You see them on more and more websites and social networks. In the form of animated avatars or simple dialog boxes, chatbots are everywhere on the internet. They help you solve a technical problem, to order a product or to direct you to the service adapted to your needs. It is also on this technology that virtual assistants like Apple Siri, Google Assistant, and Microsoft Cortana are based.
The first chatbot, Eliza, was created by MIT in the 1960s. Conversational agents are booming today thanks to the many technological advances that have taken place in recent years.
The increase in computing power, the progress made in the field of artificial intelligence, language analysis, and Machine Learning, are among the innovations that have allowed chatbots to become more efficient. However, these robots dedicated to natural communication are also closely linked to Big Data.
Chatbot and Big Data: How Conversational Agents Collect Big Data
The primary role of a chatbot is to respond to a client’s request. However, interactions with users are also sources of data that the chatbot collects and stores in a database.
Recall that big data or big data are defined by three characteristics, the 3V: volume, velocity, and variety. However, the data generated by conversations between chatbot and surfer fulfill these three conditions.
In terms of volume, a current chatbot can hold, for example, over 30,000 conversations a month. Velocity is provided by the extreme speed at which queries are processed and categorized by these robots agents. Finally, variety is guaranteed by the variety of queries and terms used by users.
Chatbot and Big Data: How Conversational Agents Analyze Big Data
The operation of chatbots is based on data analysis. Thus, when a user sends him a message, the chatbot does not really understand what he says or asks him in the same way as a human.
To understand a query, the chatbot uses pattern recognition. He will recognize certain terms or groups of words in order to deliver a response associated with these terms.
The most advanced conversational agents are also able to analyze a user’s way of speaking and associate a feeling with it. This is the technique of “sentiment analysis”. This allows companies, for example, to quickly detect a customer’s frustration in order to solve their problems in priority.
Chatbot and Big Data: How Companies Leverage Chatbots’ Data
Until recently, the main sources of customer data that companies could use for personalized marketing were e-mails or social interactions. Today, however, data collected and analyzed by chatbots is of great value to businesses.
With natural language processing and demographic analysis, this data can, for example, be used to detect trends and develop personalized messages using the same language as the target.
It is also possible to analyze the frequency of certain problems to determine which products and services are the most problematic. Adequate measures can be taken.
In addition, this data can also be exploited to create a recommendation engine. This involves combining the data collected by the chatbot with the predictive analytics technique in order to offer each customer products and services that meet their needs.
Chatbot and Big Data: how chatbots improve with the data they collect
The chatbots themselves can be improved with the data they collect. This data can be used to re-feed the deep learning algorithms on which chatbots are based in order to increase their intelligence.
For example, by reviewing the questions that a chatbot was unable to 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 or her feeling analysis abilities improve. It becomes an extension capable of collecting even more data. This virtuous cycle is made possible by Machine Learning and Big Data.
As you can see, Big Data is at the heart of chatbots and chatbots themselves generate valuable data. Over the next few years, conversational agents will continue to improve until it is impossible to differentiate them from a human interlocutor. They can, therefore, be expected to extend to more areas of application.
