How Real-Time Decisions Yield Big-Time Benefits. John Lodmell, Vice President, Credit and Data, Advance America

Transcription

1 How Real-Time Decisions Yield Big-Time Benefits John Lodmell, Vice President, Credit and Data, Advance America

2 Improving Customer Experience and Loyalty through Real-Time Marketing John Lodmell VP Credit and Data Analytics Advance America

3 Outline Company Overview and Industry Review The Analytical Challenge The Technical Solution Results and Lessons Learned 3

4 COMPANY OVERVIEW AND INDUSTRY REVIEW 4

5 Who is Advance America? Advance America is one of the nation s leading providers of Short-Term Consumer Loans Products Include: Cash Advance/Payday Loan Short-Term Installment Loan Title Loan Line of Credit Prepaid Cards, MoneyGram, Tax Prep Advance America is a wholly owned subsidiary of Grupo Elektra, one of the largest Latin American providers of finance and specialty retailing 5 5

6 Who are our customers? Approximately 19 million households annually turn to the shortterm lending industry for help Customers are: Middle Income Educated Have Checking Account Advance America U.S. Census 2010 Customers* Average Age (years) Median Household Income $ 56,228 $ 50,046 Percentage Homeowners 56% 65% Percentage with High School Degree or Higher 94% 85% 6 * Based on approximately 1.1 million customer records from Jan 2013 to Dec 2013

7 We operate a distributed network of approximately 2400 retail centers in 29 states Operate Across the Country in Retail Locations Top States: 1) OH 2) CA 3) FL 4) TX 5) MI 7

8 THE ANALYTICAL CHALLENGE 8

9 We needed a systematic solution for determining the best offer based on each customer s situation Desires Challenges Consistency Fast Customers are in a hurry Find the best offer don t just say NO Encourage timely payment State Regulated Each state is a little different Multiple Point of Sale Systems Lots of Store Employees (5000+) Need tight controls Must enable test and learn 9

10 Finding the best offer meant balancing several factors Customer Attributes High Demand, Low Supply Offer Attributes Value to Customer The Sweet Spot Hardship (willing but not able to pay) Willing to Pay Able to Pay Fraud (able but not willing to pay) Profitable High Supply, Low Demand 10

11 THE TECHNICAL SOLUTION 11

12 We needed a stand-alone Decision Engine that could connect POS systems to data and analytics Old System-Rules Imbedded New System Rules Separate Business Rules Business Rules Business Rules Engine POS 1 POS 2 POS 1 POS 2 This allowed for consistency in decision-making across platforms and increased flexibility to change without changing application code 12

13 I turned to SAS to investigate how we could build a system like this that would meet our business needs 13

14 The SAS RTDM provided easy integration into our existing SAS Analytical Tools 14

15 RESULTS AND LESSONS LEARNED 15

16 Once we implemented RTDM, we had tremendous flexibility and consistency to deliver the best offer Graphical Decision Diagrams Easy to Segment Random Tests State Product Updates made by analytical team, not IT Able to use the analytics and modeling to drive the best offer in the retail center 16

17 Since going live in September 2013, we have been very pleased with the performance Extremely fast decisioning for both new and existing customers Rapid model and strategy changes Integrated with several additional platforms High Customer Satisfaction Fast, Flexible, Consistent, and Customers Still Happy 17

18 Looking back, there are several things that we learned along the way Lessons Learned Be aware of offer hopping Importance of managing customer expectations Move slowly and build momentum and buy-in There are many future opportunities to leverage this technology to improve the overall origination process 18

19 Questions? John Lodmell VP of Credit and Data Analytics Advance America 19

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