Inspire 2014 – Southern States Cooperative: Improving Marketing ROI with Predictive Analytics

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In 2012, Southern States Cooperative entered into a case study with Alteryx to utilize the new predictive tools available. The goal was simple: utilize predictive analytics to improve the response …

In 2012, Southern States Cooperative entered into a case study with Alteryx to utilize the new predictive tools available. The goal was simple: utilize predictive analytics to improve the response rate and ROI on a direct mail campaign. A year later Southern States Cooperative has fully integrated predictive modeling into almost every direct mail effort that goes out and the results have been staggering. Response rates have increased from an average of 3% to over 10% after modeling and the ROI averages nearly 200%. In this presentation Greg will walk you through how they use predictive analytics to improve marketing ROI as well as other ways in which Southern States Cooperative understands their customers better with advanced analytical tools made possible through the use of Alteryx.

Greg Bucko, Manager of Customer Insights, Southern States Cooperative

More in: Technology , Business
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  • 1. #inspire14 Improving Marketing ROI with Predictive Analytics Greg Bucko Manager, Customer Insights Southern States Cooperative
  • 2. #inspire14#inspire14 Introduction Evolution Ape Like Approach Advanced Analytical Approach
  • 3. #inspire14#inspire14 Big Data vs. Usable Data X
  • 4. #inspire14#inspire14 Biggest Challenge? TECHNICAL CULTURAL
  • 5. #inspire14#inspire14 Marketing Channels Social Media TV Email Print Direct Mail Happy Customer!
  • 6. #inspire14#inspire14 Marketing Channels Social Media TV Email Print Direct Mail How do we improve Return on Investment? Happy Customer!
  • 7. #inspire14#inspire14 “The Shotgun Approach” • The budget dictated the size of the customer list • Sorted by spending and size • Wimpy return on investment Where We Were “The Targeted Approach” • Focus on propensity • Smaller lists • Bigger return Where We Wanted to Be Setting Goals
  • 8. #inspire14#inspire14 How We Got There Recognized the Need Worked With Dr. Dan on Case Study Began the “Buy-in” Process
  • 9. #inspire14#inspire14 How We Got There Built Our First Model Analyzed Results Communicated
  • 10. #inspire14#inspire14 How We Got There Iterated Communicated Iterated
  • 11. #inspire14#inspire14 Technical Aspects – Building Modeling Set Identified customers who responded to past direct mail Calculated statistics like recency, frequency, and monetary value for customers Appended Experian household demographic data
  • 12. #inspire14#inspire14 Technical Aspects – Modeling Looked for relationships in the data Oversampled the responders Tried different modeling types
  • 13. #inspire14#inspire14 Technical Aspects – Scoring Chose the best model Scored the database Sorted by score high to low
  • 14. #inspire14#inspire14 Technical Aspects – Analyze Results Calculate response rate, unique SKUs per transaction, ROI, etc. REPEAT
  • 15. #inspire14#inspire14 Cultural Aspects #1 Objection to Overcome: “Wait…what? We are no longer sending to all of our customers?”
  • 16. #inspire14#inspire14 The Answer… …is in the results. Drop 1 Drop 2 Drop 3 Sent To 11% 11% 24% Drop 1 Drop 2 Drop 3 Response Rate
  • 17. #inspire14#inspire14 The Answer… …is in the results. Drop 1 Drop 2 Drop 3 Margins 136% 116% 479% Drop 1 Drop 2 Drop 3 ROI We have now applied target modeling to all direct mail over the last year and have seen significant incremental lift.
  • 18. #inspire14#inspire14 In Summary Usable Data > Big Data Cultural Change > Technical Change Test. Adjust. Scale Up. Show Results. Repeat. Any Questions?
  • 19. #inspire14 THANK YOU!