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?
Data scarcity is one of the major bottlenecks for Artificial Intelligence (AI) to reach production levels. The reason is simple: data, or the lack of it, is the number one reason why AI/Natural Language Understanding (NLU) projects fail. So the AI community is working extremely hard to come up with a solution.
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.
Reducing complicated, confusing processes down to a natural conversation is potentially a huge business opportunity for anyone willing to jump headfirst and create a great user experience. Chatbots are only as smart as the words you feed them. If a bot is too rudimentary, people will lose trust in the company and will feel ignored and unappreciated. UX problems appear when user deviates from the designed linear flow.
Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.