Download Data Mining - an interactive approach to customer database segmentation using recency-frequency-value model

Data Mining - an interactive approach to
customer database segmentation using a
recency-frequency-value model
Stacie Maxey- Portman Building Society
• Segmentation: a method of organising the customer
database so that appropriate customers can be
matched to specific products
• Clustering: a technique used to place customers into
groups (treatment segments) suggested by the data
Steps to achieve an effective clustering solution
•
•
•
•
•
•
decide appropriate variables to use for segmentation
filter the data
Standardise the variables
choose an appropriate number of clusters
run proc fastclus
evaluate the clusters; map results back to the original
database, geodemographics
• filter the data
• feed the clusters into proc cluster
• produce customer profiles of the clusters
Decide appropriate variables to use for
segmentation
• objectives
• goals
• appropriate variables
• reasonably independent variables
• establish a feasible model
• evaluate the model
The Recency-Frequency-Value Model
• Recency
• Frequency
• Value
• Age
Filter the data
• Averaging
• Extreme values
• To evaluate: Access descriptor within SAS
• View with a subset clause
Standardise the variables
Data values in the
Recency-Frequency-Value
model:
Recency: date
Frequency: integer
Value: currency
Age: number of years
Syntax
proc standard
mean=0
std=1
out=stan;
var recent frequent
balance age;
run;
Choose an appropriate number of
clusters
• A workable number of treatment segments for
practical purposes
• You can alter the number if your first choice is
inappropriate
• A reasonable number of customers should
fall into each segment; there should be no
giant cluster
Proc fastclus
Syntax
• Efficient tool to cluster
large databases
• User driven, you decide
the number of clusters
to output
proc fastclus
data=stan
maxc=10
maxiter=99
out=preclus;
run;
Fastclus Results
Cluster
1
2
3
4
5
6
7
8
9
10
Quantity
16825
17419
69618
146737
38206
139798
177393
164033
43801
57920
%
2%
2%
8%
17%
4%
16%
20%
19%
5%
7%
Evaluate the clusters
Useful Information derived from investor database:
Average number of accounts held
Product portfolio
Product map
%Instant Access ownership
Initial Fastclus Results
Cluster
6
7
8
3
4
5
10
1
2
9
%
16%
20%
19%
8%
17%
4%
7%
2%
2%
5%
Recency Frequency
M
L
L
L
L
L
H
M
M
L
H
H
H
M
M
M
H
H
M
M
Value
L
L
L
M
M
M
M
H
H
H
Age
L
L
H
L
H
M
H
H
H
H
Additional Variables
Cluster
6
7
8
3
4
5
10
1
2
9
%
16%
20%
19%
8%
17%
4%
7%
2%
2%
5%
IA%
M
H
H
H
M
H
M
L
L
L
NumAccts Portfolio
L
M
L
L
L
L
M
L
M
M
M
M
M
M
H
H
H
H
H
H
Cluster Summary
• 3 ‘macro clusters’ can
be identified
• Marketing strategy
• Database audit
High Value
Investors
9%
Medium
Value
Investors
36%
Low Value
Investors
55%
Geodemographic Analysis
• Requires only the postcodes of the
customers within the clusters
• Can provide an independent cluster
analysis
• measurement: penetration by count
Filter the data
The way in which data is filtered depends on the
marketing objective
Options:
• cluster one specific cluster or internal cluster
• cluster those customers who were filtered out of the
database cluster
• establish ‘macro clusters’
Proc cluster
Syntax
• Optional step
• can be used to confirm
initial analysis
• extremely fast to run on
fastclus output
proc cluster
data=mean
method=centroid;
var balance recency
frequent
age;
copy preclus;
run;
Customer Profiles
Value of customers to the Society
Customer Life Stage
Product portfolio
Propensity to purchase - segmentation
Propensity to purchase - geodemographics
Segmentation Results
• 10 distinct clusters
• 3 main customer groups
• database audits
• mailfile creation
• special projects
• pre- and post- campaign analyses
Conclusions
• segmentation is a powerful tool
• segmentation is not a complex procedure
• proc fastclus
• customer ‘scoring’
• customer life stage
• customer behaviour
• propensity to purchase models
Future Recommendations
• Detailed descriptions of clusters
• segment ‘outliers’
• segment mortgagor database
• marketing objectives
• migration monitoring
• use the segmentation