Why Chatbots are an integral part of e-commerce companies

E-commerce has evolved to the point where AI and automation works alongside intelligent humans to deliver a WOW factor. This was especially the case for companies that go the extra mile with R&D, until now, where cost barriers to technology literally got scrapped, thanks to things like the chatbot marketplace or “bot store”as our users know it.

Today I will share with you why chatbots are so vital to e-commerce companies:

The need for better customer service

At this point in time, there are many e-commerce companies that are heavily involved in research and investigations to find better ways in which to use chatbots. The purpose is to provide the consumer with a better experience. There are many specific areas where chatbots can be implemented such as booking tickets, online shopping or simply for the sake of better customer service. Many people hear about chatbots and then listen to all the marketing hype surrounding artificial intelligence entities but they do not really know what this is all about. The question that consumers should be asking is how does that chatbot add value to their lives.

Chatbots and language processing

For people who have been exposed to sophisticated applications, a chatbot is very similar. Just like in many other artificial intelligence programs there is a database, APIs and app layer. It is very easy for people to engage with chatbots. The obstacles which will have to be conquered however is the way in which the chatbot comprehend the wishes of the consumer. A lot of what chatbots are doing currently is based upon data which has been collected in the past. In fact, a large number of chatbots is currently maintaining logs of conversations which have taken place in the past. That recorded data is useful because it is used to analyze the needs of the consumer. This is why many chatbot manufacturers and researchers make use of technology such as machine learning and other useful models and they look carefully at those inquiries which the consumer makes. They will then formulate the best possible solution which will be able to provide the consumer with the best possible answer. There are many things on the minds of consumers today such as payments and outstanding balances. Therefore, a frequently asked question will be what is my outstanding balance. If the data is available in the database, then the chatbot will proceed to give a suitable answer. If the data not available, then it is possible to make use of a variety of APIs which is being used to better educate the chatbot.

The actual training process

It will certainly not be surprising to learn that chatbot education happens at a faster rate than human education. In most cases when human resources are used for the purpose of customer service, every one of those employees will be provided with an instruction manual. That manual will be the blueprint which must be referred to when engaging with consumers. In reality, a chatbot will have access to a database which contains thousands of conversation logs. This will provide the chatbot with a basis to understand what’s the frequently encountered consumer needs and how to provide solutions. There are three classification methods which are used by chatbots. The first one which will be used is pattern matches. AIML otherwise known as an artificial intelligence markup language is a standard structure model of these patterns which is used by chatbots. In order for patterns to be useful to chatbots, there must be a remarkable pattern for every sort of question imaginable. Only then will the chatbot be able to provide the consumer with a reasonable response.

The importance of natural language understanding

NLU or natural language understanding is dependent on three things. The first is entities which will proceed to present an idea to the chatbot. This can relate to anything which is part of your business process such as the payment system. The chatbot will also look at the context. It must be understood when a sentence is examined by NLU algorithms it doesn’t have the luxury of the historical backdrop as it relates to the user’s text conversation. What this means is that should the chatbot get a response to a question which has been recently asked, the chatbot will have no recall of that inquiry. This is why the different phases during a conversation are separately stored. This may be categorized under banners such as restaurant or pizza or something else. The fact is that when using context, it is possible to relate consumer expectations with the necessity of comprehending the last question. Another important issue will be NLP or natural language processing which allows chatbots to convert the text or speech which is used by your user into structured data. This information is then used when the chatbot has to make a decision regarding a relevant answer.

Conclusion

There can be no doubt that the chatbots processes which make efficient chatbot functioning possible are extremely complicated. But that is why SnathBot aims to make it EASY. Fortunately, the average user does not have to concern themselves with all these complications. They will receive a chatbot which has already been programmed and tested and which will be ready to perform the function for which it has been implemented.

Suggested reading:

Avi Ben Ezra on Sitetrail

Avi Ben Ezra in the Brussels Times

Avi Ben Ezra on Forbes