Data Delivers a Warning on CX Strategy in the Chat GPT Age

With the advances in self-service channels powered by Machine Learning (ML) and Artificial Intelligence (AI) that keep pulling more of the transactional contacts out of the assisted support queues, live interactions are becoming ever-more important on a per-contact basis as our research shows that it is these interactions that will significantly influence loyalty and advocacy outcomes. In many of our studies, we see the Customer Service factor accounting for 5 or 6% of the total customer satisfaction model due to low-incidence rates of interactions relative to all the other variables accounted for in our studies; but when a customer does contact a brand, it can account for more than 30% of the total Customer Satisfaction model, ascend to the #1 factor, and drive loyalty and advocacy. The point is that if someone must contact your brand and speak with an agent, the entire relationship is on the line.

All Eyes on Chat GPT

Right now, we have noted that the excitement and press surrounding Chat GPT 4 has put cultural, industry, corporate and peer pressure on executive management to address the potential cost savings and service advantages of utilizing large language models in delivering customer service. This has resulted in pressure to shift more load onto AI-powered self-service channels even more quickly than planned. While this should absolutely be a priority, there is risk in focusing too much energy on this and not paying enough attention to assisted channels for brands trying to compete and win based on service excellence.

Don't Forget About the Phone

Our U.S. Residential Internet Service Provider Customer Satisfaction Study shows that customer service “influence” is most heavily weighted with the phone channel at 65%, followed by online web service at 19% and chat service at 16%, with assisted support having more impact than unassisted support. While self-service channels are an important part of your channel mix, as without them support expenses would be much higher and consumers would be paying much more, this is just one illustration that reinforces the importance of maintaining focus across channels as here we see assisted the phone channel has an outsized influence on customer satisfaction because calls that require an agent are typically very important to the customer. 

Leveraging ML/AI to Improve Traditional Channels

When we take a closer look at the customer service factor within more than 50 studies across industries, “Knowledge of the Rep” has closed in on “Timeliness of Resolution” and is now the #2 driver of the live phone customer experience. When we look at assisted global software support, “Knowledge of the Rep” is actually now the #1 driver of customer satisfaction having surged in importance along with “Concern,” which is the #2 driver. This change in customer satisfaction drivers is not happening in a vacuum. There is a big surge in “Rep Knowledge” importance because there is a big difference in “Rep Knowledge” performance across brands. Why? There are 2 key reasons behind this large difference in the perception of knowledge performance amongst brands:

1. Agent attrition
2. Lack of investment in data infrastructure and technology to help the agents get the information they need

Frontline agents in tech support and customer service are often still quitting well before they reach competency levels that are high enough to provide and demonstrate knowledge. Brands that haven’t addressed the underlying reasons for early agent attrition (of which there are many, but none more important than the meaningfulness of the work to agents), will continue to suffer. The second reason, however, is where AI/ML can help.

Brands that invest in technology that includes AI/ML-powered knowledge bases, CRM, journey and channel integration, etc., that culminate in applications that provide agents insights during the interactions can yield “knowledgeable” agents and higher first contact resolution. There are underlying pre-requisites particularly in the effective management of all relevant data points across the enterprise for integration into the right applications to maximize the benefits of this agent-assist technology, but in the end, some brands have made the investments in helping agents be more knowledgeable and some haven’t. Since a great experience anywhere affects expectations everywhere, brands that have not yet found a way to leverage AI/ML-enabled capabilities to support their agent-assisted channels should consider focusing on these applications vs. solely looking at these capabilities for self-service so they don't fall behind.  

Leverage Technology Beyond Self-Service

Agents aren’t going away anytime soon for most brands competing on service, and high performance in assisted channels is often overlooked as a powerful influencer of topline revenue. At this juncture, service providers who want to execute a strategy to differentiate themselves based on service, would do well to still invest in the assisted experience by addressing agent attrition and utilizing technology that will help chatbots and other smart applications learn while also helping agents demonstrate knowledge in resolving these more-and-more-complex interactions on the first assisted contact.    


If you don’t currently have a mechanism in place to identify how you are performing across all drivers of customer service satisfaction for your customers by channel so that you can identify and prioritize your performance gaps, contact our team today. Let us help your team get focused on the right actions to improve your business. 

About the Author: Mark Miller leads the J.D. Power Global Customer Service Advisory and is responsible for thought leadership, solutions development, strategic alliances and client support. He leads customer service, technical support and sales performance improvement and certification initiatives for the company.

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