Finding The True Value of Extended Service Programs, Part 3: Data and Warranty Analytics
In the first two installments of this series I provided a few thoughts around modernizing Extended Service Programs (ESPs) and how they can serve as a platform for customer continuity. In this article, I’ll take a look at how the data collected from service contract programs can provide additional value to Customer Relationship Marketing (CRM) initiatives, product design and engineering, becoming the basis for predictive risk and loss cost warranty analytics.
Having worked with many clients over recent years, we recognized the potential of ESP programs to generate a lot of useful data for companies. Today, After uses this data to create accessory programs, maintenance programs, causal parts analysis, and predictive loss cost forecasting to name but a few applications. We’ve also developed a novel methodology to project the insights of ESP behavior to the larger customer base to help with customer segmentation and response models for CRM initiatives.
In this article, I’ll provide a few details on how we found value within the data, how we use it today, and how you may be missing a big part of your business by not using ESP data as an asset.
In the early days of our work on ESP programs, After brought its unique analytical skills to solve common business problems ESP’s face. Many of the “asks” were around increasing response, finding the optimal price and increasing the overall value of a product registration while reducing risk.
Over time, we developed an extensive base of statistical methodologies, marketing strategies and execution know-how to solve ESP business problems. What we also saw emerge was a set of customer marketing opportunities largely ignored by traditional CRM folks.
As often happens when our teams work to solve new business problem, they also find unexpected value in the data sets they are provided. When viewed through an analyst’s lens, with marketing and technology people in the room, the ideas and opportunities quickly emerged.
After several different ESP programs had been analyzed, patterns started to develop. The data generated from these programs told similar stories regardless of industry or product. What After was finding was that the data itself was an extremely valuable asset, yet the value was not understood by our clients and in many cases was being collected by an outsourced insurance company or servicer contracted to fix or replace broken products. Incredibly in some cases, the vendor contracts did not provide rights for clients to access their own data.
As one example of the data’s richness, After found the purchase of accessories or the use of OEM consumables to be one of the most predictive values of ESP adoption. As it turns out, the inverse is also true: ESP adoption also is a great predictor of accessory and consumable purchase.
Other insights included the ability to predict repurchase timing, the likelihood to participate in a recall, register other products, or become an advocate in the social space.
Once these predictive values were understood, After began to fold them into various CRM applications. In many cases, the data itself created more value than the original ESP program being analyzed or the steps made to optimize it.
From a purely analytical perspective, two main products were born out of the wider work. Predictive Loss Cost, the statistical methodology to accurately predict loss run-off at product levels and Causal Part, a statistical methodology to find the reasons for failure and the predicted breakage on units already sold. The former is critical to finance departments that need to manage risk and that need to comply with accounting standards. The second is highly useful to engineering, manufacturing, and sourcing groups, since we can isolate problems with specific assemblies and parts.
Each of these studies provides incredible new insight to our clients, but it also changed the way they look at their business. From a risk and reward perspective, clients now have the ability to adjust price or limit risk at the product level, not just the average of all the products together (which is the typical insurance model). And for engineering, they now have the ability to proactively make changes before the issue grows and provide maintenance programs that can reduce the loss of money and customer perception. In the end, both of these insights help both the company grow and provide better customer offerings and service.
There are many other valuable insights from ESP data. The data created and collected from these programs should be protected and used as a company asset. Send us an email or give us a call if you have any other suggestions for using ESP data. Our team is always available for a conversation.