1. Figure 4 Recency-Frequency-Monetary Value (RFM) Matrix Hi monetary value Hi frequency Hi recency

2. Example #1 RFM Scoring scheme: Buy intervalfrequencytotal buy 40 = 5>$50k = 5 1-3 months = 430-40 = 4$20k-$50k = 4 3-6 months = 320-29 = 3$10k-$20k = 3 6-9 months = 210-19 = 2$5k-$10k = 2 >9 months = 1<10 = 1<$5k = 1

3. Example #1 RFM Customer ID last buy* frequency total $ RFM score A05/08126k422422F B01/08189k322322H C11/073540k244442D D07/0811k511511C E01/0765k122211A F03/084090k355553B G05/0752k111111E H07/08209k532532G *vs. now, 07/08 Convention to rearr Rank low= recovery Most valuable

4. Example #2 RFM 1) Data preparation Old variablerecoded (or create a new variable) Recency If then: Last order placed w/in past 3 months:20 points Last order w/in past 6 months10 Last order w/in past 9 months5 Last order w/in past year3 Last order w/in past 2 years1 Frequency #purchases over past 2 years x 4 points, max = 20points (i.e., if #purchases x 4 >20, reset to =20) Monetary Value $spent over past 2 years x.10 (max = 20 points) 2) Weights (judgment) Recency score:5 Frequency 3 Monetary value2 3) Multiple variables by weights and sum to get “final weighted RFM scores” for targeting good customers

5. Example #2 RFM Step 1) Data preparation The old variables are recoded (creating new vars) Recency If then: Last order placed w/in past 3 months: 20 points Last order w/in past 6 months10 Last order w/in past 9 months5 Last order w/in past year 3 Last order w/in past 2 years1 Frequency #purchases over past 2 years x 4 points, max = 20points (i.e., if #purchases x 4 >20, reset to =20) Monetary Value $spent over past 2 years x.10 (max = 20 points) Step 2) Weights (judgment) Recency score:5 Frequency 3 Monetary value2 Step 3) Multiple variables by weights and sum to get “final weighted RFM scores” for targeting good customers