MKT 700
Business Intelligence and
Decision Models
Week 6:
Segmentation and Cluster Analysis
What have we seen so far?
Data Architecture, CRISP and Preparation
1. What is Business intelligence and
database marketing
2. Database infrastructure
3. Data preparation and transformation
Customer Classification
4. Customer lifetime value
5. RFM
6. Customer Clustering
Where are we going from
now?
Reading week
7. Mid-Term
Predictive Modeling
8.
Customers’ Profiling/Decision tree
9. …Decision tree (CHAID/CRT)
10. Customers’ Propensity to buy
11. …Logistic regression
12. Campaign Metrics and Testing
Outline for Today

Clustering:
Clustering and Segmentation
 B2C and B2B
 Clustering theory


Lab
Clusters and Segments
(Chap 10)

Differences between clusters and
segments
Learning segmentation
 Dynamic segmentation

Customers are not equal


Different needs and preferences
Different responses to marketing efforts


Different marketing treatments


Product usage, product attributes,
communication, marketing channels
Packages, prices, copy strategy,
communication and sales channels
Remember the basic marketing rules
about segmentation (p. 223)
Status Levels and
Segments
Status Levels
Marketing Segments
Business
Customers
Gold
Affluent
Retired
Silver
Young
Singles
Families
With Kids
Customer
Bronze
Bargain
Shoppers
Occasional
Buyers
Marketing
Staff
Consumer Segmentation
Taxonomy





Product usage/loyalty
Buying behaviour
Preferred communication channel
Family life cycle (stage in life)
Lifestyle (personal values)
Data Sources for
Segmentation

Internal



Transactions
Surveys & Customer Service
External (Data overlays)




Lists
Census
Taxfiler
Geocoding
Geo-Segmentation in CDA
Birds of a feather f___k together…

Environics (Prizm)


http://www.environicsanalytics.ca/prizm-c2-clusterlookup
Generation5 (Mosaic)
• http://www.generation5.ca

Manifold:


http://www.manifolddatamining.com/html/lifestyle/lifes
tyle171.htm
Pitney-Bowes (Mapinfo)

http://www.utahbluemedia.com/pbbi/psyte/psyteCanad
a.html
B2B Segmentation
Taxonomy
Firm size (employees, sales)
 Industry (SIC, NAICS)
 Buying process
 Value within finished product
 Usage (Production/Maintenance)
 Order size and Frequency
 Expectations

Clustering

Measuring distances (differences) or
proximities (similarities) between
subjects
Measuring distances
(two dimensions, x and y)
A
B
C
17
Measuring distances
(two dimensions)
dac2 = (dx2 + dy2)
A
B
C
dac2 = (di)2
dac = [(di)2]1/2
18
Measuring distances
(two dimensions)
D(b,a)
A
B
D(a,c)
D(b,c)
C
19
Distances between US cities
ATL
CHI
DEN
HOU
LA
MIA
NY
SF
SEA
DC
0
587
1212
701
1936
604
748
2139
2182
543
Chicago
587
0
920
940
1745
1188
713
1858
1737
597
Denver
1212
920
0
879
831
1726
1631
949
1021
1494
701
940
879
0
1374
968
1420
1645
1891
1220
1936
1745
831
1374
0
2339
2451
347
959
2300
Miami
604
1188
1726
968
2339
0
1092
2594
2734
923
New_York
748
713
1631
1420
2451
1092
0
2571
2408
205
2139
2182
543
1858
1737
597
949
1021
1494
1645
1891
1220
347
959
2300
2594
2734
923
2571
2408
205
0
678
2442
678
0
2329
2442
2329
0
Atlanta
Houston
Los_Angeles
San_Francisco
Seattle
Washington_DC
Cluster Analysis Techniques

Hierarchical Clustering

Metric, small datasets
SPSS Hierarchical Clusters
Dendogram
SPSS Multidimensional Scaling
(Euclidean Distance)
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Atlanta
Chicago
Denver
Houston
Los_Angeles
Miami
New_York
San_Francisco
Seattle
Washington
1
2
.9575
.5090
-.6416
.2151
-1.6036
1.5101
1.4284
-1.8925
-1.7875
1.3051
-.1905
.4541
.0337
-.7631
-.5197
-.7752
.6914
-.1500
.7723
.4469
Euclidean distance mapping
Cluster Analysis Techniques

Hierarchical Clustering


K-mean Clustering


Metric variables, small datasets
Metric, large datasets
Two-Step Clustering

Metric/non-metric, large datasets,
optimal clustering
Cluster Analysis Techniques
See Chapter 23, SPSS Base Statistics for description of methods
Two-Step Cluster Tutorials

SPSS, Direct Marketing, Chapter 3 and 9
 Help  Case Studies  Direct Marketing 
Cluster Analysis
 File to be used: dmdata.sav

SPSS, Base Statistics, Chapter 24
 Analyze  Classifiy  Two-Step Cluster
 File to be used: Car_Sales.sav
 Help: “Show me”