Ensure Chatbots Understand Customer Intent

Customer intent is what the customer wants to get done, or the ultimate outcome they’re seeking when they engage with your company.

Examples of customer intent are returning a purchased item, receiving flight status, and replenishing funds for a debit card. Similar to Interactive Voice Response (IVRs), chatbots are challenged by understanding customer intent. For chatbots, there are many ways that humans express what they need in written format — that’s called customer input. Natural Language Understanding (NLU), a branch of AI, is often used by chatbots to interpret typed customer inputs.

For example, Andrea wants to return a t-shirt she purchased. Here is a small sampling of how she might type the request to a chatbot. Note that none of the requests uses the word “return,” yet the chatbot must understand that the customer intent is to return an item.

Example replies to chatbot: “t shirt too big and want $ back”, “hey, how do i send this back to u? can u pick up?”, “need a refund on this tee”

Understanding customer intent comes naturally to humans, but is extremely difficult to imbue in chatbots, particularly with the nuanced ways humans express themselves through customer input. Chatbots are only as good as the knowledge that powers them so development teams must collect large amounts of data that relate to customer intent.

Perform Chatbot Training

Chatbots need training that provides data on how to understand customer intent. Because of the complexity and diversity of ways in which humans convey their intent, it takes large amounts of data to represent the broad range of inputs that chatbots must be able to interpret. This training data is a set of examples expressing each intent that a chatbot uses to turn into a mathematical model for recognizing each intent.

Training data emphasizes target use cases, enabling chatbots to successfully recognize and handle each use case. Obtain training data from domain experts (conversational and customer service expertise), chat logs based on customers talking to chat agents, website requests, SMS, email, and any other documentation on how customers type requests.

In Andrea’s example above, to make sure chatbots are prepared to help her return the t-shirt that she purchased, development teams should collect the many ways that she might type her request, and include these in chatbot training data, like:

Continuously Test Chatbot Technology

Why? Because continuously testing chatbots assures quality at scale. Similar to other customer experience (CX) channels, chatbots require continuous testing and continuous improvement. In the previous example on Andrea’s request to return a purchase, the training data would be used to create testing data and test cases. There are increasingly new ways that humans express what they need, and automating the testing of your chatbot technology is the only efficient way to continually learn and adapt to meet customers’ needs.

The DevOps methodology of shifting left directly relates to chatbot development. When teams adopt a shift-left mentality, it means they’re able to focus on problem prevention, rather than detection. As CX software is designed, testing is designed in parallel. With shift-left development, teams create test cases directly from designs, and can typically execute testing through the design phase.

To apply CX software best practices to chatbot testing, follow these critical steps:

1. Create testing data
2. Test target use cases
3. Test non-target use cases
4. Test chatbots in the context of the whole omnichannel customer journey
5. Test chatbot escalations to a live chat agent
6. Test chatbot and live chat agent performance under peak load conditions
7. Monitor the chatbot in production

Improve Accuracy & Containment for Chatbots and IVRs Powered by Conversational AI

Cyara’s CX Transformation Platform includes automated quality assurance designed specifically to assure, maintain and monitor your voicebot and conversational AI. Cyara helps you to deliver flawless customer experience through all channels and platforms by conducting automated NLP testing, conversational flow testing, security testing, performance testing and monitoring for your chatbot.