Back in late September, I had the opportunity to speak on an expert panel at the CX Innovation Summit, down in Naples, Florida. While weathering Tropical Storm Helene with many other industry leaders, I joined Jeffery Carson, Vice President of Journey Analytics & Operations at Synchrony, and Neel Sen, Director of Consumer Product Innovation at Verizon on stage to discuss the future of customer experience in contact centers.
Our panel, From containment to engagement: Innovating to exceed expectations, centered on the critical shift from containment to engagement in contact centers and the strategies needed to exceed customer expectations in today’s rapidly evolving landscape. In this post, I’d like to share some of the key takeaways the three of us discussed together to help you, the reader, as you’re fighting the good fight to elevate customer experience while operating your contact center in the most efficient way.
The Limitations of Containment-Focused Strategies
Our discussion began with the question, “Why is Containment not enough?” With my own background as a NLP Scientist, I started our discussion: “In the past, it was easy to align building a virtual assistant with containment metrics. All you need to do is start from your business case to reduce escalations by 30% and tailor each step of the dialog to maximize automation.” However, our panel explained that this approach often leads to local optimization at the expense of increasing customer effort.
Our panelists agreed that while containment is part of the transformation story, embracing an “Engagement” goal opens multiple ways to deliver a higher return on investment (ROI) in a “have your cake and eat it too” kind of moment – you can still reduce the number of calls reaching your customer care agents, while having more satisfied customers. But this shift requires a holistic view of the customer journey, considering both the self-service experience and the outcomes of the live agent conversation across the contact center.
Measuring Customer Engagement: Beyond Surveys
One of the ways we’re doing this is by focusing on metrics, such as Customer Effort Score (CES), to gain a more accurate picture of the customer’s experience and can make data-driven decisions to enhance engagement. But Carson pointed out that most of the time, companies are using customer survey data to make their decisions. He added that survey data often represents only 3% of the customer base and can be biased. “We needed to look at the ‘dark data,’ which can be gathered by taking a closer look at the conversational data from previous customer support interactions.”
Sen confirmed this observation, noting that approximately 80% of the signal comes from unstructured data, providing valuable insights into customer sentiment. He also cautioned about the prevalence of “prolific surveyors” who may skew results.
In contrast to over-relying on customer surveys, I introduced the Interactions focus on Customer Effort Score (CES) as a leading indicator for customer engagement. I explained that “we look at the entire call, from the ‘digital front door’ (self-service), to any hold time the customer experiences, to the live agent interaction.” Using machine learning, we build models to predict CES and we extract common patterns from customer journeys that may indicate root causes for the problem.
Leveraging Data and Analytics for Personalization
Taking customer experience even further, Sen described some of the innovation approaches their Research & Development teams are exploring. “By analyzing telemetry data at the customer’s router level, we can respect customer privacy, while being able to proactively help customers with their network related issues.”
For example, when a laptop continuously disconnects from a home network, the telemetrics data may show that the root issue is a wi-fi issue, and not necessarily the ISP’s back call. We can then proactively recommend a home network router upgrade or an addition of a wireless repeater that will stabilize their home network. “Our goal is to reach out directly to the customer in the app, so that as soon as customers are made aware of a problem, they can take self-service steps to mitigate the issue.”
I added that the personalized data and insights Sen provides can be applied to virtual assistants by turning a generic “How may I help you?” prompt into a personalized, “I noticed that you might have a problem with your laptop disconnecting. Can I help you with that?” This approach allows for proactive self-service and/or a seamless transition to live agent support when necessary.
Understanding Your Customer through Segmentation
Jeffery Carson highlighted the importance of truly knowing your customers. By combining customer satisfaction metrics with demographic data, his team has developed a segmentation model that correlates with key business metrics. For example, Carson’s team is combining customer satisfaction metrics like NPS with demographics extracted from data clustering techniques. In practice, his method is reliably segmenting customers into High, Medium, and Low value categories, and experiments show that it is correlating well with customer experience metrics and ROI indicators like expected Customer Lifetime Value.
By making data like this available, CX teams have additional tools to provide tailored experiences based on a customer’s profile, ultimately measurable improvement in better engagement and business metrics.
Overcoming Challenges in Adopting Generative AI
Nowadays, you can’t have a panel discussion without mentioning Generative AI at least once. As we discussed the role of AI in enhancing engagement, we acknowledged the challenges in moving from Generative AI prototypes to production, which often requires significant changes in business processes, risk management, and training.
Carson described Synchrony’s AI Council and its success in developing a basic strategy for the business to ensure alignment on outcomes and to proactively address legal and compliance challenges. Sen shared similar successes, explaining how changing business processes and properly training every team is an all-hands effort.
In addition to adding internal controls, I described the Interactions way: “We pair human expertise with GenAI, so that suggestions to improve the conversational design and other insights are explainable.” This combination of human oversight and AI capabilities helps address legal and compliance hurdles while ensuring consistency and quality in customer interactions.
The Business Impact of Elevated Engagement
Ultimately, our panelists’ shared goal is to create experiences that quickly resolve customer issues while making them feel valued. As Carson pointed out, “Happy customers spend more,” and even small improvements in customer satisfaction can translate to significant profits. By focusing on engagement, we not only enhance customer loyalty but also drive tangible business results.
Sen stressed that understanding the business cost of not listening to customers is crucial. “Customer teams often struggle to think strategically when overwhelmed by day-to-day operations,” so intentionality is essential.
Finally, I added, “Without focusing on engagement, we will continue to struggle to see the benefits of more powerful AI. I want to see people’s requests resolved quickly, while feeling that they are noticed.”
Looking Ahead
As we continue to navigate the evolving CX landscape, our panel’s consensus was that a shift from containment to engagement is crucial. It’s an exciting time to be at the forefront of this transformation, and I’m eager to see how our collective efforts will shape the future of customer engagement.
As a take-away, to truly improve customer experience, consider these three steps:
- Look beyond containment: While reducing escalations is important, it shouldn’t be your only goal. Measure customer effort and satisfaction alongside containment metrics.
- Take a holistic view: Analyze the entire customer journey, from self-service to live agent interactions. This comprehensive approach helps identify pain points and opportunities for improvement.
- Align metrics with customer experience: Ensure your KPIs reflect both operational efficiency and customer satisfaction. Consider adopting metrics like Customer Effort Score (CES) alongside traditional measures.
* The views and opinions expressed in this document are those of the author(s) and do not necessarily reflect the official policy or position of any other agency, organization, employer, or company.
