Chapter 6: Segmentation
6.1 Introduction
6.2 Cluster Segmentation
6.3 Market Basket Analysis
6.4 Recommended Reading
1
Chapter 6: Segmentation
6.1 Introduction
6.2 Cluster Segmentation
6.3 Market Basket Analysis
6.4 Recommended Reading
2
Objectives


3
Define pattern discovery.
Name some of the statistical and analytical techniques
that are useful for pattern discovery.
Pattern Discovery
The Essence of Data Mining?
“…the discovery of
interesting, unexpected, or
valuable structures in large
data sets.”
– David Hand
4
...
Pattern Discovery
The Essence of Data Mining?
“…the discovery of
interesting, unexpected, or
valuable structures in large
data sets.”
– David Hand
5
“If you’ve got terabytes of
data, and you’re relying on
data mining to find
interesting things in there for
you, you’ve lost before
you’ve even begun.”
– Herb Edelstein
Pattern Discovery
Are there demographic characteristics to identify people
who are more likely to preorder books at a premium price
point?
What types of people are most likely to be at the food
court on a Saturday afternoon? Is that a good time to
have a promotional activity for children (and their parents)
or for teens?
What sorts of complaints are most common for
different call centers?
If a customer bought product A
this week, what is that customer
most likely to buy next?
6
Pattern Discovery Caution

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



7
Poor data quality
Opportunity
Intervention
Separability
Obviousness
Nonstationarity
...
Pattern Discovery Caution






8
Poor data quality
Opportunity
Intervention
Separability
Obviousness
Nonstationarity
Pattern Discovery Applications
Data reduction
Novelty detection
Profiling
Market basket analysis
A C
B
9
Sequence analysis
Pattern Discovery Tools
In this chapter, you learn two techniques for unsupervised
pattern discovery:
Cluster Segmentation and Profiling
Market Basket Analysis, Sequence Analysis
10
Chapter 6: Segmentation
6.1 Introduction
6.2 Cluster Segmentation
6.3 Market Basket Analysis
6.4 Recommended Reading
11
Objectives




12
Describe several examples of segmentation.
Explain k-means clustering.
Explain the Ward method in SAS Enterprise Miner.
Perform cluster segmentation and generate profiles of
the segments using SAS Enterprise Miner.
Unsupervised Classification
inputs
grouping
cluster 1
cluster 2
cluster 3
cluster 1
cluster 2
Unsupervised classification:
grouping of cases based on
similarities in input values
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Segmentation for Customer Types
You want to identify segments. While you have thousands
of customers, there are really only a handful of major
types into which most of your customers can be grouped.
 Bargain hunter
 Man/woman on a mission
 Impulse shopper
 Weary parent
 DINK (dual income, no kids)
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Segmentation for Fraud Detection
Most fraudulent customer activity is difficult to identify by a
single variable. Are there unusual combinations of
behaviors that can help identify criminal activity or fraud?
Spending $250.00 on shoes is not unusual.
An online purchase by Dan Kelly is not unusual.
Purchases in New York by Dan Kelly are not unusual
although Dan lives in Raleigh.
Dan Kelly buying $250.00 in
shoes online while he is in
New York; that is unusual.
Fraud alert!
15
Segmentation for Store Location
You want to open new grocery stores in the U.S.
based on demographics. Where should you locate
the following types of new stores?
 low-end budget grocery stores
 small boutique grocery stores
 large full-service supermarkets
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Classifying Fashion Trends
Based on the four styles of pants that your customers can
purchase, can you identify stores as serving similar
fashion types?
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country-club dresser
fashion trendsetter
comfort kick-back dresser
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Re-assign cases.
6. Repeat steps 4 and 5
until convergence.
18
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Re-assign cases.
6. Repeat steps 4 and 5
until convergence.
19
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
20
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
21
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
22
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
23
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
24
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
25
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
26
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
27
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
28
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
29
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
30
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
31
...
k-Means Clustering Algorithm
Training Data
1. Select inputs.
2. Select k cluster centers.
3. Assign cases to closest
center.
4. Update cluster centers.
5. Reassign cases.
6. Repeat steps 4 and 5
until convergence.
32
...
Segmentation Analysis
Training Data
When no clusters exist,
use the k-means
algorithm to partition
cases into contiguous
groups.
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6.01 Poll
If you ask SAS Enterprise Miner to recover five clusters
but there are not five distinct groups in the data, you do
not get a five-cluster solution. You only get as many
clusters as there are true groupings to find in the data.
 Yes
 No
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6.01 Poll – Correct Answer
If you ask SAS Enterprise Miner to recover five clusters
but there are not five distinct groups in the data, you do
not get a five-cluster solution. You only get as many
clusters as there are true groupings to find in the data.
 Yes
 No
35
What Value of k to Use
The number of seeds, k, typically translates to the final
number of clusters that are obtained. The choice of k can
be made using a variety of methods.
 Subject-matter knowledge (There
are most likely five groups.)
 Convenience (It is convenient to
market to three to four groups.)
 Constraints (You have six products
and need six segments.)
 Arbitrarily (Always pick 20.)
 Based on the data (Ward’s method)
36
What Value of k to Use
The number of seeds, k, typically translates to the final
number of clusters that are obtained. The choice of k can
be made using a variety of methods.
 Subject-matter knowledge (There
are most likely five groups.)
 Convenience (It is convenient to
market to three to four groups.)
 Constraints (You have six products
and need six segments.)
 Arbitrarily (Always pick 20.)
 Based on the data (Ward’s method)
37
Ward’s Method in SAS Enterprise Miner
Ward’s method is an algorithm for hierarchical cluster
analysis.
In this method, each observation is
considered a cluster, and the clusters
are hierarchically joined, based on
minimizing the ratio of the variation
between clusters to the variation
within clusters.
Based on a statistical analysis, the
number of clusters is selected.
This number of clusters is used for
k-means cluster analysis.
38
Ward’s Method in SAS Enterprise Miner
SAS Enterprise Miner uses an empirical approach to
select the number for k, based on a preliminary analysis
using Ward’s clustering in three steps:
1. Preliminary k-means clustering on
original data to save many cluster
centroids
2. Ward’s hierarchical clustering on
saved cluster centroids to determine
the ideal value for k
3. k-means clustering on the original
data set using k from step 2
39
Step 1
Many seeds (by default, 50) are chosen from the original
training data, and an initial k-means clustering is
performed. The means (centroids) of the 50 preliminary
clusters are saved to a data set and input to step 2.
40
Step 2
Ward’s method performs hierarchical clustering on the
preliminary clusters (the centroids saved in step 1). At
each step (k clusters, k-1 clusters, k-2 clusters, and so
on), the cubic clustering criterion statistic (CCC) is saved
to a data set. The final number of clusters is selected
based on the CCC with the following conditions:
 The final number of clusters must be greater than or
equal to the minimum number of clusters specified in
the Selection Criteria properties.
 The final number of clusters must have a CCC greater
than the CCC threshold in the Selection Criteria
properties.
41
Step 3
The number of clusters determined in step 2 provides the
value for k in a k-means clustering of the original training
data set.
 Ideally, the number of clusters should correspond to a
peak in the CCC statistic.
 When there is no peak in the CCC, the resulting
number of clusters might be suspect.
 When the CCC for the selected k is negative, the
resulting number of clusters might be suspect.
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6.02 Multiple Choice Poll
You should use a clustering solution that corresponds to
the _____________ of the CCC.
a. maximum
b. minimum
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6.02 Multiple Choice Poll – Correct Answer
You should use a clustering solution that corresponds to
the _____________ of the CCC.
a. maximum
b. minimum
44
Grocery Store Case Study
Analysis goal:
Where should you open new grocery store locations?
Group geographic regions into segments based on
income, household size, and population density.
Analysis plan:

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45
Select and transform segmentation inputs.
Select the number of segments to create.
Create segments with the Cluster tool.
Interpret the segments.
Segmenting Census Data
Grocery Store Case Study
Task: Use tools and techniques in
SAS Enterprise Miner for cluster
and segmentation analysis.
46
Idea Exchange
Do any of the segments seem to map onto the types of
stores that the grocery store company is considering
(budget, small boutique, large full-service supermarket)?
Explore different numbers of clusters for the solution. Do
your conclusions change?
47
Bank Marketing Segmentation Case Study
Analysis goal:
Who is the best target for a cross-sell/up-sell
campaign?
A consumer bank wants to segment its customers
based on historic usage patterns to identify those who
might benefit from new product offerings.
Analysis plan:
1. Perform cluster analysis.
2. Select the number of
segments to create.
3. Interpret the segments.
4. Deploy the segmentation rules
with scoring code.
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Accessing and Assaying
the Data
Bank Marketing Segmentation Case Study
Task: Use tools and techniques in
SAS Enterprise Miner for cluster
and segmentation analysis.
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Idea Exchange
50
In the examples from this course, you have performed
cluster analysis with a small number of variables.
However, in real applications, it is common that there are
many variables you could use in clustering. Cluster
analysis does not perform well with a large number of
variables, as it becomes increasingly difficult to detect
differences among groups as the number of variables
increases.
Consider an example in which you
might use many variables, such as
questionnaire items, demographics,
and purchasing behavior.
What are some strategies you would take
to reduce from a large number of variables
to something more manageable?
Exercise
This exercise reinforces the concepts discussed
previously.
51
Chapter 6: Segmentation
6.1 Introduction
6.2 Cluster Segmentation
6.3 Market Basket Analysis
6.4 Recommended Reading
52
Objectives



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53
Describe several examples where association analysis
is useful.
Distinguish between two types of association analysis:
market basket analysis and sequence analysis.
Define support and confidence in the context of
association analysis.
Perform market basket analysis and sequence
analysis in SAS Enterprise Miner.
Market Baskets for Grocery Groupings
A classic application of market basket analysis addresses
this question:
Which items are likely to be purchased together?
 If product A and product B often go together, then
placing a more expensive alternative to B near the
display for A can create an up-sell opportunity.
 If product A and B are often
purchased together, putting
them on sale at different
times can drive purchases
continually.
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Market Baskets for Hardware
A hardware store has 25 shopping aisles. Which products
should be grouped near one another?
 Key-cutting near paint or near door hardware?
 Lawn ornaments near garden or near indoor
decorative ornaments?
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Sequence Analysis for Training
Related to market basket analysis is sequence analysis,
which looks at which items go together from one time to
another. This can create opportunity for best-next-offer
campaigns.
 After a student takes the SAS Programming 2 course,
which course is most likely to be next?
 After a student takes the Statistics 1 course and the
programming certification
exam, which course is
most likely to be next?
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Market Basket Analysis
A B C
A C D
B C D
A D E
B C E
Rules: X  Y = “X implies Y”
C  A = “Given C, how often does A occur?”
A  C = “Given A, how often does C occur?”
Strength of association is measured by
support and confidence.
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Market Basket Analysis
A B C
A C D
B C D
A D E
Support (A  B) =
transactions containing every item in A and B
all transactions
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B C E
Market Basket Analysis
A B C
A C D
B C D
A D E
Confidence (A  B) =
transactions containing every item in A and B
transactions containing the items in A
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B C E
Market Basket Analysis
A B C
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A C D
B C D
A D E
B C E
Rule
Support
Confidence
AD
CA
AC
B&CD
2/5
2/5
2/5
1/5
2/3
2/4
2/3
1/3
Implication?
Checking Account
No
Yes
No
500
3500
4,000
Yes
1000
5000
6,000
Savings
Account
Support(SVG  CK) = 50%
Confidence(SVG  CK) = 83%
Expected Confidence(SVG  CK) = 85%
Lift(SVG  CK) = 0.83/0.85 < 1
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10,000
Barbie Doll  Candy
1.
2.
3.
4.
5.
6.
7.
Put them closer together in the store.
Put them far apart in the store.
Package candy bars with the dolls.
Package Barbie + candy + poorly selling item.
Raise the price on one, and lower it on the other.
Offer Barbie accessories for proofs of purchase.
Do not advertise candy
and Barbie together.
8. Offer candies in the shape
of a Barbie doll.
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Data Capacity
D
A
63
A
A
A
B C
B B
A
D
A
Banking Services Case Study
Analysis goal:
Explore associations between retail banking services
used by customers.
Analysis plan:
 Create an association data source.
 Run an association analysis.
 Interpret the association rules.
 Run a sequence analysis.
 Interpret the sequence rules.
64
Performing Association
Analysis: Market Basket
Analysis
Banking Services Case Study
Task: Perform market basket analysis on the
banking data.
65
Idea Exchange
Based on the findings from the bank data market basket
analysis, what are some business decisions you might
recommend? List five possible actionable decisions from
the analysis.
66
Performing Association
Analysis: Sequence Analysis
Banking Services Case Study
Task: Perform sequence analysis on the
banking data.
67
Idea Exchange
Consider the actionable decisions that you discussed for
market basket analysis. Based on the findings from the
bank data sequence analysis and your understanding of
the order in which products tend to occur together, how
would you update those decisions?
68
Pattern Discovery Tools: Review
Generate clusters and perform segmentation
using automatic settings and with user-defined
settings.
Compare within-segment distributions of
selected inputs to overall distributions. This
helps you understand segment definition.
Conduct market basket and sequence analysis
on transactions data. A data source must have
one target, one ID, and (if desired) one
sequence variable in the data source.
69
Idea Exchange
Think about products that you purchase together.
 Name several pairs or groups of items that are often
purchased together, or behaviors that tend to occur
together. Now suppose that these combinations of
products are common. What actionable business
decisions could be made knowing these associations?
 Name several pairs or groups of items that you
purchase in sequence, or behaviors
that you engage in sequentially. Now
suppose that these sequences of
behaviors are common. What actionable
business decisions could be made
knowing these sequences.
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Exercise
This exercise reinforces the concepts discussed
previously.
71
Chapter 6: Segmentation
6.1 Introduction
6.2 Cluster Segmentation
6.3 Market Basket Analysis
6.4 Recommended Reading
72
Recommended Reading
Gulati, Ranjay. “Inside Best Buy’s Customer-Centric
Strategy.” Harvard Business Review blogs. April 12, 2010.
http://blogs.hbr.org/hbsfaculty/2010/04/inside-best-buys-customer-cent.html
Best Buy has implemented a customer segmentation approach that has set
the company apart from its competition. This blog provides a summary of
Best Buy’s customer-centric approach driven by analytics.
73
Recommended Reading
May, Thornton. 2010. The New Know: Innovation
Powered by Analytics. New York: Wiley.
 Chapters 4 and 5
Further discussion of analysts in the workplace, the importance of
relationships, and the analysis of social network data.
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Recommended Reading
Ketchen, David J. and Christopher L. Shook. 1996. “The
Application of Cluster Analysis in Strategic Management
Research: An Analysis and Critique.” Strategic Management
Journal 17(6):441-458.
 available on JSTOR: www.jstor.org/stable/2486927
Optional reading
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