Cluster Analysis vs. Market Segmentation

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Cluster Analysis vs. Market Segmentation

• Pavel Brusilovsky

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Objectives

• Introduce cluster analysis and market
  segmentation by discussing
• Concept of cluster analysis and basic ideas and
  algorithms
• Concept of market segmentation and basic ideas
• Comparison of these two approaches

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Cluster Analysis Algorithms

• There appear to be more algorithms for clustering
  data than data to analyze
• Quant People Folklore

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Supervised vs. Unsupervised

• Cluster analysis is a product of at least two
  different quantitative fields statistics and
  machine learning
• Machine learning
• Unsupervised is a learning from raw data (no
  examples of correct classification). In other
  words, class label information is unavailable.
• No measure of success
• Heuristic arguments for judgments
• Lots of methods developed
• Supervised is a learning from data where the
  correct classification of examples is given
  (class label information is available)

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Questions about groups

• Groups are unknown
• Are there groups in the data?
• Traditional Cluster Analysis
• Kohonen Vector Quantization
• Groups are known
• Given the groups, are there differences in the
  central tendency of the groups?
• ANOVA (one dependent variable)
• MANOVA (several dependent variables)
• To which groups does this new object belong?
• Discriminant Analysis

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Market segmentation

• Market segmentation is one of the most
  fundamental strategic marketing concepts
• grouping people (with the willingness, purchasing
  power, and the authority to buy) according to
  their similarity in several dimensions related to
  a product under consideration.
• The better the segments chosen for targeting by a
  particular organization, the more successful the
  organization is in the marketplace. The
  objectives are accurately predict the needs of
  customers and improve the profitability.

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Variables used in market segmentation

• Demographics
• Age
• Gender
• Education
• Income
• Home ownership, etc.
• Psychographics
• Lifestyle
• Attitude
• Beliefs
• Personality
• Buying motives, etc.
• Brand Loyalty
• Geography
• State
• ZIP
• City size
• Rural vs. Urban, etc.

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Market Segmentation and Cluster Analysis

• Help marketers discover distinct groups in their
  customer bases, and then use this knowledge to
  develop targeted marketing programs
• The underlying definition of cluster analysis
  procedures mimic the goals of market
  segmentation
• to identify groups of respondents that minimizes
  differences among members of the same group
• highly internally homogeneous groups
• while maximizing differences between different
  groups
• highly externally heterogeneous groups
• Market Segmentation solution depends on
• variables used to segment the market
• method used to arrive at a certain segmentation

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Criteria for Successful Market Segmentation

• Identifiability
• Can we see clear differences between segments?
• Substantiality
• Are the segments large enough to warrant separate
  marketing targeting?
• Accessibility
• Can we reach our customers?
• Stability
• Do our segments stable over a certain period of
  time?
• Responsiveness
• Is the response to our marketing effort segment
  specific?
• Actionability
• Do the segmentation provides direction of
  marketing efforts?

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Types of Clustering

• Partitional clustering
• A division of objects into non-overlapping
  subsets (clusters) such that each object is in
  exactly one cluster
• Hierarchical clustering
• A set of nested clusters organized as a
  hierarchical tree

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Other Distinctions Between Different Clustering

• Different treatment of object characteristics vs.
  even treatment
• Characteristics are subdivided into two groups
  dependent variable and independent variables
  (Classification and Regression trees)
• There is no such a subdivision (K-means)
• Model-based vs. Non-model-based
• A model is hypothesized for each of the clusters
  and the idea is to find the best fit of that
  model to each cluster (Latent Class Clustering)

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Limitations and Problems of Traditional Cluster
Analysis Methods

• Need to specify K (number of clusters) in
  advance
• Applicable only for interval variables (only
  numeric data)
• Has problems when clusters are of differing
• Sizes
• Densities
• Non-globular shapes
• Unable to handle noisy data and outliers

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Latent Class Cluster Analysis (LCCA)

• LCCA is a model-based approach
• Statistical model is postulated for the
  population from which the data sample is obtained
• LC model do not rely on the traditional modeling
  assumptions (linearity, normality, homogeneity)
• It is assumed that a mixture of underlying
  probability distributions generates the data
• LC model includes a K-category latent variable,
  each category represents a cluster
• Objects are classified into clusters based upon
  membership probabilities that are estimated
  directly from the data

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Advantages of Latent Class Cluster Analysis (LCCA)

• Optimal number of clusters is determined as a
  result of LCCA, using rigorous statistical tests
• No decisions have to be made about the scaling of
  the observed variables
• Variables maybe continuous, nominal, ordinal,
  count, or any combination of these

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Theory and Cluster Analysis

• Is clustering a theory?
• A theory could be true or false
• Unlike a theory, a clustering is neither true nor
  false, and should be judged largely on the
  interpretability and usefulness of results
• No measure of success
• Heuristic arguments for judgments
• Selection of right method is a problem
• However, a clustering may be useful for
  suggesting a theory, which could then be tested

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References

• Leonard Kaufman and Peter Rousseeuw (2005),
  Finding Groups in Data An Introduction to
  Cluster Analysis, Wiley Series in Probability
  and Statistics, 337 p.
• Mark Aldenderfer and Roger Blashfield (1984),
  Cluster Analysis (Quantitative Applications in
  the Social Sciences), SAGE Publications, Inc., 90
  p.
• Brian Everitt, Sabine Landau and Morven Leese
  (2001) Cluster Analysis, Oxford University Press,
  248 p.
• Marketing Segmentation (http//www.beckmanmarketin
  g8e.nelson.com/ppt/chapter03.pps. )

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Application of clustering and customer
segmentation to survey data
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Case study background, objectives, and
methodology

• Producer and distributor of health and beauty
  products launched a new product. The product can
  be ordered only on the website.
• In six month an internet survey was conducted.
  Only three simple questions were asked
• How many adults are in your household?
• How many of them adopted the product?
• How many of them did not adopt the product?
• When the total number of adopters and
  non-adopters is less than the number of adults in
  a household, the difference is treated as the
  number of unknowns. There are some other
  situation when the number of unknown makes sense
  to introduce.
• The client asked us to analyze the survey data
  (obviously it is not the most informative
  survey BI Solutions dealt with).
• The objectives of the study was to extract as
  much as possible useful information from the
  survey data in order to understand the
  distribution and the usage of the product among
  households, associate with each household a
  corresponding likelihood of adoption, and develop
  methodology to employ this info in the marketing
  programs.
• Methodology synergy of cluster analysis of
  proportional data and intuitive segmentation.

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Clustering of households

• We calculated the following three variables
• P1 is a proportion of customers in a household
  with unknown product adaption behavior
• P2 is a proportion of customers in a household
  who adopted the product
• P3 is a proportion of customers in a household
  who did not adopt the product
• Therefore, each household is characterized by a
  point in three-dimensional proportion space. Once
  again, it was the only available information
  (that we got from the client).
• We decided to employ synergy of cluster analysis
  and customer segmentation. Six clusters were
  identified as the result of K-means clustering.
• Variables importance in K-means clustering

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Household representation in three dimensional
proportion space
Each household is represented by a data point (a
square) in 3-dimensional proportion space.
Households form a 2-dimensional triangle. Each
square might represent several customers.
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Clusters of households mean value of
proportions and cluster interpretation
Cluster Interpretation Number of customers Mean value () Mean value () Mean value ()
Cluster Interpretation Number of customers P1 (Non-Adopters) P2 (Adopters) P3 (Unknown)
1 Non-Adopters 1463 96.4 0.6 3.0
2 Adopters 3915 0.3 97.9 1.8
3 Mixed households with adopters and non-adopters 474 46.3 51.8 1.9
4 Mixed households with non-adopters and unknowns 537 54.0 4.3 41.7
5 Unknowns 1097 0.8 0.9 98.3
6 Households with adopters and unknowns 1201 0.3 57.9 41.8
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Cluster Profiling good separation and good
interpretability
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Households as a set of three proportions /
percentages
Three proportions P1 (unknown) P2
(non-adopters) P3 (adopters) Different color
reflect clusters Cluster 1
(Non-Adopters) Cluster 2
(Adopters) Cluster 3 (Adopters and
Non-Adopters) Cluster 4 (Non-Adopters
and Unknown) Cluster 5
(Uncertain) Cluster 6 (Adopters and
Unknown)
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Cluster /Sub-cluster profile
Cluster Number of Households in Cluster Cluster Profile Likelihood of Adoption Segmentation Rule Number of Households in Sub-Cluster
1 1,463 Non-Adopters Low All households in cluster 1,463
2 3,915 Adopters High Medium Likely to adopt gt 40 The Rest 1,874 2,041
3 474 Mixed households with adopters and non-adopters Medium Low Likely/Unlikely gt 40 The rest 401 73

4 537 Mixed households with non-adopters and unknowns Low All households in cluster 537
5 1,097 Unknowns Unknown All households in cluster 1,097
6 1,201 Households with adopters and unknowns High Low Unknown Likely to adopt gt 60 Likely/Unlikely gt 40 The rest 378 723 100
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Likelihood of the new product adoption
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Next steps

• Customer profiling
• Data enrichment
• Data enrichment (ZIP level census data)
• Usage other health/beauty products (household
  level data)
• Estimation of the likelihood of the product
  adoption by data mining predictive analysts /
  scoring households with unknown purchasing
  behaviour
• Identifying customers with high likelihood of the
  product adoption for targeting
• Developing program for increasing up-sell and
  cross-sell
• Developing program for customer retention
• Spatial clustering of potential and real customers

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