If you think that all chatbots are created equally, you’re unfortunately in the wrong. Chatbots today come in all shapes and sizes and have varying levels of capabilities. While basic chatbots may be adequate for most scenarios, there are scenarios in which more advanced chatbots are needed.
Menu-based chatbots are the most basic type of chatbot on the market today. In most cases, these chatbots are glorified decision tree hierarchies presented to the user in the form of buttons. Similar to the automated phone menus we all interact with on almost a daily basis, these chatbots require the user to make several selections to dig deeper towards the ultimate answer.While these chatbots are sufficient for answering that handful of basic questions that make up 80% of support queries; they fall well short in more advanced scenarios in which there are too many variables or too much knowledge at play to predict how users should get to specific answers with confidence.
Menu buttons help guide new users in Boomtown chatbots.
Unlike menu-based chatbots, these chatbots can actually listen to what users type and respond appropriately, or at least try to. These chatbots utilize customizable keywords to determine how to serve an appropriate response to the user. For example, if a user asked the question ‘How do I set up an auto logout transaction on a Poynt device?’, the bot would likely use the keywords ‘auto’, ‘logout’, and ‘Poynt’, to best determine which answer to respond with.
The Poynt Bot using keywords to respond with the right answer.[/caption]These types of chatbots fall short when they have to answer a lot of similar questions. The bots will start to slip when there are keyword redundancies between several questions.It is becoming quite popular to see chatbots that are a hybrid of keyword-based and menu-based. These bots provide users with the choice to try to ask their question directly or use the bot’s menu if the keyword functionality is yielding poor results.
Contextual chatbots are by far the most advanced of the three bots discussed in this post. These bots utilize Machine Learning (ML) and Artificial Intelligence (AI) to remember conversations with specific users to learn and grow over time. Unlike keyword-based chatbots, contextual chatbots are smart enough to self-improve based on what users are asking for and how they are asking it.For example, a contextual chatbot that allows users to order pizza will store the data from each conversation and learn what the user likes to order. The result is that eventually when a user chats with this bot, it will remember their most common order, their delivery address, and their payment information and simply ask if they’d like to repeat this order. Instead of having to respond to several questions the user just has to answer with ‘Yes’ and pizza is on its way!
Ordering a pizza with a Contextual Chatbot. While this is a very basic example, it is easy to see just how powerful conversation context can be when applied to AI and ML. At Boomtown, we’re in the process of incorporating conversational context to our chatbots to utilize our ever-growing library of support-related data. The eventual result will be drastic improvements in issue resolution time and accuracy for everyone on the Boomtown platform.
Have you used our chatbots yet? If not, we’d love to give you a demo and show you just how big of an impact they can make on your support team. If you have used them, we’d gladly welcome any feedback you may have.