A few days ago we talked about how chatbots didn’t prove to be the revolution many of us expected. As we saw, one of the problems was related to expectations: chatbots were expected to be a magic wand that would solve everything. Yet, this doesn’t mean chatbots are not extremely useful when applied in the right environments and with the right goals in mind. We’ll present some cases of useful chatbots in the next weeks.
All companies that have a large number of clients want to be “customer-centric”, always placing the customer as the center of their strategies. This translates into taking good care of them, promptly and 24/7, without increasing costs, if possible.
Data scarcity is one of the major bottlenecks that AI practitioners have to deal with when training production-level models. Obtaining additional data typically involves costly manual annotation processes which, as we described in a previous post are fraught with problems.
Live Chats are one of the most useful features an online store can offer to its customers. The idea is simple: the store’s website shows a small window in which the user can interact (chat) directly with a representative of the company. Fast, transparent and easy for the customer. 29% of today’s contact centers offer Live Chat technology to the customers they serve, and it’s expected to grow to 64% in a couple of years.
You have a chatbot up and running, offering help to your customers. But how do you know whether the help you are providing is correct or not? Evaluating chatbots can be complex, especially because it is affected by many factors.
All machine learning engines (including the ones that make chatbots work) need training data to be useful. The better the training data is, the better results you will get. What’s a data scientist to do if they lack sufficient data to train a machine learning model?