The most effective way to discover the intent behind your customer’s questions and provide the right answer is by using a decision tree. What are they and how do they work?
When it comes to chatbots, businesses want to know one thing. The million dollar question for a market which will be worth billions within a few years is – can my virtual agent answer my customers’ questions?
Assuming your chatbot has robust natural language processing (NLP technology), the most effective way to do this is through decision trees.
What is a decision tree exactly?
In the context of chatbots, a decision tree essentially helps them find the exact answer to your question. Further information on what decision trees are outside of the AI world can be found here.
The root of the tree is your initial question. For example, you might ask to buy tickets for a concert. Of course, a chatbot will need more information than that to fulfill your request.
To do this, it will ask a series of questions, the branches of the decision tree if you like. Each one narrows down on the customer’s goal through chatbot intents.
Therefore when buying a ticket, the chatbot might ask who you want to see. Having selected “U2” it might then proceed to ask for the date and venue you want, then the price range and finally the specific seat.
Only once we have reached the end (or the leaves) and you have selected your seat has the decision tree ended.
Making transactions using a decision tree in chatbots
Decision trees are flexible enough to carry out a number of functions for your virtual agent.
One major problem companies face is shopping cart abandonments – currently standing at 78% and costing $4 trillion a year.
Decision tree transactions can significantly reduce this by ensuring customers locate exactly what they want in a conversational format. Just like in the concert ticket example above, users can buy almost anything through this method.
In terms of cart abandonment, the chatbot’s decision tree is able to streamline the actual payment process at the checkout to ensure the user does not become frustrated.
Decision tree: solving the difficult questions
In addition, decision trees can improve the overall customer experience by tackling nonmonetary transactions such as password recovery. Bots can identify which account you want to change by asking for your details.
Decision trees can also replace general FAQs. A major problem with help sites is that their answers are far too general for customers who value personal interaction. The decision tree is able to initiate a conversation with the user to understand exactly which answer is the most relevant to them.
For example, a user might want to know when their package is arriving. Naturally, more information is needed. A decision tree can account for this by asking for an order number or anything else that identifies that exact purchase before informing the customer.
Meanwhile, a company with general FAQs will not be able to provide the same level of service and will have to rely on live agents – this can be costly and time consuming for both customer and company.
Have you got a real life example?
Inbenta’s chatbot Veronica uses decision trees for a variety of scenarios. For example, this is the decision tree created when someone asks Veronica about integrating Inbenta technology on their platform.
Veronica can provide some background before asking what platform the person uses. This is an example of a decision tree based on natural language as the customer is asked to type in their answer rather than select an option. Note how Veronica is able to recognize ‘Facebook’ despite the misspelling.
This decision tree has been edited on Inbenta’s Backstage by arranging the existing contents into a coherent journey of discovery towards the user’s intent.
Whatever chatbot you choose to handle your decision trees must be able to cater for some of the more complex titles that might occur. For example, a decision tree about return policy must have options for different time spans depending on how long the user has had the product for.
Other examples of decision tree structures include a ‘Buttons’ option which presents the user with all the available options for them to select from and a “Yes/No” format which narrows down what the user wants by asking a series of questions.
Customers want a personal service from companies. But they also want their questions answered correctly. Using decision trees for your chatbot will satisfy these basic desires in a revolutionary way never before experienced.
Inbenta utilizes its patented natural language processing and +11 years of research & development to create interactive chatbots with an industry leading +90% self-service rate.
Companies around the world including Ticketmaster UK utilize the Inbenta Chatbot to maintain a personal service for their customers while reducing support tickets.
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