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?
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.
Those familiar with stock markets surely know how traders communicate with each other via chatrooms in order to keep up with the trends and get actionable insights from peers. Most of these platforms, especially those offering free access, are a cluster of activity that needs to be moderated to ensure the interactions are civil and legal. Why do it manually through professional moderators when you can use an automated AI system?
People who use financial databases are aware of the hardships of ensuring information is structured and legible. Don’t worry! Knowledge graphs are here to help.