Cyara Solutions:
Chatbot Testing & Optimization
Don’t Let Your Conversational AI Fail. Take It from Good to Great.
The use of conversational AI is on the rise. Every industry, including ecommerce retailers, banks, and healthcare providers, are deploying conversational AI bots for self-service to improve their customers’ experiences while keeping productivity up and costs down. Market size projections are off the charts with a projected $4.9 billion by 2032, with a CAGR of 19.3%, according to Precedence Research.
While many companies are enthusiastically optimistic about the role of conversational AI as a customer self-service channel, consumers’ opinions on conversational AI vary widely depending on their needs and past experiences. A recent commissioned study conducted by Forrester found that customers appreciate the convenience of 24/7 support; however, negative or frustrating chatbot experiences can push customers to seek out alternate – and potentially more costly – channels to interact with a brand.
Research by Gartner revealed that only 8% of customers chose to use a chatbot during their most recent engagement with customer service. And of those, only 25% felt that they would use a chatbot in future interactions.
Why are so many chatbots failing?
Some of the primary reasons a conversational AI bot fails, or at least falls short of customer expectations, are that most bots do not understand customer intent, and many lack sufficient training and testing. And to exacerbate the situation, when bots cannot understand a customer, many fail to provide an easy escalation to a human.
For conversational AI to be successful, it needs to be more than a convenience. They need to deliver fast, accurate answers whenever possible, and they need to be able to move a request forward in a way that is most efficient and helpful to the customer. To accomplish this, enterprise conversational AI development teams should focus their attention on the capabilities below.
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), conversational AI bots are challenged by understanding customer intent. For these bots, 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 conversational AI 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 an AI agent. Note that none of the requests uses the word “return,” yet the bot 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 conversational AI, particularly with the nuanced ways humans express themselves through customer input. Conversational AI bots 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
Conversational AI bots 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 conversational AI bots must be able to interpret. This training data is a set of examples expressing each intent that a conversational AI bot uses to turn into a mathematical model for recognizing each intent.
Training data emphasizes target use cases, enabling conversational AI 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 conversational AI bots 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 bot training data, like:
- t shirt too big and want $ back
- hey, how do i send this back to u? can u pick?
- Need refund on this t
- can i exchange for a small
- give me credit for t shirt
- hey amazon, take this back
Continuously Test and Optimize Chatbot Technology
Why? Because continuously testing conversational AI assures quality at scale. Similar to other customer experience (CX) channels, conversational AI requires 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 conversational AI technology and using those insights to optimize your bot’s model is the only efficient way to continually learn and adapt to meet customers’ needs.
The DevOps methodology of shifting left directly relates to conversational AI 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:
- Create testing data
- Test target use cases
- Test non-target use cases
- Test conversational AI bots in the context of the whole omnichannel customer journey
- Test conversational AI escalations to a live chat agent
- Test bot and live chat agent performance under peak load conditions
- Monitor the conversational AI bot in production and identify areas for improvement and optimization
Improve Accuracy & Containment for Chatbots and IVRs and Increase Customer Satisfaction – 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, monitoring and optimization for your chatbot and models.
Conversational AI bots are on the rise, but so are bot failures. Teach your conversational AI how to understand customer intent, train your bots, and continuously test your bot technology to ensure that, if issues occur, you can catch and resolve them before your customers experience them.
Utilizing conversational AI to cost-effectively and efficiently deliver a flawless customer experience is within reach. Make your conversational AI bot an example of great customer experience. Learn how Cyara’s AI-Led CX Transformation Platform and Cyara Botium enable flawless CX journeys across voice and digital channels.
Assure Journeys. Transform Experiences.
Cyara revolutionizes the way businesses transform and optimize customer experiences. The Cyara AI-Led CX Transformation Platform replaces time-consuming, error-prone manual processes, empowering enterprises to drive CX transformation, monitor and assure customer journeys, and optimize CX across all self-service and agent-assisted channels, including voice, video, and conversational AI. Cyara helps many of today’s leading brands raise the bar on customer experience by ensuring that customer journeys perform exactly as designed, and that CX systems operate as intended, even under pressure. Cyara is the only choice for complete, end-to-end CX transformation!
