When residents interact with state and local government, they expect a customer experience that is as easy and seamless as those offered by CX champs like Amazon, Google or Netflix. In fact, at the federal level, that expectation is increasingly a mandate in the wake of a December 2021 executive order on CX directing public sector agencies to “put people at the center of everything the government does.”

Despite this, public sector CX continues to lag behind private sector efforts. One survey of 8,000 consumers in 27 countries found that while more than three quarters of customers are satisfied with private sector CX, less than two thirds (61%) felt that way about government agency CX. Part of the problem is a public sector overreliance on traditional Voice of the Customer (VoC) data and surveys that only measure CX based on individual touchpoints with customers, not the entire customer journey.

Beyond Just Touchpoints: CX Analytics for Deeper Insight and Control of the Customer Journey

Fortunately, more state agencies are learning to embrace what’s known as CX Analytics, which provides a 360-degree view of the resident experience that goes beyond isolated touchpoints to create a more holistic and nuanced understanding of the entire customer journey.

Researchers at McKinsey were among the first to make this distinction in a report that argued “performance on journeys is substantially more strongly correlated with customer satisfaction than performance on touchpoints—and performance on journeys is significantly more strongly correlated with business outcomes such as revenue, churn and repeat purchase.“

CX Analytics dives deeper than just analyzing surveys, single phone calls and other VoC touchpoints. Instead, CX Analytics allows agencies to build a holistic view of residents and their entire customer journey. And with the right IT platform and partner, government agencies can deploy their CX Analytics implementation in manageable increments. First, agencies must implement essential descriptive analytics that integrate and visualize data from a variety of sources to present a 360-degree view of resident activity. Diagnostic capabilities can then be overlaid via filters, drill-downs and correlation analyses to examine relationships and resident experience trends.

From there, predictive analytics models can project tomorrow’s conditions via machine learning analysis – a process that clusters and classifies residents and specific journey elements to forecast future behaviors and interactions. Ultimately, prescriptive modeling for decision support becomes possible with AI-driven deep learning and recommendation models that allow agencies to take a proactive approach to service decisions.

Use Case: CX Analytics for Taxpayer Services

A fully developed CX Analytics platform brings an amazing amount of control and value generation for a state agency, including the ability to update outbound communication with behavioral nudging, proactively staff certain call centers and business processes to address upcoming needs, and tailor treatment streams to move residents into a desired outcome earlier. For a real-world example of how this is done, let’s examine a recent deployment we implemented for the IRS.

We started by connecting previously siloed databases on transaction and account status history, inbound and outbound contacts, account holder details and other data into an integrated analytics platform. Using that platform, we were able to create a much more descriptive data set with the help of no-code data exploration and graph databases. This allowed internal users of the system to better identify and define business problems and uncover common journey elements and linked dimensions.

From there, we built a diagnostic engine powered by correlation analysis and natural language processing capabilities for topic modeling and sentiment analysis of call transcripts to gain a deeper understanding of why a resident’s journey is unfolding as it is.

Finally, we matured the agency’s CX Analytics further into event prediction via recommendation engines that scour historical journey data and filter predictions using a “positivity” score. That allowed the recommendation engine to prioritize options that lower the overall cost of the journey to both the agency and to the individual. Taken together, the combined capabilities of the CX Analytics solution helped the client set strategic policy and establish proactive tactics such as targeted outreach campaigns; demand-based, just-in-time staffing of call centers; and alternative treatment stream suggestions for telephone agents to help nudge residents toward preferred behaviors.

Those are just a few of the benefits from just one use case that illustrate how CX Analytics can vault state agencies to the front of the CX innovation curve. As agencies work to implement these solutions, they will find themselves better able to navigate and optimize the entire customer journey for improved resident satisfaction and reduced costs to serve.