One of the most significant aspects of a virtual agent is how fast it can learn. With a human in the conversational loop, training AI goes much faster: your bot learns and changes, keeping knowledge up to date. Plus, users never get the “Sorry, I did not understand your request” response, your brand will be able to solve the problem right away.
Gartner suggests companies working on AI and ML should employ human-in-the-loop crowdsourcing as an enabler of AI solutions since this approach gives a wider access to problem solving, model training, classification and validation capabilities, in comparison with traditional ML processes. Therefore, when rules are too complicated for automation or the ML algorithm cannot get more accurate results is time to resort to humans.
If you AI isn’t trained for a request yet, it will let the customer know it’s getting help from a human. Your team will be on the stand-by ready to moderate in such situations. This way, your sales representative will get a notification from your bot on your channel preferences. Those human agents will help their bot co-worker by answering the question. Then, your bot will release the answer to the customer and even confirm whether it was helpful or not.
By collecting questions that could not be answered and the corresponding human answers, the chatbot can be further trained and expanded. This makes sure that the chatbot only learns the questions and answers that are actually desired by its users. Irrelevant content is not even learned by the chatbot. As a best practice, it is recommended to cluster all your requests and then gradually update the most frequently asked questions. This ensures that the chatbot is constantly improving through a quality assurance process. We are proud to be the only company in the world that achieves intent detection with 90% accuracy in up to 6 months. You can download a case study.
In the following chart, you will see how a bot falls back on a human agent when it doesn’t understand a query from a user:
After this process, some tests must be made to see if a model is properly working, especially when the algorithm must face more complex issues and may not be confident enough to make the right decision on its own.
Human-in-the-loop (HITL), as mentioned in our previous post 'When Humans and Machines Come Together', is a concept combining both human and machine intelligence in order to improve the performance of ML algorithms. In this process, humans participate actively in training, tuning and testing data for those algorithms. ML algorithms do not work as traditional algorithms. They must be fed with great amounts of data to increase their accuracy. Here is where Bitext multilingual synthetic training data come to the fore. Our model works either if you do not have any existing training data or need to increase your accuracy or expand to other languages with consistency. It understands free speech, not just predefined phrases.
But this is not all, by collecting chat histories, you can gather exciting insights about your target audience. With a chatbot, unlike websites and apps, all requests can be viewed in plain text. Therefore, it is possible to see your customers desires directly. A sentiment analysis can be used to learn about your customers emotions and the reputation of your brand.
Would you like to read a case study? It is a great example of deploying a bot which is able to engage in successful conversations with customers worldwide. Do not wait to read it!
