Segmentation and Profiling using SPSS for Windows

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Segmentation and Profiling using SPSS for
Windows

• Kate Grayson

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Why Segmentation?

• Used by e.g. retail and consumer product
  companies
• Trying to learn about and describe their
  customers' buying habits, gender, age, income
  level, etc.
• These companies tailor their marketing and
  product development strategies to each consumer
  group to increase sales and build brand loyalty.
• A valuable approach in Market Research, and SPSS
  offers some useful tools to facilitate this
  commercial process

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Segmentation in SPSS

• Most of the techniques for segmentation and
  profiling are exploratory
• There is no right or wrong answer, and the
  results are open to interpretation
• Trying to make sense of the data or find patterns
• Iterative techniques
• If it does not make business sense then it is not
  a good model!

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Segmentation in SPSS

• Techniques include
• Factor Analysis / Principal Components Analysis
• Hierarchical Clustering
• K-Means Cluster
• Non-Linear Principal Components Analysis
  (PRINCALS/CATPCA)
• The new Two-Step Cluster

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Which Technique to Use?
Cluster Analysis
Categories
Factor Analysis
Exploratory
Confirmatory
Discriminant Analysis
AnswerTree
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Which Test to use?

• Factor Analysis - to find patterns within
  variables
• Categories - use if data doesnt fit assumptions
  for Factor Analysis
• Cluster Analysis - to find patterns between
  individuals
• Two-Step Cluster To use with both categorical
  and continuous variables
• Discriminant Analysis - to look for differences
  between groups, try to predict target variable
• AnswerTree - combinations of data, to predict
  target

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Multivariate Analysis

• These techniques are inter-related, but dont
  have to use all of them
• Can use a combination of these techniques to
  segment the data

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Main Considerations

• Looking for patterns or trying to make
  predictions?
• Levels of Measurement of the data (categorical or
  continuous)
• Sample size
• Missing values
• Does data fulfil assumptions for test?

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Before you start. .. Check
your data!
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Handling Missing Data

• Check before analysis for any patterns within
  missing data
• Check before analysis that missing values are
  defined as missing - otherwise may compromise the
  model
• Be aware that most segmentation techniques ignore
  any cases with missing values - so may have less
  usable data than you think!

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Variable and Value Labels.

• It is worth checking the labels on your file
• SPSS may truncate long variable and value labels
  in the output, making it difficult to interpret
  the output
• Make sure all the useful information is at the
  beginning of the variable and value labels - so
  even if they are truncated, the output is still
  easy to read

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Data Coding

• Check the direction of the coding scheme, and
  maybe consider re-coding the data if the codes
  are counter-intuitive
• e.g. if have a rating scale that ranges from high
  to low, rather than low to high
• ... it can be difficult to interpret output and
  factor scores etc. once the data has been through
  several transformations

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Sample Data

• Data usage of underarm deodorants for men
• Three brands tested
• Rambo the current market leader
• Brad second most popular
• Clint recently launched product

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Profiling the Customers..

• Clint isnt selling as well as was hoped, so
  the research aims to find out
• Who is buying Clint?
• What sort of characteristics do they share?
• Who is buying the other deodorants tested?
• How might the marketing campaign be changed to
  ensure that the correct market is targeted?

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Data Collected

• Ratings of a range of lifestyle attribute
  questions, e.g. I tend to own the most
  up-to-date products, My family is most
  important thing in my life, I prefer to dress
  and entertain casually etc. (34 of these)
• Demographics age, type of work, exercise etc.
• Brand of D/O usually use
• How see yourself in relation to others, e.g.
  What makes you distinctive from your friends

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Segmentation the steps

1. Run Principal Components Analysis on attribute
   rating questions, to see if any underlying
   dimension in the variables
2. Check using Discriminant Analysis to see if these
   dimensions help predict brand used
3. Run Cluster Analysis to see if can find
   similarities between cases
4. Decide if other variables need to be included,
   e.g. categorical demographics
5. Run Two-Step Cluster using all variables

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Factor Analysis
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Factor Analysis what is it?

• Looks for relationships between continuous
  variables (based on correlations), in this case
  attribute rating questions
• Derives underlying constructs or dimensions in
  the data
• Tries to reduce a large number of variables to a
  small number of factors which explain most of the
  variance in the data
• If cant interpret the resulting solution then no
  good!

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Run Principal Components Analysis on 34 rated
attributes
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Factor Analysis Results

• The best solution produced 9 factors, interpreted
  below
• F1 High computer use
• F2 Rules, need to conform
• F3 Party animal
• F4 Family man
• F5 Likes new products, experiments
• F6 Likes pampering, pays more for trusted brands
• F7 Cautious, follower rather than leader for new
  products
• F8 Relaxed, casual
• F9 Home loving

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Do these factors help?

• Run Discriminant Analysis to see if can predict
  D/O used

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Factor Analysis Results

• The factors are good at predicting Rambo usage,
  but not at differentiating between Brad and
  Clint
• So try instead investigating relationships
  between cases using Cluster Analysis
• Options for clustering are
• Hierarchical Cluster
• K-Means Cluster
• Two-Step Cluster

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Hierarchical Cluster

• This is often thought of as the proper cluster
  method
• Looking for natural groupings within the data
• Bases groupings upon the similarity or
  dissimilarity between cases, rather than
  variables
• Very iterative technique time consuming!

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Clustering Data - Diagram
data point one case
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Decisions before Cluster

• Which variables to use?
• Which distance measures between cases to use?
• Which criteria for creating clusters to choose?
• NB
• The quality of the analysis will always depend
  upon the variables used
• Cluster Analysis will always find a solution!
• It is not possible to assess in the analysis
  itself how appropriate a variable is

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Stages of Hierarchical Cluster

• Select variables for analysis (carefully!)
• Build and assess model
• Save cluster membership
• If required, create cluster matrix for K-Means
• NB
• Because based on cases, need to make sure data is
  measured on same scale - if not, data should be
  standardized

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Run Hierarchical Cluster Analysison Saved
Factor Variables
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Decision with D/O Data

• I cant get a very good (i.e. useful to the
  business) model from Hierarchical Cluster
  analysis
• Also, I want to be able to include both
  categorical and continuous variables in the same
  model
• So I decide to use Two-Step Cluster instead

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Two-Step Cluster
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Two-Step Cluster

• The TwoStep Cluster Analysis procedure is an
  exploratory tool designed to reveal natural
  groupings (or clusters) within a data set that
  would otherwise not be apparent.
• The algorithm employed by this procedure has
  several features that differentiate it from
  traditional clustering techniques
• The ability to create clusters based on both
  categorical and continuous variables.
• Automatic selection of the number of clusters.
• The ability to analyze large data files
  efficiently.

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TwoStep Cluster

• Uses scalable cluster analysis algorithm
• This algorithm can handle both continuous and
  categorical variables or attributes and requires
  only one data pass in the procedure
• The first step of the procedure pre-clusters the
  records into many small sub-clusters
• Then it clusters the sub-clusters created in the
  pre-cluster step into the desired number of
  clusters
• If the desired number of clusters is unknown,
  TwoStep Cluster analysis automatically finds the
  proper number of clusters

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Two-Step Cluster

• This is unlike other clustering methods in SPSS -
  if the desired number of clusters is unknown,
  TwoStep Cluster analysis automatically finds the
  proper number of clusters
• Or you can pre-specify the number of clusters
  required - flexibility

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Run Two-Step Cluster Analysison Saved Factor
Variablesand Categorical Variables
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Link to more information

• More useful information about Two-Step Cluster
  can be found at the following websites
• http//www.rrz.uni-hamburg.de/RRZ/Software/SPSS/Al
  gorith.120/twostep_cluster.pdf
• NB This was the handout for the talk, with
  algorithm etc.
• Also useful
• http//www.spss.com/pdfs/S115AD8-1202A.pdf
• http//www.norusis.com/pdf/SPC_v13.pdf

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Some of the output producedby the Two-Step
Cluster Analysis is reproduced in thenext few
slides
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Brand usually use by Cluster

• Clint spray seems to be associated with Cluster
  6, with the roll-on version being associated with
  Clusters 4 and 2

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Employment Status by Cluster

• Cluster 2 (Clint roll-on) is largely made up of
  part-time, retired and not working respondents,
  Cluster 4 also has a high number of retired
  respondents, while Cluster 6 Clint spray) also
  has a high percentage of part-time and unemployed.

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Age Group by Cluster

• Cluster 2 (Clint roll-on) is largely made up of
  the younger and older age groups, Cluster 4 also
  has a high percentage of older respondents.
  Cluster 6 is more from 25 years upwards

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Cluster 4 (Clint roll-on) has below average
computer use and need to conform, above
average on Home Loving Family Man
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Cluster 6 (Clint spray) has above average
scores on Relaxed, Casual but not much else
this is Mr Laid Back!
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Summary of Findings

• Profiling of this data suggests that Clint is
  not targeting the expected market
• Clint is often not seen as sufficiently
  different from Brad, it has no perceived USP
• Clint is being used by a high percentage of
  older, retired, and part-time or not employed
  consumers, which may be a result of the
  aggressive product launch campaign with free
  samples, discounted prices etc.
• Clint marketing needs some more work!

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Summary of Segmenting and Profiling this data
using SPSS

• Principal Components Analysis helped investigate
  relationships between the rated attribute
  variables
• Hierarchical Cluster was used to try and find
  similarities between cases, using the factors
  derived from PCA
• Two-Step Cluster was then used to enable
  clustering of both continuous and categorical
  variables in the same model
• Useful conclusions were drawn about the market
  positioning of Clint deodorant