This is a guest post by Fabrice Martin, CPO, Clarabridge.

At the center of every sport is strategy. As a game unfolds, winning teams adapt their strategies to fit changing circumstances. Underlying these decisions is a reliance on technology and big data analytics for competitive advantage. To be crowned champions, winning team owners and coaches optimize their training programs and player selection by gathering extensive sets of data and analyzing it for practical insights.

It might be surprising to find out that contact centers and sport teams aren’t so different in this regard. Companies without useful and accurate insight into customer experience will fall behind customers’ ever-increasing demands. Much like the film Moneyball, contact centers can turn to interaction analytics to select the best agents for the team and win the customer experience game.

Here’s how companies can become CX champions using interaction analytics.

What does “interaction analytics” mean?

A substantial amount of information passes through a contact center every day. While some managers are already using tools like customer surveys to capture data, they miss most of the real-time details in the conversations their agents have. The way a customer speaks or reacts during a call can reveal much more than an after-service survey alone. The challenge, however, lies in successfully mining these unstructured conversations for data and insights, and doing so at scale.

To mine the treasure trove of conversational data, contact centers are turning to speech and text analytics applied to interactions. Today’s tools enable managers to visually explore customer chats or calls and discover insights that can lead to practical customer experience changes.

Interaction analytics systems work by accumulating conversations and related customer and interaction metadata, then using natural language processing (NLP) to sort and structure conversations by category, such as topics of conversation. The system augments its analysis using natural language understanding (NLU) to identify attributes like a customer’s emotional state or intent, an agent’s empathy and effort levels expressed, among others. The full analysis offers accurate, nuanced insights into the customer’s state of mind and overall CX.

What really changes the game is that interaction analytics work at scale. Contact centers can run millions of audio and digital conversations through an analytics engine and produce reliable insights to improve agent performance and CX. Better players and a greater understanding of the playing field gives contact centers more to work with on their path to winning the customer experience game.

Interaction analytics and the customer experience

Last year demonstrated how fast the contact center playing field could change. At first, fearful and uncertain customers overwhelmed call centers, generating a huge spike in call volume. Customer service centers, in the midst of shifting to remote work, were often unprepared for the volume, emotional distress and trajectory of conversations. Customers took to more digital channels to reach out, like email, social media and chat. Key insights existed within these channels, but contact centers needed the right tools and acumen to find and use them.

Champions are made with those kinds of key insights. Sports teams want to win games, but they also want to do so with style. The experience they create for their fans makes the difference, and contact centers keen on building memorable experiences are no different. By deploying interaction analytics across communication channels, centers can learn what their customers — their fans — want and show them their feedback is leading to tangible results.

For instance, if a customer service center experiences a sudden surge in interaction volumes, managers would want to know why. Using contact driver analysis, management can discern emerging trends or patterns over time, and discover what drives customers to select one communication channel over another. Knowing this, centers can re-engineer agents’ knowledge bases with greater accuracy, create more helpful self-service resources to send traffic to lower-cost service channels, and optimize overall customer experiences.

As another example, long hold times, unsuccessful chatbot engagements and unresolved issues increase customer effort and ruin their ‘fan’ experience. Effort analysis can analyze these issues within the context of potential upstream process issues, such as confusing billing practices, ineffective self-service options or product or shipping defects decreasing overall customer satisfaction. An easier customer journey ultimately leads to deeper customer loyalty and repeat business.

Improve agent performance with interaction analytics

In Moneyball, Oakland Athletics manager Billy Beane and his team of statisticians used data to assemble the best team possible from their limited resources. Their data-backed decisions redefined how baseball was played. In contact centers, your agents are your players, and interaction analytics can help you build the best team.

Quality of service (QoS) forms the backbone metric for success in building teams. Star players — your top agents — set themselves apart from the pack with how well they serve customers and contribute to overall CX. They can adapt to any change in the playing field while still providing excellent customer service.

Monitoring and assessing QoS became more important as the pandemic required centers to operate remotely. Interaction analytics help standardize QoS scores across a center’s service channels, which management can use to pinpoint where agents struggle, recommend changes in their customer approach, and improve their overall game. Whenever there’s a change in customer experience, the analytics engine can identify technical and QoS challenges as well as measure customer satisfaction.

Interaction analytics powered by NLU helps managers find and support their top agents, too. Analyses like root cause analysis, trend analysis, script adherence, emotion and sentiment analysis, intent detection, effort evaluation, regulatory compliance and ad-hoc discovery facilitate this process. Managers can discover their strongest agents and understand why they succeed, enabling team leaders to build and retain championship service teams.

Customer circumstances will surely change often, and contact centers must adapt their strategies accordingly if they want to win. With interaction analytics, management can plan their winning strategies and build their teams by capturing data at scale and using it to build better insights. Analytics allow contact centers to optimize their operations and develop best-in-industry customer experiences. Champions are not born; they’re made. Use interaction analytics to win the CX game.

This is a guest post by Fabrice Martin, CPO, Clarabridge.

About Fabrice

Fabrice Martin is the CPO at Clarabridge and is responsible for the vision, roadmap and go-to-market strategy for the Clarabridge CX Suite of products. He brings more than two decades of experience launching new products and business applications focused on solving large, complex analytical problems and delivering valuable insights.

Image by Pete Linforth from Pixabay