Business Intelligence/ Decision Models

1. Business Intelligence/Decision Models Week 5 RFM Analysis

2. Outline • Explain how RFM works, and how you can use it to improve response rates • SPSS RFM • With transaction files • With customer files • Use of RFM with Target Variable

3. Recency, Frequency, and Monetary (RFM) Analysis According to Hughes: • Used for marketing to customers • Always improves response and profits • Better than any demographic model • The most powerful segmentation method for predicting response

4. Old-fashioned RFM? • Never assume a CHAID program or even a regression model will outperform an old-fashioned RFM… (David Shepard)

5. Response by Recency Quintile

6. Response by Frequency Quintile

7. Response by Monetary Quintile

8. Big Ticket: Response to $5,000 CD Offer by Monetary Percentage of households promoted who purchased 2 1.68 1.5 1.17 1 0.88 0.66 0.5 0.32 0 5 4 3 2 1 Monetary Quintile

9. F 5 M 35 335 4 34 334 3 33 333 332 32 2 331 31 Twenty-five sorts 1 Five Sorts Database One Sort RFM Code Construction 1 R

10. RFM Code Construction 2 R F M 5 5 5 4 4 4 3 3 3 2 2 2 1 1 1 DB 1st Sort DB 2nd Sort DB 3rd Sort

11. RFM Code Construction 3 • Predefined subjective breaks: • R = < 2 wks, 2-6 wks, 7-8 wks, and > 12 wks • F = 10, 8, 6, 3, 1 • M = > $500, $300-500, $200-300, $100-200, < $200,

12. RFM Code Construction 4 • R = 5, 4, 3, 2, 1 @ weight = 10 • F = 5, 4, 3, 2, 1 @ weight = 5 • M =5, 4, 3, 2, 1 @ weight = 1 • Weighted sum • Score =  xiwi

13. F 5 M 35 335 4 34 334 3 33 333 332 32 2 331 31 Twenty-five sorts 1 Five Sorts Database One Sort Let’s stick with this one! R

14. Break Even Response Rate • BE = Unit Cost / Unit Profit • Example: • Unit cost = .55 • Unit profit = $33 • Thus $.55 / $33 = 0.0167 or 1.67%

15. Number of Cells • 5 x 5 x 5 = 125 cells • How many individuals per cell so that response is significant? • Minimum  4 / BE • Ex. 4 / .0167 = 240 individuals per cell • # of customers +/- 30,000 • Use Common sense: 5x4x4, or 5x4x3, or 5x2x2, or 4x2x3

16. Systematic Sampling: Selecting every Nth case Customer Database 300,000 Records For Nth by 10, select every tenth record. Nth Result will be a statistical replica of the database 30,000 Records

17. Result of Test Mailing to 30,000

18. Break Even Index (BEI) • BE = .01375 to be set at 0 or 100 • Assume Response = .025 • If BEI set to 0 • BEI = [(Resp – BE)/BE] * 100 • Ex. [(.025 - .01375)/.01375]*100 • Ex. 81.82  82 • If BEI set to 100 • BEI = [Resp/BE] * 100 • Ex. [.025/.01375] * 100 = 181.82  182

19. Case Study • # of customers 2,100,000 • Test with 30,000 • Left with 2,070,000 • If 555 RFM  125 cells • 16,560 customers per cell

20. Test Response Rate by RFM Cell 34/125 cells above BE

21. Profit from Test Mailing

22. Test, Full File & RFM Selects Compared 16,560 per cell X 34 cells = 563,040 Vs. 554,182 in textbook

23. SPSS Procedure • Compare Means  Means • DV  Response Variable (0/1) • IV  RFM (Means and N) • Copy output table to Excel • Sort all columns by mean response • Descending order • Determine BE

24. Selected RFM

25. Bits and Pieces • Recency with continuity: • Change in buying decisions • Frequency? • Purchases in year or # of items • Transactions in a year • # of visits, # of calls • Profitability? • Tests and Rollouts

26. Test Vs Rollout Response Rates Test = Lines Rollout = Bars

27. WhenNOT to use RFM • If you use it all the time, half your customers will never hear from you; they will be lost • The others will suffer from File Fatigue • Use it sparingly; when you need a boost • Use it to identify your best customers • Don’t go hog wild!

28. Topline from Hughes’ Reading

29. Top line from Chapter 6-1RFM Analysis • RFM has been used for more than 60 years. It is used only for customers, not prospects. • It tells you which direct mail customers not to mail to because their response rates will be below breakeven. • RFM does not work well for e-mail, because the cost of mailing is so low, you might as well mail them all. The formula for the breakeven response rate for a group of direct mail customers is: BE = Unit Cost/Unit Profit  • In recency, the top quintile (5) has response rates very much higher than the next lower quintile (4). • Every RFM cell has exactly the same number of customers as any other RFM cell. • RFM is cheap to use. You do not need to know anything about statistics. • Frequency is not as predictive as recency. The lowest quintile in frequency usually has a surprisingly large response rate because is contains lots of recent buyers.

30. Top line from Chapter 6-2RFM Analysis • For low ticket items (less than $120), frequency is seldom predictive. It is very predictive of responses for big ticket items. • For typical RFM, you sort the file 31 times and end up with 125 RFM cells from 555 down to 111. • For an RFM test, you use an Nth which is an exact statistical replica of your main file. • Once you have done a test mailing, you know which RFM cells were profitable. You mail to only those cells on the rollout. • There is free software for a trial of RFM. It is located at www.dbmarketing.com. • For a business-to-business mailing, 125 cells is usually too many. Cut it down by reducing frequency or monetary, such as 5 × 2 × 2 = 20. • RFM is the basis of any good predictive model.

31. Top line from Chapter 6-3RFM Analysis • Don’t use RFM all the time. Use it when you want to make a good impression. • Frequency is often difficult to quantify. • RFM does not measure LTV or profitability. • Rollout response rates do not do as well as test response rates. Why? Because marketers cannot leave well enough alone.