Hi recency
Hi frequency
Hi monetary value

Scoring scheme:
Buy interval
<1 month = 5
1-3 months = 4
3-6 months = 3
6-9 months = 2
>9 months = 1
frequency
>40 = 5
30-40 = 4
20-29 = 3
10-19 = 2
<10 = 1 total buy
>$50k = 5
$20k-$50k = 4
$10k-$20k = 3
$5k-$10k = 2
<$5k = 1

B
C
D
E
F
G
H
Customer
ID
A last buy* frequency total $ RFM score
05/08 12 6k 422 422
01/08
11/07
07/08
01/07
03/08
05/07
07/08
18
35
1
6
40
5
20
9k
40k
1k
5k
90k
322
244
511
122
355
322
442
511
211
553
2k 111 111
9k 532 532
*vs. now, 07/08
Convention to rearr
Rank low= recovery
Most valuable
F
H
D
C
A
B
E
G

1) Data preparation
Old variable recoded (or create a new variable)
Recency
If
Last order placed w/in past 3 months:
Last order w/in past 6 months
Last order w/in past 9 months
Last order w/in past year
Last order w/in past 2 years then:
20 points
10
5
3
1
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 value 2
3) Multiple variables by weights and sum to get “final weighted RFM scores” for targeting good customers

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 months 10
Last order w/in past 9 months
Last order w/in past year
Last order w/in past 2 years
5
3
1
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:
Frequency
Monetary value
Step 3) Multiple variables by weights and sum to get “final weighted RFM scores” for targeting good customers
5
3
2