Chatbot and Big Data: agents depend on data

Chatbot and Big Data are two cutting-edge technologies that are inextricably linked. Find out how conversational agents collect, analyze, and exploit megadata.

You see them on more and more websites and social networks. Whether in the form of animated avatars or simple dialog boxes, chatbots are everywhere on the internet. They are capable of to answer questionsWe can help you solve a technical problem, order a product, or direct you to the appropriate service for your needs.

Sculpin definition

It is also the technology on which virtual assistants such as Siri, Google Assistant and Microsoft Cortana. For many experts, chatbots are even destined to completely replace mobile applications.

The first chatbot, Eliza, was created by MIT in the 1960s. If conversational agents are experiencing a meteoric rise today, it is thanks to the many technological advances that have taken place in recent years.

Increased computing power, advances in artificial intelligence, language analysis, and Machine Learning are among the innovations that have enabled chatbots to become more efficient. However, these robots dedicated to natural communication are also closely linked to Big Data. Discover the links between chatbots and megadata.

Chatbot and Big Data: How Conversational Agents Collect Megadata

chatbot big data collection

The primary role of a chatbot is to respond to a customer’s request. However, interactions with users are also sources of data. that the chatbot collects and stores in a database.

Remember that Megadata or Big Data is defined by three characteristics, the 3V: volume, velocity and variety. However, the data generated by the conversations between chatbot and Internet user meet these three conditions.

In terms of volume, the chatbot Nina from the Bank of Sweden has for example 30,000 conversations per month. These robotic agents process the elements at extreme speed. Finally, users use a wide variety of terms and are interested in many search topics.

Chatbot and Big Data: How Conversational Agents Analyze Megadata

chatbot analysis

The chatbots are based on data analysis. Thus, when an Internet user sends a message, the chatbot does not really understand what he is saying or asking him in the same way as a human.

To understand a request, the chatbot uses pattern recognition. It will recognize certain terms or groups of words in order to deliver a response associated with those terms.

The most advanced conversational agents are also able to analyze a user’s way of talking and associate a feeling with it. These are the “sentiment analysis” technique. . This allows companies to quickly detect a customer’s frustration in order to solve problems as a priority and improve customer support.

Chatbot and Big Data: how companies use chatbot data

operating chatbot

Until recently, the main sources of customer data that companies can use for personalized marketing were e-mails or social network interactions. Today, however, the data collected and analysed by the chatbots is of great value for business.

Through natural language processing and demographic analysis, these data can, for example, be used to detect trends and develop personalized messages using the same language as the target.

It is also possible analyse the frequency of certain problems reported to customer service to determine which products and services are of most concern. The appropriate action can then be taken.

In addition, this data may also be used to create a recommendation engine. This involves combining the data collected by the chatbot with the technique of predictive analysis in order to offer each customer products and services that meet their needs. In general, chatbots improve the customer experience.

Chatbot Messenger

Chatbots can also be used to communicate with customers via mobile applications, such as messaging applications. For example, at its F8 2016 developer conference, Facebook launched support for chat bots for its Messenger platform. Other messaging applications such as Telegram and Slack also support chat bots.

This allows companies to simplify communication with their customers by being accessible on a messaging system that they use on a daily basis. This improves and strengthens customer relations. In May 2018, Facebook revealed that there are now 300,000 active chatbots on its Messenger platform and that 8 billion messages have been exchanged between companies and their customers.

Many of renowned companies use Facebook Messenger chatbots. Examples include Whole Foods, Sephora, Wall Street Journal, Pizza Hut, Mastercard, Lyft, Spotify and Skyscanner.

Others implement solutions developed by startups. Illuin Technology provides ChronoPost with its chatbot which automates the answers. It carries out 12,000 messages per day. This conversational agent named Leonard responds to the explosion of parcel orders. Indeed, the success of e-commerce multiplies the number of after-sales service queries. According to Usine Digitale, Leonard has initiated 5 million conversations in 10 months.

Chatbot and Big Data: how chatbots improve with the data they collect

conversational enhancer

The chatbots themselves can be improved by the data they collect. This data can be used to re-feed the deep learning algorithms on which the chatbots rely to increase their intelligence.

For example, by reviewing questions a chatbot couldn’t answer and the words used by the client, developers can assign new responses to these queries.

Similarly, as the chatbot tries to assign feelings to a user’s words, his abilities to analyze feelings are improving.. It becomes by extension capable of collecting even more data. This virtuous cycle is made possible by the Machine Learning and Big Data.

As you can see, Big Data is at the heart of the chatbots’ operation and the chatbots themselves generate valuable data. Over the next few years, chatbots will continue to improve… until it’s impossible to tell them apart from a human interlocutor…. It can therefore be expected that they will be extended to more areas of application.

How to create a chatbot with Big Data?

chatbot creation

In reality, the chatbots’ very functioning relies on Big Data. Yes, it does, a chatbot is created is trained from data sets.

The method of data handling depends on the type of chatbot. For example, a scripted robot can only offer a limited set of functions and can only handle certain specific questions.

On the other hand, thanks to the Machine Learning, chatbots can acquire superior knowledge and understanding skills examining examples of past conversations. Even the most advanced chatbots can watch live conversations to gain new knowledge.

How does a Chatbot work?

The data required for their operation may come from a variety of sources. These may include customer service queries, information stored on the company’s FAQ page, call logs, or open data sets made available by governments or other organizations.

The data are then converted into a structured form. from which chatbots can take. It is possible to develop the chatbot using a decision tree, but it will be much simpler and more effective to use Natural Language Processing (NLP), Natural Language Understanding (NLU) and Machine Learning technologies.

All you have to do is provide data sets to the robots for training purposes before they’re deployed to real-world situations. For example, the NLP can use a declarative approach to allow chatbots to recognize intentions and entities. Large amounts of example sentences will then be used to tell the robot which terms are important in a conversation and what the user wants.

Then, once the chatbot is deployed, it will be possible to analyze the conversions to detect the sentences it has trouble analyzing. We will then be able to opt for a “supervised” Machine Learning approach This consists of manually adding new example sentences to help the chatbot improve his main weak points.

Facebook and Stanford researchers create a chatbot that can learn from its mistakes

stanford facebook

In an article published in January 2019 on Arxiv.org, entitled “Learning from Dialogue after Deployment: Feed Yourself, Chatbot! “scientists from Facebook AI Research and Stanford University describe a chatbot capable of improving independently by extracting data from his conversations.

Thus, when a conversation goes well, the user’s answers become new examples of training to be imitated. On the contrary, when the agent thinks he has made a mistake, he asks for feedback. At learning to predict feedback, the chatbot improves its dialogue skills.

The chatbot trains using only natural responses that do not require no special structure or human intervention. The AI will therefore be able to adapt continuously without even needing human supervision.

The problem of unsupervised learning

The only downside: by letting the catbot train alone, it is possible that he reinforces his mistakes. This could lead to absurd conversations. To remedy this problem, researchers have imagined a system. It allows the collection of a set of data measuring satisfaction of the interlocutors.

To develop this dataset, the researchers asked contract workers to discuss with the RN Officer and assign a mark between 1 and 5 for the quality of each of his answers. This score is used to teach the chatbot to predict whether human responses are “satisfied” or “dissatisfied” with his or her actions.

By interacting with humans, the chatbot can practice both the dialogue (what he will say next) and the feedback (the consistency of his answers). It generates its responses based on previous exchanges. If the level of satisfaction of the interlocutor reaches a certain threshold, it extracts training data. If the level is too low, the robot asks for feedback and uses the response to create a new example of feedback prediction.

Transform, a conversational agent for customer satisfaction

In total, during this research, scientists fed the chatbot based on Transform neural architecture with 131,428 dialogue examples between humans from the PersonaChat dataset. These examples allowed the agent to increase its accuracy to 31%. His ability to predict caller satisfaction also increased significantly, even using only 1,000 examples.

This experience shows that it is possible to improve the dialogue capabilities of an AI imitating human responses when the speaker is satisfied, or asking for feedback when the speaker is not satisfied. Furthermore, it demonstrates that classifying user satisfaction is an important task to learn for the self-feeding process.

Perhaps this study will help to making future chatbots more conversational.. In the meantime, the datasets, models and training code described in this research can be found on Facebook’s ParlAI platform.

Top of the best platforms to create a chatbot

Machine Learning is a complex technology. It is not prohibitive. You should know that there are many tools to create chatbots without technical knowledge in this matter. Each of these platforms takes a different approach, but all of them allow to create powerful chatbots in an intuitive and fast way. If you want to create a conversational agent for your company, we recommend that you use one of these tools.

Chatfuel

With Chatfuel, you can easily create a chatbot for Faebook Messenger. You won’t need to know how to code to use it. The message management and reuse features are also well developed.

In addition, the free version provides access to almost all features with a limit of 5000 users. However, users will see the Chatfuel logo. The paid version is available from $15 per month. It offers more insights, priority customer service and additional data management features.

ManyChat

ManyChat is one of Chatfuel’s main competitors. The two platforms have a lot in common. Again, you won’t need any computer code skills to use it. In fact, ManyChat is easier to use than Chatfuel.

However, there is a lack of certain message management functionalities. In addition, the free version is very limited since you can only have 500 users. The paid version starts at $15 per month for 1000 users, and $45 per month for 5000 users. In terms of data management and analysis features, and custom experience creation, ManyChat is far superior to Chatfuel.

Flow XO

Floz XO allows you to create chatbots, host them and deploy them on a large number of platforms. Thus, this software is not limited to Facebook Messenger. It is even possible to create chatbots widgets for a website or to integrate them with suitable third party platforms. Users can also share your bots.

The interface is very easy to use. The free trial version allows you to discover all the featureswithin a limit of 500 interactions. The paid version costs $19 per month for 5,000 interactions, and $25 per month for 30,000 interactions.

Botsify

https://www.youtube.com/watch?v=Y7JqZNH6xm4

Botsify allows you to easily create chatbots for a website or for Facebook Messenger. In addition, there are very practical integration features that set it apart from the competition.

For example, it is possible to integrate the Botsify chatbots with Shopitfy, WordPress or Alexa. A feature also allows a human employee to join a conversation if needed. You can try Botsify for free, or opt for the paid version which starts from $10 per month.

ChatterOn: an answer to the question how to make a chatbot

ChatterOn presents itself as the fastest bot builder on the marketand allows you to create chatbots in less than five minutes. To get started, the editor offers 20 prefabricated chatbots. It is then possible to choose one and customize it using a very simple interface.

It is possible to integrate carousels, buttons, photos, gifs and chatbots videos. In addition, contextual ads offered by Radbots allow you to monetize the chatbots. In its free version, the platform limits the number of messages sent to 15,000 per month. Thereafter, each message costs $0.0010.

There are a large number of other chatbots creation platforms. We can notably mention Pandorabots, Sequel, TARS, Wit.ai, or Botkit. All these platforms will allow you to take advantage of chatbots technology without the need for programming skills. Choose the one whose features and interface match your needs and preferences.

Chatbot and safety: not a matter of course

Just like the rest of a company’s infrastructure, chatbots need to be secure. To do this, it is necessary to ensure that scripts are correctly deployed and that the data they process is encrypted. Implementing multi-factor authentication reduces the risk factor. A hacker will find it more difficult to impersonate a user and thus steal data. Furthermore, this encryption can be achieved by hashing information. Then, it is stored in various places in the company to avoid having to put “all his marbles in the same basket”.

There have already been several cases of Chatbot hacking. TicketMaster suffered the consequences in 2018 after a poor deployment of the MageCart product in the United Kingdom. As a result, hackers stole 40,000 credit cards from international customers.

A year before, a hacker had hacked into the website of Delta, an American airline. She had installed the publisher’s chatbot… [24]7 whose computers had been infiltrated. For 15 days, the cybercriminal could consult the addresses, names, and banking information of 825,000 customers in the United States. The passwords used to protect these machines proved to be too weak. Delta is now suing its service provider. The entire chain must be secured to prevent this type of event.

The French don’t trust conversational agents.

Truspilot conducted a study of 1,800 people representative of the French population. The result was that 57.6% of those surveyed did not trust chatbots. Nearly 20% of them stop any online purchase project when they have to go through a chat agent. Moreover, one customer in three is not satisfied with their exchanges with such a tool. For 53.6% of them, the absence of human factors is unacceptable. Finally, 57% of respondents fear that these robots will replace humans.

According to Gartner, 25% of customer services will be using chatbots by 2020. In France, people prefer to be helped by a human, whether on the phone or in front of a computer. Adoption will probably take longer in this country. To encourage the advent of this technology, suppliers must improve the quality of responses.

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