Cardholder Attrition Analysis and Treatments Framework

Wei Lin, Bill Schmarzo, and Joshua Siegel accept the Best of Big Data Award at the 2016 Knowledge Sharing Celebration. L-R: Debi Graci, Knowledge Sharing Program; Joe Milardo, Sr. Director, EMC Education Services; Wei Lin, Chief Data Scientist and Principal Consultant, Professional Services, EMC; Joshua Siegel, Director, Big Data Transformation, Professional Services, EMC; Jennifer St. Hill, Vice President, EMC Education Services; Bill Schmarzo, CTO, Global Services Big Data Practice, EMC.

As competition heats up for customers, companies regardless of industry are focused on customer satisfaction and retention. Specifically, companies with transaction-level data are attempting to use that history to calculate attrition risk for their current customers and intervene before those customers go elsewhere. Payment card companies make up one industry with detailed transaction level data – perfect for big data analytics.

In this Knowledge Sharing article—awarded Best of Big Data in the 2016 Knowledge Sharing Competition—Wei Lin, Joshua Siegel, and Bill Schmarzo explore how to use a Big Data approach to perform Individualized Cardholder Attrition Analysis to maximize profit by Churn Reduction. The article focuses on Issuer Processors who process transactions for Issuing Banks.

Read the full article here.

Watch an interview with Wei Lin, Bill Schmarzo, and Joshua Siegel discussing their article here.