2. 2
Segmentation And The Fallacy Of Averages
Average Offer;
Average
Communications
Average Offer;
Average
Communications
“Just give me basic service”“Just give me basic service”“Give me all the bells and whistles”
“Give me all the bells and whistles”
“They are not really cutting
edge.”
“They are just after my
money. I really do not want
those services.”
4. 4
Segmentation Description Techniques
• A priori segmentation models--generally use as the dependent variable (the basis for
segmentation) either product-specific variables (product usage, loyalty) or general
customer characteristics (demographic variables)
• Cluster-based segmentation models--do not assume the segments are know a priori,
therefore, they assign customers to a predominate cluster type based upon factor analysis,
multi/nominal and cluster analysis
• Hybrid segmentation models--cluster-based segmentation models can be combined with
some a priori bases
• Flexible segmentation models--differs from a priori segmentation in which segments are
predetermined at the outset of the study and the cluster-based segmentation in which the
selected segments are based on the clustering analysis because of its flexibility of building
up segments based on consumers' response to alternative product offerings
5. 5
Segmentation Analysis
• Classification procedures for determining memberships in market
segments vary markedly according to the specific segmentation model
used
• For a priori approaches it is most likely sorting and cross-tabulation
• When clustering segmentation is employed, some sort of cluster or
factor analysis is generally used
• Componential and flexible segmentation involve primarily conjoint
analysis (hypothetical choice models) and the computer simulation
derived from these hypothetical models
6. 6
The Best
• The best depends on the marketing strategy
• You want segments
• You can reach
• At a reasonable cost
• Who will respond
• The hard part is not in segmenting; it’s in
developing a marketing plan for those segments
and executing
7. 7
Introduction To Clustering
• Response based segmentation is often done with statistical procedures
called “cluster analysis”
• As much art as a science
• Many ad hoc procedures with little statistical justification
• Choice of variables and pre-processing leads to different solutions
• The computer will blindly create clusters even where none exist
• Choice of variables very important
• Generally, need to make sure all responses are at least within-case
standardized
• All solutions need to be validated (cluster profiled)
13. • Nodal methods
• Selection of objects that serve as focal points or “nodes” for the
clusters
• Iterative partitioning methods
• Nodal methods that begin with an initial partition and then update
those initial points
• “Seed” points can be random, deliberately far apart, or pre-
specified
Non-Hierarchical Cluster Techniques
13
15. Ward’s MethodWard’s Method
K-meansK-means
Final ClustersFinal Clusters
Use Ward’s method to generate
the initial clusters
Input the centroids into K-means
and let it clean them up
Use the k-means as the
final clusters
*It’s more work, though, and increases the analysis cost, so we often
don’t work this way
Hybrids
15
16. • Single most crucial decision
• Do what is conceptually correct
• Usually, you have a type of segmentation in mind, such as “benefit
segmentation,” so you use only those variables as the “active” variables
• Clusters can then be profiled on the other variables, or “illustrative”
variables
• It’s hard to mix continuous and categorical variables
• Variables must be actionable
Variable Selection
16
17. • If scales are widely different, standardize the variables to the same
mean and standard deviation
• However, if scales are the same, you sometimes want to use the fact
that some attributes have more variance than others
• Using mean substitution or re-coding missing to some neutral value is
widely used so that everyone has a score for each variable
• There are more sophisticated , albeit ,time consuming methods to
impute missing values
Standardization and Missing Values
17
18. • Some people say everything is important while others say nothing is
important
• Some use the upper end of the scale (yea-sayers) rather than the
lower (nay sayers)
• You can eliminate these effects with “within case standardization”
• Also called respondent centered
• Subtract the mean for that respondent from each score
• Single centered data is centered around each respondent’s mean
value
• This removes the “elevation” effects for that respondent
• Double centered data is also centered around the mean for all cases
• That is, it is standardized to the group mean
Response Style Effects
18
19. • Various technical criteria
• Large complex literature on measures
• The common market research criteria:
• Few enough clusters to allow complete strategy development
• Each large enough to warrant strategic attention
• and to be reachable and defensible
• Use what makes sense from a marketing point of view
• Experience is the best guide
• Validity checks
Number Of Clusters
19
20. • Discriminant Analysis
• Use discriminant analysis to show that you can predict cluster
membership at very high levels (90%+) from the variables
• Split half reliability
• Split the sample in half and make certain the cluster solutions are
similar
• Won’t work on small samples
• Chaid (Answer Tree)
Validity Checks
20
21. • Use discriminant analysis or Chaid to predict group membership
• You can often reduce the number of attributes that have to be asked in
a follow-up
• Classify the new respondents into the old clusters using the
classification functions
• Mapping to databases
Later Classification
21
22. • Customer Relationship Management is a process that is both information- and
technology-driven which aims to leverage customer behavioral data, life event triggers
and marketing models to efficiently and continuously cultivate customer relationships:
• Maximize the potential of each customer relationship and generate incremental
revenue.
• Migrate from a seasonal, ad-hoc campaigns to an automated, perpetual marketing
process.
• Evaluate investment within segments, identify revenue potential and marketing
opportunities.
Customer Relationship
Management
Real-time marketing
execution
Data extraction,
data mining and
analysis
Segmentation is Activated by CRM Processes
22
23. Linkages to other data sources is critical
Competitive
Forecasts
Revenue
Forecasts
Market Size
Demand Side Analysis
•Market Potential
•Purchase Behavior
•Economic Trends
•Niche Segments
•Competing Technologies
•Pricing Analysis/Forecasts
•Product Opportunities Supply Side Analysis
•Industry Structure
•Competitive Positioning
•Product
•Pricing
•Promotion
•Distribution
•Innovation
•Alliances, JVs, M&A
Competitive
Forecasts
Revenue
Forecasts
Market Size
Used for:
Externalized
Opportunity/
Threat Analysis
Demand Side Analysis
•Market Potential
•Purchase Behavior
•Economic Trends
•Niche Segments
•Competing Technologies
•Pricing Analysis/Forecasts
•Product Opportunities Supply Side Analysis
•Industry Structure
•Competitive Positioning
•Product
•Pricing
•Promotion
•Distribution
•Innovation
•Alliances, JVs, M&A
Used for:
Internalized
Strength/
Weakness
Analysis
23
24. • Market Segmentation and Data Base Development For this project, commercial & industrial and residential customers and potential
consumers were surveyed to segment the market and to create a data base that could to target customers for the successful introduction of
new products and services. In addition, customer valuation through identification and analysis of the factors that determine a customer’s
current and potential lifetime value to the company were incorporated into the design. Focus groups were also employed
• Telecommunications Commercial Feasibility Study This study was used to predict the relative potential among local area businesses for
new telecommunication services in the St. Louis area. It was used to identify and profile consumers interested in switching local exchange
carriers; determine possible participation rates among consumers by services/service bundles; assess whether additional services would
enhance the offering; evaluate optimal pricing strategies; and appraise customer interest by segment to map back to executable market
database.
• Segmentation and Valuation Project in HC The primary objective was to provide a comprehensive plan including: a situation assessment,
strategic direction, possible approaches, timelines, and budgets. Our team provided a thorough assessment of the current situation and a
complete familiarization with and orientation around the information, techniques, and tools necessary to determine: (1) market segmentation
through the use of database creation, maintenance, and analysis to target customers for the successful introduction of marketing initiatives and
new products and services; and (2) customer valuation through identification and analysis of the factors that determine a customer’s current
and potential lifetime value to the company.
Case Studies
24
