Market segmentation techniques explored.

1. Optimizing Segmentation
Insight Research Group

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Why is Segmentation Difficult?
 Infinite number of possible solutions
 Hundreds of possible variables for to use
 Clearly defined clusters are rarely present in real life data-
sets

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Technical Challenges
 Challenge: Incorporating fundamentally
categorical variables
 Ethnicity, Religion, Political Party, Etc.
 Standard methods assume continuous data (ideal case) and
require interval level data as worst case (e.g. ratings scales)
 Correlation, linear regression, k-means clustering

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Technical Solutions
 Challenge: Incorporating fundamentally categorical
variables
 Multiple Correspondence Analysis (factor analysis for categorical
data)
 Pro: handles both demographic (categorical) and ratings variables
 Would allow treating sets of variables separately (i.e. demographic,
behavioral, psychological) – these sets could be used as inputs to
clustering method
 Con: segmentation would be based on extracted components

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Technical Challenges
 Determining the number of clusters/segments in the
data
 Standard methods require the user to specify the number of cluster
to extract
 Our standard practice results in fewer clusters then input variables
 e.g. AMC segmentation solutions required ~12 variables to find ~5
segments
 This ratio of features-to-segments will „water-down‟ the effect of the
individual variables (segments do not differ significantly on most items)

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Technical Solution 1
 Challenge: Determining the number of clusters/segments
in the data
 Solution: fit a probabilistic mixture model and compute a
complexity penalized likelihood (AIC / BIC scores)
 The model with the best AIC / BIC score is our best guess for the
number of natural clusters in the data
 Gaussian mixture models for continuous data
 Latent Class Models for categorical data
 Latent class models can handle both categorical and continuous data if the
continuous data is binned.
 Both of the above return BIC scores to determine the number of
clusters

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Technical Solution 1
 How many clusters do you see? (4 sources generated the data –duh)

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Technical Solution 1
 The BIC infers 4 clusters (4 clusters solution had the best BIC score)

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Technical Solution 1
 How many clusters do you see? (4 sources generated the data –not so obvious)

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Technical Solution 1
 The BIC says 4! (4 clusters had the best BIC score, thanks BIC!)

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Technical Solution 2
 Challenge: Determining the number of clusters/segments
in the data
 Solution: ensure there are fewer input variables then extracted
clusters
2(+) segments can be obtained from
a single variable.
That is a 2-1 ratio of segments-to-
variables
For AMC & MTV we got 5 segments
from ~12 variables. A ratio of 0.4-1.
- That is less then 1 segments for
every two variables…
 Also See: Van Buuren & Heiser (1989); Vichi & Kiers (2001); Hwang, Dillon, &
Takane (2006).

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Technical Challenges
 Respondents vary in their use of
ratings scales
 Some respondents only use part
of the scale,
 Either top or bottom of range
 Segmentation method will find the
high/low scale-use respondents
and define segments for them
 See AMC segments,

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Psychographic
banner for AMC
segments.
These items were not
used to define the cluster
solution.

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Technical Solution 1
 Challenge: Respondents vary in their use of ratings
scales
 Calibrate respondents to equate ratings scale across sample
 Overcoming Scale Use Heterogeneity (2003) Peter E. Rossi
 Pro: Improves the accuracy and validly of standard methods
 E.g. correlation, regression, clustering
 Con: requires complex and computational expensive models
 i.e. hierarchical bayesian models – available as R package

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Technical Solution 2
 Challenge: Respondents vary in their use of ratings
scales
 Abandon rating scales – use simple Agree/Disagree variables
 Focus on methods for categorical variables
 Multiple Correspondence Analysis (factor analysis for categorical data)
 Pro: handles both demographic (categorical) and ratings variables
 Would allow treating sets of variables separately (i.e. demographic, behavioral,
psychological) – these sets could be used as inputs to clustering methods

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What Slows Us Down?
 Each segmentation iteration consumes resources
 Producing new segmentation variable for each respondent
 .5 man hour
 Producing new banners
 Generating tables - .25 hours
 Formatting and printing – 1+ man hours
 Analyzing full banner for new segmentation
 Requires entire research team, 6+ man hours

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How to Speed it up
 Producing new segmentation variable for each respondent
 .5 man hour – Not the bottleneck
 Producing new banners
 Generating tables - .25 hours – Not the bottleneck
 Formatting and printing – 1+ man hours – Potential for Automation
 Analyzing full banner for the new segmentation
 Requires entire research team, 6+ man hours – workflow bottleneck
 Ideas / brainstorm
 Criteria of success is often vague
 When the goal is well defined quant methods can increase efficiency
 If you can formalize it you can solve it
 Time invested in the planning phase will reap productivity gains during analysis

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Hypothetical Case Study
 Goals Brainstorm:
 Client and previous research says:
 “segmentation should differentiate enthusiasts (early adopters) and utility
consumers (late adopters)”
 “also, segmentation should include demographics that are known to influence
technology adoption.
 Age, Gender, Income, Education
 Quant answers:
 “Ok, lets write a battery of questions addressing consumers perceptions and
relation to technology products – this will be distilled into a single „tech
enthusiasm‟ measure.
 “Also, all relevant demographic information can be reduced into a one (or more)
demo factors
 “Segments will be defined from a „reduced dimensionality‟ representation of the
data (MCA)”

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Hypothetical Case Study

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Hypothetical Case Study
Categories graph

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Hypothetical Case Study
Combined graph

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Hypothetical Case Study

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MCA for Segmentation
 (2006). An extension of multiple correspondence analysis for identifying
heterogeneous subgroups of respondents
 (2010). Traveler segmentation strategy with nominal variables through
correspondence analysis
 (2010). Fuzzy cluster multiple correspondence analysis
 (2010). Simultaneous two-way clustering of multiple correspondence
analysis
 (2005). A simultaneous approach to constrained multiple correspondence
analysis and cluster analysis for market segmentation
 (2002). Analysis of categorical marketing data by generalized constrained
multiple correspondence analysis

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Further Directions
 Extension to Multiple Correspondence Analysis
 Methods that let us combine nominal, numeric, and ordinal
variables
 Methods that let us group variables into sets.
 E.g. could ensures that psychographic, behavioral and demographic
have an equal influence on the final solution.
 Methods that simultaneously preform dimensionality
reduction and cluster discovery
 Optimizes the entire analysis to discover the most distinctive
clusters
 Very promising approach
 Con: I have not found an implementation of these methods.