Kenan-Flagler researcher unlocks secret to better bots

By now, we’re used to voice-based chatbots in our personal lives such as Google Assistant, Amazon’s Alexa, Apple’s Siri and Microsoft’s Cortana. But when businesses implement similar bots as customer-service agents, they face many hurdles.

Research by Yuqian Xu, an associate professor of operations at UNC Kenan-Flagler Business School, is furthering our understanding of what a capable artificial intelligence customer-service agent looks like and how companies can improve experiences for consumers.

Voice-based chatbots are complex compared to their earlier, text-based cousins. Oral communication is comprised of so many different variables that companies and researchers need to account for, such as tone of voice, accent and speech cadence.

How companies can reap the cost-cutting benefits of AI without alienating customers or diminishing trust and competence is at the core of Xu’s study.

In new research, she identified a way for AI customer-service agents to better meet user expectations: proactively predict customers’ needs. She shares her findings in “Voice Chatbot Design: Leveraging the Preemptive Prediction Algorithm.”

Predictions improve the customer experience

Xu and doctoral student Shuai Hao worked with a leading e-commerce company that already used chatbots to handle customer inquiries about their orders. For this study, Xu modified the AI to include a feature that proactively identified the likely package each customer was inquiring about.

The modified AI began each call with something like, “Are you calling about package ABC?” It made predictions using a machine-learning algorithm that analyzed key logistics-related data from the individual’s history in the company’s package tracking system.

When the AI correctly predicted which package a customer was calling about, the results were impressive. Customer satisfaction with the interaction went up by 6.4%, call times shrank by 7.7%, and fewer calls needed to be transferred to human agents.

If the AI agent predicted incorrectly, the customer experience suffered, but the negative impact was considerably smaller than the gains achieved with a correct prediction.

Xu’s research demonstrates that establishing trust between humans and AI early in their interaction significantly increases the likelihood that customers will allow the AI to address their issues, rather than requesting a transfer to a human agent right away.

Building trust in technology

Research has found that many people instinctively feel an aversion to new, advanced technology like AI that seems to mimic human qualities. They are put off by its human-like intelligence and autonomy paired with a lack of emotion and dislike the opacity behind AI’s “thinking.” That aversion can translate to a lack of faith in AI-driven products.

Xu’s research found that improving chatbot intelligence with prediction algorithms can help earn customers’ trust. Correct predictions signal to the user that the AI agent is competent and they can rely on it to address their concerns. This makes customers more likely to continue working with the chatbot, rather than skipping to a human agent at the first possible opportunity.

As companies increasingly implement AI technology in customer-facing roles, building trust will continue to be a critical goal. Trustworthiness helps customers have satisfactory experiences interacting with AI agents and a positive image of the companies using them.

Next, Xu will be investigating how to enhance the trustworthiness of AI chatbots. For example, how does tone of voice affect how a customer feels about an AI agent? Do different word choices and phrasings make a difference? Should chatbots try to offer multiple predictions in a conversation? What happens if customers go way off script?

As voice-based AI chatbots become an increasing player in customer service, research like Xu’s becomes even more important — to find out what truly works for the good of firms and consumers alike.