In fact, this definition fully coincides with an Amazon practice, this time as a global product sales website, which we all have experienced: under the article we are about to buy, the section introduced by the phrase “Customers also shopped for”, which provides other articles of possible interest. The introductory phrase of this section perfectly reflects the procedure carried out: Amazon collects millions of behaviors of its users in terms of purchasing processes, analyzes them in order to predict the future behavior of their customers, and with high probability manages to increase sales on the web.
There is no doubt that this methodology turns out to be an extremely powerful weapon when it comes to doing directed marketing, forecasting demand or of course turning visits to the web into sales. But what happens if the same strategy is applied to customer support?
The Chatbot is nothing else than an additional channel to solve doubts about airline tickets, check contractual aspects of a bank account, download manuals of household appliances, consult data of a hotel reservation or the balance of a telephone line … The knowledge about the company provided through the Chatbot must not differ from the rest of the website or from the other customer service channels, whether these channels are automated or managed by human agents.
A wrong answer, especially in reply to a delicate question, could have disastrous consequences. Imagine that a chatbot providing support to the policyholders on the website of an insurance company, in reply to a question about breast leads the user to answers about plastic surgery, because they are more common according to Machine Learning, instead of offering one or more answers about pathologies, gynecology, etc. The image of this insurance company would be damaged due to the low reliability of the Chatbot, more precisely due to the misuse of a predictive methodology, such as Machine Learning, with the purpose of customer care.
The alternative that remains is to create data manually, as if they were traces collected from users, to “train” an ad-hoc Machine Learning model. This training process is highly promoted by numerous chatbot providers, to the point that it has created the false belief that the only way to implement a chatbot is to train the system manually, for months, with as many utterances as possible — considering as utterances the many different ways of expressing the same intention. This myth is fed by those who do not have Natural Language Processing technology to relieve the lack of previous data. Likewise, the manual creation of data to fit with Machine Learning models implies a colossal effort for companies, an effort which translates into both a prolongation of the project’s time-to-market and disenchantment with Artificial Intelligence.
On the contrary, a provider capable of using the Natural Language allows solving the two problems that Machine Learning entails for a Chatbot customer service: on the one hand, it does not require extensive volumes of data, on the other it is based on linguistic engineering for offer semantically relevant, contextualized and highly reliable answers.
Betting on Natural Language Processing, on the other hand, allows to guarantee the reliability of the Chatbot, thus the reliability of the company, as well as favoring an agile time-to-market and trust towards Artificial Intelligence.
Looking at this panorama, the ideal approach could be to bring together the best of each world: fully rely on the Natural Language Processing as the main technology of the Chatbot for customer support, and at the same time resort to some features of Machine Learning to complete certain purposes, such as analyzing customer interactions to suggest new contents for the knowledge base, or using clickthrough statistics to adapt the interface and improve the user experience.