By Dan Hulkower, Senior Vice President of Business Development at After, Inc.

Given the massive and increasing global competition in the manufacturing industry, warranty programs have evolved into tools for driving customer satisfaction post-sale, retention and competitive advantage.

Product quality, reliability and the need for predictive analytics have also become key strategic priorities. Warranty Analytics software packages were designed to help warranty organizations explore their data without the need for IT resources, dig deeper into trends and outliers, and build simplistic scoring models.

So, what has hindered more widespread acceptance of these solutions? Why aren't more warranty organizations using them? Here are three key reasons these gaps exist in current solutions.

Gap #1: Warranty Industry Expertise

A primary issue with current Warranty Analytics Software (WAS) solutions is that they have been architected for other markets like financial services, telecommunications, and healthcare. These solutions may incorporate the most advanced data analysis capabilities like machine learning and Artificial Intelligence (AI), but these capabilities can't be utilized if the systems don't deliver the data in the right format to be accurately analyzed. Even basic warranty reports -- unit sales, paid claims, claim cost rakings by product/part/dealer -- can have skewed results.

For example, warranty-specific data sets like service technician comments require warranty industry knowledge to code properly. If a provider doesn't have knowledge of the jargon, acronyms, or shorthand used in technician comments, the data can be excluded from analysis or processed incorrectly.

Warranty data expertise is also necessary to build warranty-specific reports not found in typical analytics software -- reports that show trends, heat maps and diagnostics that can be especially valuable to quality teams looking to improve product reliability.

Gap #2: Ongoing Data / Modelling Support

The second issue with current WAS solutions is that warranty managers and quality engineers have no data analyst support after the software is installed. When new claims and warranty data sets are ready to be uploaded or changes need to be made to data structure or format, warranty managers and quality engineers don't have the time or skill sets available to process them.

This step is crucial to the continued value of a warranty analytics platform. Without data experts to prep the data, detect, and fix errors in real-time before uploading, the analytics will lose its accuracy and relevance.

For example, if a team of quality engineers is using the software platform to predict timing of part failure ("Early Warning Systems") and the data sets that they receive each month have inconsistencies that aren't addressed, then the models will be flawed and inaccurate. And if predictions are wrong month over month, the engineers will lose trust in the validity of the software and stop using it going forward.

Gap #3: High-Impact Strategic Insights

Without warranty industry experts who also have the required warranty data knowledge, WAS solutions become little more than interactive dashboards and reporting software. These solutions can't uncover accurate insights to answer "what happened?" questions, nor can they create relevant predictive models to inform strategic decision making.

In order to accomplish this, warranty organizations must find a solution that combines warranty-specific platforms with outsourced warranty data analysts to ensure that strategic insights are accurate, relevant and impactful.

Introducing Warranty Analytics 2.0

Warranty Analytics 2.0 is a set of solutions that combine warranty-specific visual analytics software with dedicated warranty data analysts that organizations can "rent" on a low cost, month-to-month basis. Organizations who typically don't have in-house analytical resources at their fingertips will find this analytics-as-a-service business model extremely valuable.

If warranty or quality teams are looking to build custom, predictive models to answer more high-impact strategic questions, they can hire a team of data analysts and statistical modelers -- highly trained in warranty data -- to build them. Warranty Analytics 2.0 custom solutions can be delivered on a project-by-project basis, or ongoing, as a dedicated warranty analytics arm.

About Dan Hulkower, SVP of Business Development, After, Inc.

Hulkower joined After, Inc. in January of 2015 as SVP, Business Development and is focused on broadening the company's reach into our current markets as well as new industries. He has a 30-year history of driving profitable growth in multiple industries.