Teaching Descriptive Analytics, Customer Profiling and Clustering
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This presentation was given on October 12, 2013 at the Marketing EDGE Jacobs and Clevenger Casewriter's competition, where it received a Silver Award. The case outlines how to teach descriptive analytics, profiling and clustering for a fictional company.
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Teaching Descriptive Analytics, Customer Profiling and Clustering
- 1. ECB.com: Customer Profiling and Segmentation Dr. Debra Zahay-Blatz Aurora University Dr. Blodwen Tarter Golden Gate University Jacobs and Clevenger Casewriter’s Competition Presentation, McCormick Place 10/12/2013 Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 2. We Know Customer Information Management Breeds Success Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 3. Problem: How can we teach undergraduates/graduates the fundamentals of data analysis? Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 4. ECB.com Business Model • Sells coupons directly to consumers online (formerly a print service) • Coupons are used directly for types of entertainment • $25 coupon for a restaurant (the data used here) is only $10. Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 5. Problem Setup: • Data analytics team ponders segmentation • Points of view: two junior analysts • Existing customer segmentation: New, Engaged, Lapsed, Inactive (RFM) • Can we ‘beat the control’? • Are there other insights? Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 6. Student Goals •Understand how to read and interpret a data dictionary for a customer database. •Be able to run basic descriptive analysis on customer data and analyze it. •Be able to create a case summary report using a grouping variable, such as cluster membership. •Be able to use a TwoStep Cluster Analysis to create customer segments in SPSS and interpret the results. •Understand the basic principles of an outside segmentation scheme created by a data vendor and what the segments might look like. •(Optional): Be able to determine the effects of missing data in a customer database. •Understand the basics of what a Marketing Data Analyst does in his/her job. Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 7. Case Approaches • All or part of the case • Analysis or interpretation • Data vs. strategy • SPSS vs. Excel vs. just interpretation Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 8. Understanding a Data Dictionary is a Stand-Alone Lesson DATA DICTIONARY Variable Name Unique ID Transaction/Segment Data # days since last order # of Orders # of Certificates Revenue Tenure (in days) Lifecycletenurerecovery Average Order Value Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013 Description Unique Identifier for Customer # of days since last order (recency) in the last 3 years # of Orders in the last 3 years # of Certificates in the last 3 years Revenue in the last 3 years # of days since first order with ECB New = First order with ECB in the last 3 months Engaged = Last order was within the last 6 months (and not new) Lapsed = Last order was 6 - 12 months ago Inactive = Last order was over 12 months ago Revenue / # of Orders in the last 3 years
- 9. Q1: Typical Customer from Descriptives Descriptive Statistics Days since Last order N Minimum Maximum 60000 1 1095 No. of Orders 60000 1 961 2.75 5.373 No. of Certificats 60000 1 1043 8.45 13.629 Revenue 60000 $.00 $5,193.90 $26.1003 $41.53822 Tenure in Days 60000 10 2612 589.10 446.789 Avg. Order Value 60000 $.00 $920.00 $11.7441 $13.93730 No. of orders w/promotion No. of orders w/o promotion 60000 0 409 2.47 4.010 60000 0 44 .44 1.049 No. of orders w/ 80 percent off promotion 60000 0 156 1.53 2.530 No. of orders w/ 90 percent off promotion Valid N (listwise) 60000 0 15 .16 .501 60000 Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013 Mean Std. Deviation 386.57 295.136
- 10. Q2: Segment Behavior from Case Summary Case Summaries lifecycletenurerecovery Engaged N Dayssincelast Numberoford order ers 13521 13521 avgordervalu e Revenue 13521 13521 Mean N 26177 26177 26177 26177 673.87 1.81 $19.7037 $12.4295 N 15610 15610 15610 15610 263.46 2.62 $25.6383 $11.7408 N 4692 4692 4692 4692 41.93 1.38 $17.5838 $13.4102 N 60000 60000 60000 60000 Mean Total $9.8431 Mean New $41.9729 Mean Lapsed 5.19 Mean Inactive 92.07 386.57 2.75 $26.1003 $11.7441 Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 11. Q2: Try to “beat the control” Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 12. Q2: Use the exercise to explain clustering procedures Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 13. Q3: Data Vendors • Nielsen PRIZM (formerly Claritas) • Experian • Axciom • ESRI Tapestry Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
- 14. Bonus: 80/20 Rule Case Summaries: Current Segmentation Scheme, about 42% of revenue in last three years comes from Engaged and New Customers (30%, (13521+4692)/60000) under the old Segmentation Scheme Revenue lifecycletenurereco very Engaged % of Total Sum N Mean Sum 13521 $41.9729 $567,515.84 36.2% Inactive 26177 $19.7037 $515,782.78 32.9% Lapsed 15610 $25.6383 $400,214.35 25.6% 4692 $17.5838 $82,502.97 5.3% New Total Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013 60000 $26.1003 $1.57E6 100.0%
- 15. Contact information • Dr. Debra Zahay-Blatz • dzahayblatz@aurora.edu • Cell 630-300-8838 • Work 630-844-3825 • Dr. Blodwen Tarter • btarter@ggu.edu • Work 415-442-6587 Copyright by Debra Zahay-Blatz and Blodwen Tarter 2013
Hinweis der Redaktion
- Students know information mangement breeds success but not the techniques to do so