Business Intelligence/ Decision Models RFM Analysis

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

Recency, Frequency, and Monetary (RFM) Analysis According to Hughes! Always improves response and profits Better than any demographic model The most powerful segmentation method for predicting response

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

Response by Recency Quintile

Response by Frequency Quintile

Response by Monetary Quintile

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

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

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

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,

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 Ex. RFM 421 = Score 51

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

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

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

Selecting every Nth case 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

Result of Test for 30,000

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

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

Break Even Index (BEI) BE: .0167 set to 0 or 100 as a reference point Ex. Assume Response = .025 for a given cell If BEI set to 0 BEI = [(Resp – BE)/BE] * 100 Ex. [(.025 - .0167)/.0167]*100 Ex. 49.7 rounded 50 If BEI set to 100 BEI = [Resp/BE] * 100 Ex. [.025/.0167] * 100 = 150

Profit from Test Program

Test Vs Rollout Response Rates Test = Lines Rollout = Bars

Test, Full File & RFM Selects Compared Response Rate 1.34% 1.17% 2.76% Responses 402 23,412 15,540 Net Revenue $16,080 $936,480 $621,596 Quantities 30,000 2,001,585 563,040 Fixed Costs $16,500 $1,100,581 $309,672 Profits -$420 -$164,101 $311,924

RFM Procedure Transaction file Customer file Run RFM on transaction file Create an new RFM dataset Merge with customer file Customer file File is already prepared Run RFM on time since last purchase, number of purchases, and money spent

SPSS Means Procedure Compare Means  Means Copy output table to Excel 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 and economics

Selected RFM $ 62,162 $ 21,920 -$ 40,242 RFM score Mean N Cost Income 515 .46 547 $ 410 $ 1,008 512 .42 1097 $ 823 $ 1,860 514 530 $ 398 $ 892 513 .41 185 $ 139 $ 304 511 .38 318 $ 239 $ 488 411 .34 302 $ 227 $ 416 415 565 $ 424 $ 760 414 .32 561 $ 421 $ 708 412 .31 1205 $ 904 $ 1,516 413 .30 $ 224 312 .28 1543 $ 1,157 $ 1,704 311 .27 527 $ 395 $ 576 314 724 $ 543 $ 768 315 .25 749 $ 562 $ 744 313 .24 132 $ 99 $ 128 211 .21 1053 $ 790 213 .18 1318 $ 7,667 $ 13,000 $ 5,333 U. Cost U. Profit BE 0.75 4 0.1875 Total .07 82882 $ 62,162 $ 21,920 -$ 40,242

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

Caution about RFM Customer reach File fatigue Use RFM for testing Use it to identify your best customers Objectives? Profit, ROI, Reach

RFM Customer File

RFM Customer File

RFM Customer File

RFM Customer File

RFM Customer File

RFM Customer File

RFM Sort Analyze Compare Means Means

RFM Sort, Import into Excel 535 .1219 320 541 .0719 320 542 .0688 320 543 .0594 320 544 .0656 320 545 .0500 320 551 .0219 320 552 .0188 320 553 .0281 320 554 .0250 320 555 .0281 320 Total .1484 39999 RFM Mean N 111 .1975 319 112 .1937 320 113 .1531 320 114 .1438 320 115 .1531 320 121 .0562 320 122 .0281 320 123 .0438 320 124 .0438 320 125 .0344 320 131 .0094 320 ………..…

RFM Economics Ex. Catalogue Fixed Cost $4.00 Gross Profit $20.00 BE $4 / $20 = .20 Sent to all 39,999 Cost $159,996 Resp .1484 5,936 Gross $118,720 - $41,276 Sent to > .20 10,240 $40,960 Buyers 4,367 $87,340 $46,380

RFM Economics Email Fixed Cost $0.02 Unsub. .025 Gross Profit $20.00 Worst Case .050 BE .02/20 = .001 CLV $100 Sent to all 39,999 Cost $800 Resp .1484 5,936 Gross $118,720 Cust. Loss 20,000 $2,000,000 - $1,882,100 Sent to > .20 10,240 $205 Buyers 4,367 $87,340 256 $25,600 $61,535