Skip navigation
Customer satisfaction (Getty).jpg Getty Images
Customer satisfaction surveys in digital age require fine-tuning.

Customer Satisfaction Metrics Crucial to Meeting Market Demands

Research that yields useful insights requires careful attention to detail in the planning, development and deployment processes. Survey initiatives must be tuned to measure specific behaviors to inform decisions that drive specific outcomes.

The rise of new technologies that make it possible to directly capture end-user behavior and the emergence of tools that have democratized the fielding of sentiment surveys have altered the role of customer satisfaction research in automotive retailing.

As the landscape evolves, it will be essential for industry leaders to ensure the application of new tools and technologies is matched by a clear understanding of how customer satisfaction surveys fit within the data-rich environment of our increasingly digital landscape. In doing so, two key issues will need to be addressed.

The first revolves around the misapplication of customer satisfaction surveys to measure a wide range of industry variables, including employee productivity and departmental performance. The second centers on the role of customer satisfaction research as the industry moves through its digital transformation.

Balancing Tools With Expertise

While the market has seen tremendous innovation in  capturing user behavior with artificial intelligence (AI), machine learning (ML) and big data analytics, technology also has democratized the ability to capture customer satisfaction insights. New digital tools and apps have greatly enhanced the ability to collect sentiment data at the point of sale or through follow-up activities via mobile and email channels.

Unfortunately, the skills and expertise needed to create statistically valid surveys have not kept pace with the availability of these tools. It is a big problem because customer satisfaction surveys are not blunt instruments.

Research that yields useful insights requires careful attention to detail in planning, development and deployment. Survey initiatives must be tuned to measure specific behaviors that drive specific outcomes. Unfortunately, this front-end rigor is often absent in research initiatives that leverage low-cost, web-based solutions that are easy to use.

As a result, critiques are rising about customer satisfaction survey initiatives that draw data-driven conclusions that are improper and, in many cases, unfair. It is inappropriate, for instance, to measure the performance of sales consultants and service advisors based on customer feedback from areas that are outside of their control. It is a statement of the obvious. And yet, it is an outcome that occurs with unsettling frequency.

Integrating Sentiment and Telemetry to Capture Deeper Insights

Extraordinary developments in AI/ML, advanced analytics and cloud – among other technologies – have added the voice-of-the-vehicle (VoV) to a demand analysis chorus that once was the exclusive domain of the Voice of the Customer (VoC). The ability to capture data on actual usage – often in real-time – provides new critical context that allows the industry to compare what people say (sentiment) with what they do (telemetry).

It has been interesting to see the differences that emerge when comparing results from these two data sets. That said, it would be a mistake to conclude that one is more accurate than the other. For one thing, the two research methodologies answer very different core questions. VoV data accurately addresses what consumers do. VoC research arguably provides the best way of understanding why they do it.

Telemetry research can offer definitive insights into functions people use or ignore. Sentiment research, including customer satisfaction surveys, can reveal that people are simply unaware of features. They can suggest that more education is needed to help people learn and adopt new vehicle content.

Chris Sutton.jpgContext Is King

Manufacturers, dealerships and other critical players in today's increasingly complex ecosystem have never had more access to data points. Correctly interpreted, this rich array of information can lead to insights that improve decision-making at strategic and tactical levels.

Effective data-driven decision-making, however, will require leaders in the industry to nurture organizational competencies and develop specific skill sets. In the final analysis, the right analytical tools in the wrong hands can generate very negative results.

Chris Sutton (pictured, above left) is vice president of automotive retail at J.D. Power.

Hide comments


  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.