, the industry they work in and their role within the company or organisation. To ensure that everyone who contributes to the bot is completely autonomous and able to work together as best as possible when managing the BMS, dydu offers high-performance features requiring no technical or programming skills:

  • Real-time and continuous visualisation of the rendering of answers (WYSIWYG)
  • Management of access, rights and workflows for anybody contributing to the knowledge base 
  • Mass import / export of the knowledge base to analyse the base as well as the dialogues, or to add a new language
  • Tagging and sharing comments to a knowledge article or dialogue
  • Option to review dialogues as per your filters and manage the history (anonymisation, choice of period, topic, etc.)
  • Storage of images in the gallery that can be immediately accessed

Continuous bot improvement

The algorithm was designed for continuous learning. This is all the more effective if the bot’s manager makes good use of everything the bot learns.

  • Our BMS Analytics module provides a continuous view of the volume of bot traffic and dialogues, their level of quality and user satisfaction via several KPIs (dialogue curves, visual mapping of the knowledge articles that are used the most often, correctly understood and the most satisfactory etc.)  
  • Our supervised learning mode includes lots of features to help the administrator manage and improve the bot’s business knowledge by providing quality alerts, audit and related knowledge article reports or suggestions.

Not all chatbots are the same, especially with regards to the services and experience they provide your users with. Dydu’s BMS, the leader on the French market, enables you – whatever your job (and above all with no developing or coding) – to create, manage and optimise a bot that will best satisfy your users 24/7. 

]]> 10 tips to create editorial content for your chatbot https://uat.dydu.ai/en/10-tips-to-create-editorial-content-for-your-chatbot/?utm_source=rss&utm_medium=rss&utm_campaign=10-tips-to-create-editorial-content-for-your-chatbot Thu, 01 Oct 2020 10:58:46 +0000 https://uat.dydu.ai/?p=547606
conversational robot knowledge base

Tone, text length, wording… Here are 10 good practices to consider before building your chatbot’s knowledge base:

1. Give your chatbot a name, but not just any one

Several studies show that your first and last names can strongly influence the way you are perceived by others. This is also true of your chatbot! Choose a simple and short name that is easy to remember, and in keeping with your avatar. The chatbot’s tone, references and possible analogies should also give the user an idea of its function and make it likeable.

For an internal company chatbot, ask the future users to choose a name. This will encourage them to later adopt the chatbot.

2. Level of formality

Be coherent with your brand. Your chatbot will represent your company via instant messages, so the tone and language should be in keeping with your brand, values and target audience. In addition to the level of formality used, think about the sense of humour, cultural references and expressions you have chosen for the chatbot to converse with the user.

3. Don’t forget introductions

Any good conversation starts with a greeting and introduction. The same applies to a chatbot. Why did you launch this bot? What is its purpose? How can it help users? In the very first messages, the chatbot should briefly but clearly explain its role in order to avoid misunderstandings and therefore user disappointment.

4. Stay focused, 1 idea = 1 message

Large blocks of text are not only difficult to read, they are also very different to the way people exchange in instant messages. A chatbot is a conversational robot and long speeches are not suited to a conversation.

It is therefore important to divide anything the chatbot says into just as many messages, for maximum readability and authenticity.

5. Stay below 300 characters per message

Instant messages can soon seem very long because of the way they are displayed, whether in a chat window or on a mobile phone screen. Make sure that your messages are no longer than 300 characters to encourage users to read them all the way to the end.

6. Avoid monologues

Chatbots are designed to provide a service, give advice, entertain… making politeness paramount. Your chatbot should not appear to be monopolising the conversation, so send no more than 5 messages in a row.

7. Favour a (slight) response time to an instantaneous message

Your chatbot is a machine and therefore performs as such. In other words, this means that your chatbot can answer a request within a millisecond.

To make it closer to its users, and to increase its likeability, add approximately 3 seconds between the user’s question and the chatbot’s answer. This will create the illusion that the bot is reading, thinking and answering like a real person.

8. Don’t only use text content

Once again, your chatbot should base its online communication methods on humans. Images, videos, GIFs and emojis are an integral part of instant message conversations. This kind of content allows to express certain emotions in an original way, often more simply than with text, and to therefore make the conversation more friendly and dynamic.

9. Don’t let the conversation drag on

Ideally, your chatbot should fulfil its objective in 2 minutes. Beyond that, you risk boring the user and causing them to leave prematurely. Your users want answers to their questions, so be direct and precise.

10. Misuse of chatbots – be smarter

No matter what your chatbot is used for, there will always be users who want to test its limits. Off-topic questions, jokes, insults or declarations of love… Forget the usual “I don’t understand” and think out of the box. Humour, originality and anticipation are key to ensure your chatbot is never caught out.

]]> AI and chatbot in the e-commerce industry https://uat.dydu.ai/en/an-ai-and-chatbot-invasion-of-the-e-commerce-industry/?utm_source=rss&utm_medium=rss&utm_campaign=an-ai-and-chatbot-invasion-of-the-e-commerce-industry Wed, 08 Jan 2020 11:50:52 +0000 https://uat.dydu.ai/?p=419597

The 100 billion mark is in sight! Over the last 15 years, the French e-commerce market has not stopped growing. It reached 92.6 billion euros in 2018, a 13.4% increase compared to 2017, despite an unfavourable social context at the end of the year. The number of retail websites shot up by 12.6% in a year to reach nearly 200 000 active online stores. Yet despite the sheer number of players, 90% of the market lies in the hands of just 6% of e-merchants, approximately 10 000 websites (source: ZDNet).

To stand out, the ”small” players focus on innovation and the quality of their customer relationships. They are therefore increasingly turning to automation software to improve their business processes, make predictions, or improve their customer service. This article explores how AI and chatbots allow the e-commerce industry to reinvent itself in order to continue to grow and prepare for the years to come.

AI at the service of e-commerce

  • Effective target marketing

AI is capable of analysing huge quantities of data. Used intelligently, they can complement marketing analytics and improve the brand’s target marketing. These solutions collect customer data (age, sex, location) and their behaviour (pages visited, time spent on website, number of clicks) in order to identify their consumer habits and promote the product(s) best adapted to the customer’s needs.

AI solutions can also help identify tomorrow’s trends. L’Oréal for example analyses thousands of images on social media in order to spot the products and details (colour, patterns) in vogue to add to their product ranges.

  • Optimised logistics

AI may not have reached its full potential yet, but its scope of action within the supply chain is already considerable, whether in the optimisation of order preparations, transport, or stock forecasting. Take the giant Alibaba for example, who uses Cainiao, its logistics subsidiary, to pinpoint the most efficient delivery routes by using vast amounts of data. As such, they have reduced their use of vehicles by 10% and the distances travelled by 30%.

As for stock management, algorithms can be applied to identify early on which products are likely to sell faster or slower than expected.

  • Visual searches

Truly revolutionary for customers – in just a few seconds, they can find the product they want with a photo taken on their smartphone. Fashion, home décor or furniture brands have been quick to adopt this new use, such as La Redoute who added visual recognition to its mobile app in 2017.

  • Customer service and automated conversations

More and more e-commerce sites are equipping themselves with conversational assistants, otherwise known as chatbots. Featuring on their website, mobile app or messenger, these chatbots provide users with information before, during and after their purchase.

The rest of this article will focus on the above, and how chatbots can help improve an online store’s customer service.

Conversational AI for better customer service

Conversational Artificial Intelligence can be defined as a machine (a computer programme), with the ability to hold a conversation with a human at different levels of complexity. Chatbots are one of these conversational AI applications.

  • Chatbots, from virtual assistants to personal shoppers

“The more you tell, the more you sell.” The rise of chatbots and their multiple uses in the e-commerce sector seems to confirm this adage.

Over the last 10 years, chabots have made their way onto most companies’ websites, mobile apps and messaging platforms. They appear in the form of a pop-up window and usually begin by defining their scope (chatbots are not supposed to answer all questions): product recommendations, after-sales queries, logistics questions (cost, delivery times, refund policy, etc.). And when the chatbot does not know the answer, it hands over to a human operator.

Today, chatbots are far more popular and reliable than you may think. According to a recent Ubisend report, 1 in 5 customers consider buying a product or service via a chatbot.

We will now take a look at how chatbots can improve the customer’s e-commerce purchasing experience.

  • Availability and instantaneity

Availability is a chatbot’s best asset! They provide answers in real time, 24/7, a considerable advantage bearing in mind that consumers tend to make their online purchases in the evening, with 19% of sales between 6 and 8pm (source: IFOP).

A question about delivery, a return or refund? A chatbot’s availability and instantaneity is what appeals most to users. 71% of consumers say they would use messaging apps for customer support (Hubspot, 2017), primarily to ensure the swift resolution of their problem. The quality of customer service is one of the main reasons a customer stays, returns or decides to leave an e-commerce site.

In addition to their availability, every chatbot is endowed with its own personality, designed in advance by the brand implementing it (tone of voice, expressions). Chatbots are therefore also their brand’s spokesperson and can inspire user sympathy. Bots are never in a bad mood and can sometimes even make you smile.

  • Personalisation et product recommendation

Beyond their ability to answer user questions about products, terms of purchase or logistics, chatbots can create a range of content that is adapted to each visitor.

Sephora’s messenger chatbot is a perfect example of this. Simply answer a few quick questions and gain access to personalised beauty advice. Providing appropriate and relevant content both satisfies existing customers and converts those who are on the fence. Visitors to a website feel that they are being assisted, thus building a relationship of trust. Chatbots engage the user and encourage them to buy. They can also promote other products that may interest the customer, otherwise known as cross-selling: “if you like… then you will like…”. They can also upsell by offering personalised promotions for each visitor in order to encourage them to make another purchase.

As such, a well-designed chatbot can improve an e-commerce site’s conversion rate, whilst decreasing their bounce rate, one of the biggest challenges faced by online stores.

By asking the right questions, a chatbot can recommend personalised products based on the user’s answers or saved preferences.

LEGO’s chatbot messenger Ralph is a perfect example of this. Ralph asks a series of questions about the person’s age, budget, or favourite topics in order to find the perfect gift.

  • Smooth customer service and cost reductions

Chatbots are useful for buyers and future buyers, but they also strongly benefit the companies implementing them; increase in productivity, cost reductions, and employees who feel valued.

Customer advisors are no longer required to answer the simplest and most frequently asked questions; the bot does it instead. In many cases, a significant drop in the number of inbound contacts can be observed following the implementation of a bot. Lower contact rates mean lower costs. The cost per customer request for an agent is said to average around 6€ in B2C and over 11€ in B2B (Harvard Business Review, 2017).

Automating answers that were once provided by agents does mean that the company wants to replace them. Covering 20% of the most frequently asked questions avoids 80% of enquiries. Chatbots do not therefore need to answer everything to be efficient. In certain cases, a hundred or so knowledge articles are more than enough. The agent works the chatbot and takes over for the more complex queries.

When a brand implements a chatbot, they ensure both their conversational strategy with customers, and their conversion strategy. A well-treated customer, whose expectations are met, is a loyal customer. Research, data analysis, classification, advice… Humans have endowed bots with their intelligence. And this is only the beginning of what bots will be able to do tomorrow.

]]> Natural Language Processing – how does it work? https://uat.dydu.ai/en/natural-language-processing-how-does-it-work/?utm_source=rss&utm_medium=rss&utm_campaign=natural-language-processing-how-does-it-work Thu, 07 Nov 2019 12:48:41 +0000 https://uat.dydu.ai/?p=376599

Since 2016, chatbots are increasingly present on apps and websites, whether commercial or service, and even on intranets. They are equipped with increasingly advanced functionalities; personalisation of responses, context awareness, emotion detection, animated avatars, etc. This helps make the conversation as rounded and natural as possible, to guarantee the very best user experience. Despite a great deal of work on the bot’s personality, design and knowledge, user satisfaction primarily lies in the bot’s capacity to understand and process natural language.

The very essence of natural language processing is to understand the user’s intent in order to provide them with the most appropriate response to their request. In this article, we will explain how this technology works.

What is an NLP algorithm?

In NLP (Natural Language Processing) software, there are three components:

  • A visual interface (dialogue box or chatbox) where the user can interact directly with the chatbot
  • A natural language processing (NLP) engine that uses an algorithm to understand the user’s request (otherwise known as an intent)
  • An admin console (in dydu’s case) that allows to manage all the responses to be provided to the user and to manage the chatbot on a daily basis via performance indicators

An algorithm is a set of operating rules whose application solves a problem. In general, computers are skilled at performing repetitive tasks without ever getting bored. It is therefore possible to describe an algorithm with calculations and computer methods, and for them to repeat the task as often as necessary. The NLP algorithm is a computer programme that has been taught to identify a user’s intent through a series of predefined examples.

How does it work?

NLP chatbot

To understand the user’s intent, the algorithm analyses the sentence’s linguistic structure, by dividing it up into words or compound words.

To understand the meaning of a sentence, each word in its composition must be interpreted. Yet, homonyms and polysemous words make this interpretation particularly complicated.

The NLP algorithm then associates each word with a set of meanings and each meaning is correlated to a “penalty”, according to its occurrence probability. The weight of a word therefore depends on how frequently it is used in the given language.

To interpret a user’s intent, there are several necessary steps:

  • Correct spelling

Spelling mistakes are commonplace in the way users express themselves, particularly on the internet. The algorithm suggests several possible corrections, and each correction is associated with a score.

  • Identify lemmas

For each word, the algorithm searches for the different accessible lemmas. A lemma is the base form of a word without any endings, such as an infinitive verb or a singular noun. Links towards lemmas can be defined for common abbreviations, such as: “asap → as soon as possible”.

  • Identify synonyms and hypernyms

A hypernym is a generalisation of meaning. Hypernyms are essentially used to define a set of products or terms specific to the assistant’s business logic. For example, “dog” and “cat” are not synonyms but “animal” is a hypernym of these two terms.

Synonyms of lemmas as well as hypernyms are identified and associated with the corresponding words. This allows to reduce the number of utterances necessary and to improve the algorithm’s understanding.

  • Calculating the distance between words (distance measuring)

The algorithm directly calculates the distance between the user’s question and reference phrases that already exist in the knowledge base. The closer a user’s question and reference (same words/lemmas, in the same order or not), the higher the score.

  • Obtaining a final score

A user’s question is matched to a reference in the base using what is called a matching score. In dydu’s solution, this score ranges from 0 to 1024. 0 means that the two sentences have nothing in common, 1024 means that both sentences are identical.

The chatbot can provide three possible responses, depending on the matching score:

  • between 800 and 1024, the bot considers that the user’s intent has been understood and immediately provides the response associated with the reference question in the base
  • between 400 and 800, the bot suggests one or more rewordings to confirm the user’s initial question has been correctly understood
  • between 0 and 400, the bot informs the user that the request has not been understood and asks for them to reword it. The bot can also suggest moving over to another communication channel (after several unsuccessful attempts to understand)

What advantages does this technology offer?

dydu’s technology relies on distance calculation, but there are other methods for understanding natural language, such as syntactic analysis and keyword matching:

  • Syntactic analysis focuses on sentence structure and is dependant on the language in which the utterance is expressed (subject-verb-complement in French). This method allows a good understanding of the sentence and to avoid any errors (even when the nuance is subtle), but also relies heavily on grammar, making it difficult to apply to everyday language. 
  • The keyword matching method works in the same way as a search engine. Although the method is simple to implement, it struggles to distinguish between two sentences with very close meanings.

The advantage of the distance calculation method is that it allows a precise understanding of a sentence and does not require the user’s utterances to be grammatically correct. It does however require a learning period for the bot based on the first user questions.

If the bot you are dialoguing with is based on a powerful algorithm and a well-constructed base of intents, then the user will obtain the answers they are looking for, even if they make mistakes, digress, talk about several subjects at once, or do not provide all the necessary information.

If you would like to implement a chabot or vocal conversational robot (voicebot or callbot), it is essential to choose a publisher equipped with a powerful NLP technology, who can also connect to other matching engines to further improve the overall understanding of user intents.

Are you interested in implementing a conversational robot for your clients or employees? Do not hesitate to ask for a demo of the dydu solution.

You can also test our solution free of charge for 14 days.

]]> Zen, your Microsoft work environment expert chatbot https://uat.dydu.ai/en/zen-your-microsoft-work-environment-expert-chatbot/?utm_source=rss&utm_medium=rss&utm_campaign=zen-your-microsoft-work-environment-expert-chatbot Tue, 24 Sep 2019 17:24:18 +0000 https://uat.dydu.ai/?p=347599

Ai³ and dydu launch the first chatbot that supports employees as they become familiar with, and master, their Microsoft work environment. A problem with Outlook? Help needed with Excel? This chatbot is always available to answer employee questions about a number of tools included in the Microsoft suite: PowerPoint, OneNote, Windows 10, OneDrive, Teams, Planner, Forms, SharePoint, Stream, Kaizala, Yammer, etc. 

The benefits of this chatbot are twofold; the employee has immediate access to the information sought, and to a knowledge base regularly updated by Ai3. And the company encourages the use of self-service tools amongst its employees, and thus reduces the number of requests to IT support. The latter, less congested by recurring queries, can therefore focus on resolving more complex and rewarding issues.  

“As a major partner of Microsoft in France, we work on products and services that extensively cover all of a company’s needs,” explains the managing director of Ai³, Xavier Raymond. “With the rise of natural language processing, it seemed logical to us to provide employees, from any company, with a chatbot that supports them when using the collaborative and productivity tools included in the Microsoft suite. To do so, we called upon dydu’s technology, amongst the most advanced on the market, to offer our clients regularly updated and quality content.”

“The biggest challenge when implementing a chatbot is generating the content required to train it,” comments dydu co-founder, Cyril Texier. “The main benefit of this new tool is that Ai³ has a great deal of experience with the Microsoft suite, and has already compiled all of the necessary content, allowing clients to simply deploy the immediately-operational chatbot internally. The knowledge base can also be bought and imported into an existing chatbot.”

]]> Create your own chatbot with the new dydu platform https://uat.dydu.ai/en/create-your-own-chatbot-with-the-new-dydu-platform/?utm_source=rss&utm_medium=rss&utm_campaign=create-your-own-chatbot-with-the-new-dydu-platform Tue, 10 Sep 2019 11:53:43 +0000 https://uat.dydu.ai/?p=334152

Create your own chatbot easily

dydu is launching a new platform to create chatbots. Redesigned from top to bottom to be even more intuitive and easy to use, it allows users to create a chatbot from A to Z in an autonomous manner. Knowledge, intents, entities, dialogue box – everything can be configured so as to quickly meet the end user’s needs.

Whether a small to medium sized company, an artisan, a small shop, an agency, or even a restaurant, you can create your chatbot in just a few clicks and automatically answer your customers’ most frequently asked questions: What are the shops opening times? How can I book a table? What are the delivery times and costs? I’m looking for a tablet, can you help?

No cod, no dev

The dydu platform does not require any technical knowledge or coding skills in order to develop a chatbot. On the first login, a step-by-step guide presents the solution and its features. Each feature has been simplified to make it easier to use. If you are lost, a quick look at our available documentation (getting started, thematic pages, frequent use cases, glossary, etc.) or an email to our support team will allow you unblock the situation quickly.

4 simple steps to create and publish your chatbot

  • Step 1: configuring your chatbot

This first step allows you to define your chatbot’s general settings, such as the languages spoken, the dialogue display format, or the administrator rights if there are several managers.

  • Step 2: creating the questions and answers to which your chatbot will respond

Once the configuration has been completed, you need to supply your chatbot with the content required to respond to your users. User questions that the chatbot needs to understand, also known as intents, and the corresponding answers, are created via a simple text editor. Our intuitive management interface allows to easily define dialogue scenarios using decision trees, and to add context conditions to enrich and personalise the user experience.

Finally, you can connect your chatbot to your company’s internal management software using web services. For example, by connecting to your CRM system, you can enrich your chatbot’s responses with personalised user information. Connecting to a ticketing tool allows to directly open a ticket via the chatbot and automatically send it to your customer support service to be processed.

  • Step 3: Deploying your chatbot

Once the content has been created, choose a design for the dialogue box from the proposed models. Your chatbot is now ready to be published on your website, external messaging platform (Facebook Messenger, Goodle Hangout), or Microsoft bot connector. The features and integration possibilities are regularly enriched to offer users the best possible experience.

  • Step 4: Enriching the chatbot with user dialogues

Now your chatbot is online, you can check its performances using the comprehensive dashboard; number of dialogues, number of visitors, satisfaction rate, most frequently used knowledge articles, link clicks, etc. These real-time analytics allow to manage and continually improve your chatbot as it exchanges with your users. You can also view the failed dialogues in order to add new tags and expand the chatbot’s knowledge.

You can try the solution for free by clicking here.

]]> A bot via telephone? Everything you need to know about callbots https://uat.dydu.ai/en/a-bot-via-telephone-everything-you-need-to-know-about-callbots/?utm_source=rss&utm_medium=rss&utm_campaign=a-bot-via-telephone-everything-you-need-to-know-about-callbots Tue, 06 Aug 2019 16:10:07 +0000 https://uat.dydu.ai/?p=269008

You may already have heard of callbots, new conversational robots that can be reached via telephone. Recently deployed in companies facing a high volume of calls (insurance, telephony, transport, e-commerce, hospitality…), they automatically answer customers or future customers’ questions and requests. They also qualify these requests and can transfer them to skilled advisers, as per the operational strategy in place.

These new bots replace Interactive Vocal Response (IVR) systems, where the user presses buttons on their keypad to reach the appropriate service: “To speak to a sales representative, please press 1. To reach our customer service, please press 2. To be connected to an agent, please press 3…”

Unlike IVRs, callbots dialogue with users in natural language. Read on to find out how.

How do they work?

Like chatbots, callbots function with a Natural Language Processing (NLP) algorithm. This algorithm makes it possible to render the conversation as natural as possible, thanks to the management of digressions, “small talk” and personalised responses, according to context and user history.

For a chatbot to become a callbot, two extra steps need to be added:

  • Voice recognition (Speech-To-Text), the input which allows to capture the request and analyse it
  • Voice synthesis (Text-To-Speech), the output which allows to deliver the requested information vocally to the user

Callbots are also connected to the company’s various web services (IS, CRM and ticketing platforms), to personalise the responses and trigger actions such as escalation to an adviser or sending a confirmation email/text message.

Extra work is required in comparison to setting up a chatbot; voice design! A voice-user interface is very different to a text one, and the bot needs to be adapted accordingly. Tone of voice, nuances, interruptions… In order for the conversation to be as natural as possible, the callbot must adapt to the user.

To find out more, read our article about conversational design.

Why should you implement a callbot?

By implementing a telephone conversational robot, you can meet the customer, adviser and company’s needs all at once:

And what about humans?

Like any other chatbot, callbots are not destined to replace advisers, but to work alongside them. The human role is essential, the bot acts as a complement to their work and therefore allows them to focus on more important tasks.

At the moment, whether textual or vocal, bots are unable to manage all the tasks performed by man. As soon as the conversation becomes more complex, the presence of an agent is essential to ensure a good customer experience.

And even if technologies are evolving rapidly and use cases multiplying, it is not in a company’s interest to let robots manage their entire customer relationship.

MACSF case study

MASCF, an insurance company for health professionals, implemented a callbot in December 2018 in order to extend the opening hours of its customer service and pre-qualify incoming requests. The aim? To improve the experience of (future) policy holders, increase sales and make the processing of requests smoother.

Thanks to the callbot, named Marion, if a user reports a claim in the evening or at night, they are automatically called back the next day to confirm, saving a considerable amount of time for both the insurer and policy holder. Users can also take out new car insurance via the callbot, effective immediately. At the end of the conversation, the bot sends the user confirmation by text message and integrates their request into the IS so that it can be processed by the correct management service.

During the very first month following the launch, Marion answered 2000 calls with an 89% intent recognition rate. The next steps will be to optimise the existing use cases, and to add new use cases to implement a permanent robot (working and non-working hours).

Last April, MACSF won the “Argus d’or” for its callbot, a prize rewarding the best innovation initiatives in the insurance industry.

If you would like to know more about our dydu callbots, please don’t hesitate to ask for a demo.

]]> Voicebots – simple gadgets or future technological revolution? https://uat.dydu.ai/en/voicebots-simple-gadgets-or-future-technological-revolution/?utm_source=rss&utm_medium=rss&utm_campaign=voicebots-simple-gadgets-or-future-technological-revolution Mon, 13 May 2019 15:59:43 +0000 https://uat.dydu.ai/?p=121260

What exactly is a voicebot?

If you missed the media frenzy surrounding voicebots, here’s a quick catch-up; a voicebot is a vocal conversational robot. They are equipped with the same capabilities as chatbots, meaning an understanding of natural language and the ability to respond to users automatically. The only difference lies in the interaction interface, which in this case is vocal. The most famous voicebots include Google Assistant, Alexa, or even Siri.

Some of these vocal assistants can be found in recent car models, smartphones or even some connected speakers such as Google Home, Amazon Echo or HomePod. In terms of innovation, they are the very latest, must-have technologies, that companies dream of acquiring in order to offer a unique and out of the ordinary user experience.

Voicebots, simple gadgets or future technological revolution?

Despite an increase in vocal applications and their different uses, user satisfaction is not always guaranteed. As such, many of us still shy away from using these conversational robots in our everyday lives, primarily for the following reasons:

  • They require behavioural change; not everyone is comfortable talking to an object. Just look at the number of people who use the vocal assistant on their telephone (do you?)
  • We don’t know what to ask. Or how to go about it. What can they understand? The experience is disappointing when the voicebot doesn’t know how to respond (even more so than with a chatbot!)
  • We can’t have a “real” conversation with them. They respond to simple vocal commands such as “Switch the light on”, “Turn the music on”, or common questions such as “What’s the weather like?”, but you cannot enter into an actual dialogue with them
  • We don’t always understand their utility
  • We don’t trust them (am I being listened to? what happens to all my data?)

So, with this in mind, why are voicebots expected to be a revolution in years to come?

Voicebots, tomorrow’s must-have technology

> They free up your hands

Voicebots allow users to multitask, enabling them to complete an action without having access to the control interface in question. Vocal command is particularly useful in cars, because it allows the user to drive whilst using their smartphone or changing their vehicle configuration.

They are well suited to other everyday situations; who has never covered their smartphone with flour in the kitchen whilst trying to read the next part of a recipe? When it would be so much easier to say out loud, “What’s the next step?”.

> They facilitate accessibility

Vocal assistance makes technology accessible to everyone; in particular young children, old people, people with disabilities, or even illiterate people. By simply using their voice, a multitude of actions and information become readily available to them.

> They personalise

Voice is a strong marker of a person or brand’s identity. For a company, it is a factor of differentiation and personification. Just as you would choose your logo, slogan or visual identity, you choose a voice for your brand and Voicebot. It should be easily identifiable by its tone, character and personality. Who in France wouldn’t immediately recognise Simone, the SNCF train service’s voice? Voice is becoming a major topic of consideration for communication and marketing teams.

> They ‘re-enchant’ the user experience

Users are more and more demanding; they want novelty, new experiences, sensations and emotion. Which is precisely what voicebots have to offer. If the user experience is thought out and built with the right users in mind, then the final result will be very impressive. This is the case for insurance company MACSF’s telephone voicebot, for example. Deployed for their beneficiaries, it won the Argus prize that rewards innovation initiatives in the insurance industry.

The stakes are just as high as the risk of a disappointing experience. But when the voice design concepts are respected, and the instances of use and processes well built, then voicebots are the easiest and quickest ways for users to access your services.

A few Voice Design best practices

Creating a good voicebot is not trivial. A design phase specifically for the use of voice is required, known as Voice Design, in order to produce a Voice User Interface (VUI) worthy of that name. To guarantee a unique vocal experience, certain principles must be respected:

  • Guide users interacting with the voicebot, on what they can and cannot do, but also when the bot does not understand something. “Sorry, I can only help you in the kitchen” will always be better received than an abrupt “I don’t understand”
  • Take the context of the conversation into account (user profile, time, location, voicebot conditions of use, etc.), in order to enrich the dialogue with relevant suggestions
  • Be brief and concise; a user’s time is precious. Avoid long monologues and leave pauses for the users to express themselves too
  • Give the voicebot a personality to increase its emotional impact (and therefore the brand’s)
  • Make the conversation feel human; tone of voice, nuance, interruptions, human expressions (“hmm, yes I see”, ”perfect”, “noted”)
  • Divide up the construction of the voicebot to improve the VUI progressively
  • Favour sentences and questions leading to precise answers; “Please specify if you would like to book a hotel or a taxi?” would work better than “Would you like to book a hotel or a taxi?”

The list is long and non-exhaustive. Each voicebot is unique, and therefore needs to be adapted, whenever possible, to each instance of use.

So, will Voicebots replace Chatbots?

Speaking is 3 times faster than typing on a keyboard. Speech is mastered well before writing and is our most natural form of communication. Since a UX without visuals frees the user from interfaces, many believe that this will become our new standard.

That is not to say that Chatbots have signed their death warrant. On the contrary, their use can be complementary to Voicebots, depending on user preferences and the conditions of use. For example, a chatbot will be favoured in noisy or public places for discretion and confidentiality reasons. They still have a bright future ahead!

]]> Conversational design for an improved user experience https://uat.dydu.ai/en/conversational-design-for-an-improved-user-experience/?utm_source=rss&utm_medium=rss&utm_campaign=conversational-design-for-an-improved-user-experience Thu, 07 Mar 2019 09:19:04 +0000 https://uat.dydu.ai/?p=56305

The rise of chatbots and other conversational robots is nothing new. In 2015, companies began deploying them massively on their websites or mobile apps, in order to relieve pressure on their call centres.

Since then, their uses have multiplied; product recommendation, employee support (IT, HR, helpdesk, legal), welcoming visitors, home automation… Today, numerous companies are equipped with one or more bots, and are always exploring new ways to use them.

Far more than just a passing trend, these virtual assistants have real advantages for both companies and end users; rapidly accessible information, reduced wait times, a smoother processing of requests, reduced contact volume… Yet, sometimes users are disappointed; misunderstandings, poor suggestions, repetitions… Some bots clearly don’t live up to our expectations.

To avoid user frustration, it is essential to work on your bot’s conversational design. Read on to find out how.

Spending time on conversational design to encourage the adoption of your bot

Conversational design includes all the steps necessary to making interactions between a human and a machine as smooth and efficient as possible. To create your User Interface (UI), you must first define the User Experience (UX).

UX takes all of your end users’ needs into consideration, to offer them the best possible experience. Your bot should adapt to them, and not the other way round. UI is the experience’s visible medium, a direct link between the user and product. A good UX/UI makes all the difference.

Understanding your users and their needs

The first step when creating a chatbot is to put yourself in your end users’ shoes by creating personas. In other words, identifying your target and establishing their personality, habits, motivations and frustrations. Next define all the possible expectations from these personas. Only after this step can you start to create a bot’s knowledge base.

Define your chatbot’s personality

Name, avatar, conversational style, tone… Everything should be thought out with your end users’ profile in mind, sticking as closely as possible to your company’s brand image. As such, a chatbot can be funny, friendly or more formal.

Knowing the context

Your bot should be able to contextualise user intent and bounce back with suggestions that make sense according to the user’s profile and the bot’s use. You can also inject your bot with “emotions”, via an animated avatar or emojis. This will make it appear more human and friendly, and users will enjoy chatting with it!

Guiding your users

When starting a conversation, your bot should first and foremost introduce itself and then specify the perimeter within which it operates, to avoid any frustrations. The bot is not supposed to have an answer for everything! The conversation could start for example by, “Hello, I’m Leo, your virtual adviser. I’m here to answer any questions you may have about tax deducted at source”.

The chatbot should also guide your users during a dialogue, through the misunderstandings and potential actions and requests. Should an ambiguous situation arise, the bot should be able to specify the user’s intent and transfer them to a human when unable to answer. All of these different scenarios are defined right at the beginning of the project and integrated into the bot’s back-office to be able to handle 80% of end user interactions.

Generating smooth conversations

To guarantee the best possible experience, your bot must be able to generate a smooth conversation, as natural as possible. Slot filling can be an interesting functionality to use here; it enables the bot to gather information or elements throughout the conversation, without necessarily following a question/answer sequence, that are needed to provide a response to a user’s intent.

For example, to respond to the intent “I would like to order a pizza”, you can configure a “toppings” slot and a “number of people” slot. If the user says:

  • “I would like to order a pizza”: the bot asks the user to specify which toppings and for how many people
  • “I would like to order a cheese pizza”: the algorithm fills in the “toppings” slot with “cheese” and the bot asks the user to specify for how many people
  • “I would like to order a cheese pizza for 4 people” OR “I would like to order a pizza for 4 people with cheese”: the algorithm fills in the “toppings” slot with “cheese”, the “number of people” slot with 4, and the bot can continue with the order

And what about voice?

You have probably already exchanged with a vocal assistant, which is hardly surprising given that they are a hot topic of discussion at the moment and have invaded our everyday lives; on our smartphones with Siri or Cortana, via connected speakers like Google Home and Amazon Echo, or other connected devices. Gartner estimates that 20% of interactions will occur via vocal assistants in 2019. A figure that makes sense when you consider that talking is 3 times quicker than typing on a keyboard.

Voicebots are based on the same technology as NLP (Natural Language Processing) and endowed with the same capacities as chatbots, the only difference being that their interactions are vocal. They do however also include two extra steps: voice recognition (Speech-To-Text) and vocal synthesis (Text-To-Speech).

Voice design, an essential step

To create a voicebot or a callbot (a voicebot via telephone), you can follow the same steps as for a chatbot.

Interactions between humans and robots are nevertheless different because the user interacts using speech instead of writing. The choice of words, syntax, utterances and expressions are thus all the more important. In this step, it is essential to understand the difference between talking and writing before creating your bot’s knowledge articles and the related decision trees.

The keys to a good Voice UI, according to dydu:

  • Choosing the right voice
  • Being as natural as possible: tone, nuances, interruptions
  • Keeping it short and concise: the user’s time is precious
  • Being cooperative and empathetic
  • Making suggestions
  • Varying words and expressions to avoid repetitive sentences
  • Using and understanding common words and human expressions: “Hmm, right, I see…”, “Perfect”, “Noted, thanks”
  • Testing, retesting and re-retesting the bot ?

Whether used alone, or in addition to text and images, speech can truly offer a rich user experience, as long as you respect the best practices when it comes to voice design.

]]> dydu.lab explores new conversational applications https://uat.dydu.ai/en/dydu-lab-draws-new-horizons-for-our-nlp-solution/?utm_source=rss&utm_medium=rss&utm_campaign=dydu-lab-draws-new-horizons-for-our-nlp-solution https://uat.dydu.ai/en/dydu-lab-draws-new-horizons-for-our-nlp-solution/#respond Tue, 18 Dec 2018 16:45:44 +0000 https://uat.dydu.ai/?p=1240
chatbot NLP Lab

Like febrile but happy parents, we are proud to announce the birth of dydu.lab, the dydu research laboratory, a trend-setter and bearer of future technological innovations.

dydu.lab’s mission is to set up innovative and futuristic projects, which focus on prospection and exploration. As experts, we want to stay one step ahead of our competitors, and prove to our customers that we are still at the forefront of technology, even after 10 years of intense R&D.

Our core business is conversation: understanding a question to provide the best answer, immediately. So far, mainly confined to text, we want to open our chakras to add new interfaces to interact with our Natural Language Processing software.

With our partners and customers, we produce Proof Of Concept (POC) to assess the feasibility of our projects in terms of conversational assistance and artificial intelligence. There is no doubt these projects will lead to new products and services, our future growth drivers!

dydu.lab isconvinced to be a true participative innovation hub. It will be a space dedicated to the experimentation, prototyping and development of new innovative projects.

Speaking of which, we are announcing new products : Callbot, Voicebot, Humanoid Robot and Domobot. We are very concerned to see them grow up in the best possible conditions ?

So this is the mission that dydu.lab has set itself for the coming years.

By the way, if you are interested in exploring a new subject with us, we are always keen to hear your suggestions and ideas.

]]> https://uat.dydu.ai/en/dydu-lab-draws-new-horizons-for-our-nlp-solution/feed/ 0 All about the Alliance for Open Chatbot https://uat.dydu.ai/en/all-about-the-alliance-for-open-chatbot/?utm_source=rss&utm_medium=rss&utm_campaign=all-about-the-alliance-for-open-chatbot Mon, 12 Nov 2018 10:16:42 +0000 https://uat.dydu.ai/?p=28387

dydu has joined forces with Kynapse, Kwalys, Synapse and Living Actor, all leading players in the chatbot industry, in order to create an alliance to make companies’ chatbots mutually compatible.

This alliance, in the form of an association (law 1901), is based on the conception and development of an open source interface, allowing chatbots created by different solution providers to communicate with each other.

Incompatible Chatbots

The keen interest in conversational robots within companies has led to an increase of industry players. Each solution provider uses its own technology to address specific needs, leading to the creation of different chatbots depending on the platforms, devices or uses they are intended for.

As such, they are unable to consult one another to provide the best answer, multiplying entry points for users.

Orchestrating chatbots with a metabot

In light of this, dydu has combined its skills and expertise with other industry players, in order to offer a simple solution, by creating a “metabot” able to orchestrate several chatbots, and by defining a recommended interface (API) to be implemented by software publishers.

“Our vision is to provide a much-needed solution to current challenges faced by companies who need to deploy new conversational agents, but do not wish to start from scratch every time. For example, if a chatbot can answer questions about the company, all of the other ones should benefit from this same knowledge. We have been working on this project for over 10 months now in order to bring together initiatives and provide a simple, open source solution.” the founding members.

This standard also allows different interconnected conversational agents to exchange data and to benefit from the other chatbots’ features, for an improved collective intelligence. The Alliance’s work is published as “open source”, as a free and collaborative tool to be used by all.

The Alliance’s ambition is to take this initiative to a European and international level.

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