Segmentation and Targeting by 4n16l8zx

VIEWS: 27 PAGES: 77

									Segmentation and Targeting
   Basics
   Market Definition
   Segmentation Research and Methods
   Behavior-Based Segmentation
Market Segmentation
• Market segmentation is the
  subdividing of a market into
  distinct subsets of customers.

Segments

• Members are different between
  segments but similar within.
Segmentation Marketing
  Definition

  Differentiating your product and
  marketing efforts to meet the
  needs of different segments, that
  is, applying the marketing
  concept to market segmentation.
          Primary Characteristics
               of Segments
• Bases—characteristics that tell us why
  segments differ (e.g. needs, preferences,
  decision processes).

• Descriptors—characteristics that help us
  find and reach segments.

•       (Business markets)   (Consumer markets)

        Industry                Age/Income
        Size                    Education
        Location                Profession
        Organizational          Life styles
          structure             Media habits
      A Two-Stage Approach
       in Business Markets
Macro-Segments:

• First stage/rough cut
    – Industry/application
    – Firm size



Micro-Segments:

• Second-stage/fine cut
    – Different customer needs, wants, values within macro-segment
        Relevant Segmentation
             Descriptor
                                   Variable A: Climatic Region
                                     1. Snow Belt
                                     2. Moderate Belt
                                     3. Sun Belt



Fraction of
Customers


                  Segment 1
                              Segment 2
                                          Segment 3

              0                                       100%

              Likelihood of Purchasing Solar Water Heater
                                  (a)
       Irrelevant Segmentation
              Descriptor
                                    Variable B: Education
                                      1. Low Education
                                      2. Moderate Education
                                      3. High Education



Fraction of
Customers
                  Segment 1
                              Segment 2


                                          Segment 3

              0                                       100%

              Likelihood of Purchasing Solar Water Heater
                                  (b)
                  Variables to Segment
                  and Describe Markets
                          Consumer                               Industrial
Segmentation       Needs, wants benefits,         Needs, wants benefits, solutions to
  Bases            solutions to problems,         problems, usage situation, usage rate,
                   usage situation, usage rate.   size*, industrial*.
Descriptors        Age, income, marital status,   Industry, size, location, current
 Demographics      family type & size,            supplier(s), technology utilization,
                   gender, social class, etc.     etc.
 Psychographics    Lifestyle, values, &           Personality characteristics of
                   personality characteristics.   decision makers.
 Behavior          Use occasions, usage level,    Use occasions, usage level,
                   complementary &                complementary & substitute
                   substitute products used,      products used, brand loyalty, order
                   brand loyalty, etc.            size, applications, etc.
 Decision Making   Individual or group            Formalization of purchasing
                   (family) choice, low or high   procedures, size & characteristics
                   involvement purchase,          of decision making group, use of
                   attitudes and knowledge        outside consultants, purchasing
                   about product class, price     criteria, (de) centralizing buying,
                   sensitivity, etc.              price sensitivity, switching costs, etc.
 Media Patterns    Level of use, types of         Level of use, types of media used,
                   media used, times of use,      time of use, patronage at trade shows,
                   etc.                           receptivity of sales people, etc.
     Segmentation in Action
We segment our customers by letter volume, by
postage volume, by the type of equipment they
use. Then we segment on whether they buy or
lease equipment.

Based on this knowledge, we target our
marketing messages, fine tune our sales tactics,
learn which benefits appeal to which customers
and zero in on key decision makers at a
company.
          Segmentation

If you’re not thinking segments, you’re
not thinking. To think segments means
you have to think about what drives
customers, customer groups, and the
choices that are or might be available to
them.

                     —Levitt, Marketing Imagination
          STP as Business Strategy
Segmentation
• Identify segmentation bases and segment the market.
• Develop profiles of resulting segments.


Targeting
• Evaluate attractiveness of each segment.
• Select target segments.


Positioning
• Identify possible positioning concepts for each target segment.
• Select, develop, and communicate the chosen concept.


                                     … to create and claim value
Overview of Methods for STP
• Clustering and discriminant
  analysis

• Choice-based segmentation



• Perceptual mapping
  - later
    Segmentation (for Carpet Fibers)
                                  Perceptions/Ratings for one respondent:
                                  Customer Values

                         . ..              . .
       Strength
                        .
                       A ... .
                       .. .
                       .
                                        D .

                                        .
                                           .
                                        . .. .
                                        ... .
                                                          Distance between
     (Importance)

                        .
                       B. .
                                           .
                                                          segments C and D

                       . .. . .         C. .
A,B,C,D:
 Location of           ... .            . . .. .
                                        ... .
 segment centers.
Typical members:
                       .                .
 A: schools
 B: light commercial
 C: indoor/outdoor
     carpeting         Water Resistance
 D: health clubs
                          (Importance)
               Targeting
                   Segment(s) to serve


                 . .
                 ... .               . .
                                  . ... .
               .. .               ... .
               .                  .
Strength
(Importance)      . .
               . ... .              . .
               ... .              . ... .
                                  ... .
               .                  .
                Water Resistance
                   (Importance)
               Positioning
                   Product Positioning




               .. .
                Comp 1
                             Us
                                     . .
                                  . ... .
                                  ... .
                 Comp 2           .
Strength
(Importance)       . .
                . ... .             . .
                ... .             . ... .
                                  ... .
                .                 .
                Water Resistance
                   (Importance)
         A Note on Positioning
Positioning involves designing an offering so that the
target segment members perceive it in a distinct and
valued way relative to competitors.

Three ways to position an offering:
  1. Unique       (“Only product/service with XXX”)
  2. Difference   (“More than twice the [feature] vs.
                  [competitor]”)
  3. Similarities (“Same functionality as [competitor];
                  lower price”)

What are you telling your targeted segments?
Behavior-Based Segmentation

• Traditional segmentation
  (eg, demographic,
  psychographic)

• Needs-based segmentation

• Behavior-based segmentation
  (choice models)
     Steps in a Segmentation Study
• Articulate a strategic rationale for segmentation (ie, why are
  we segmenting this market?).

• Select a set of needs-based segmentation variables most
  useful for achieving the strategic goals.

• Select a cluster analysis procedure for aggregating (or
  disaggregating customers) into segments.

• Group customers into a defined number of different
  segments.

• Choose the segments that will best serve the firm’s strategy,
  given its capabilities and the likely reactions of competitors.
Segmentation: Methods
     Overview
• Factor analysis (to reduce data
  before cluster analysis).

• Cluster analysis to form segments.

• Discriminant analysis to describe
  segments.
        Cluster Analysis for
        Segmenting Markets
• Define a measure to assess the similarity of
  customers on the basis of their needs.

• Group customers with similar needs. Recommend:
  the “Ward’s minimum variance criterion” and, as an
  option, the K-Means algorithm for doing this.

• Select the number of segments using numeric and
  strategic criteria, and your judgment.

• Profile the needs of the selected segments (e.g., using
  cluster means).
        Cluster Analysis Issues
• Defining a measure of similarity (or distance) between
  segments.

• Identifying “outliers.”

• Selecting a clustering procedure
    – Hierarchical clustering (e.g., Single linkage, average linkage, and
      minimum variance methods)
    – Partitioning methods (e.g., K-Means)


• Cluster profiling
    – Univariate analysis
    – Multiple discriminant analysis
       Doing Cluster Analysis
                                                  a = distance from member
                                                      to cluster center
                                                  b = distance from I to III

                        •
                    •
                        • •
                         •
              III                   Perceptions or ratings data
Dimension 2                            from one respondent
                        b

                        •   •                        •
                  •                 •                    •   •
                                a
              I                                     II


                                Dimension 1
               Ward’s Minimum Variance
               Agglomerative Clustering
                      Procedure
First Stage:      A = 2         B =   5        C = 9      D = 10       E = 15

Second Stage:                  AB =   4.5     BD = 12.5
                               AC = 24.5      BE = 50.0
                               AD = 32.0     CD = 0.5
                               AE = 84.5      CE = 18.0
                               BC =   8.0     DE = 12.5

Third Stage:    CDA = 38.0    CDB = 14.0    CDE = 20.66   AB =   5.0
                 AE = 85.0     BE = 50.5

Fourth Stage:                ABCD = 41.0     ABE= 93.17 CDE = 25.18

Fifth Stage:                              ABCDE = 98.8
Ward’s Minimum Variance
Agglomerative Clustering
       Procedure
98.80


25.18



 5.00


 0.50

        A   B   C   D   E
 Discriminant Analysis for
Describing Market Segments

 • Identify a set of “observable” variables
   that helps you to understand how to
   reach and serve the needs of selected
   clusters.

 • Use discriminant analysis to identify
   underlying dimensions (axes) that
   maximally differentiate between the
   selected clusters.
          Two-Group Discriminant
                Analysis
                                   XXOXOOO
                                XXXOXXOOOO
                    Price
                                 XXXXOOOXOOO
                  Sensitivity
                                XXOXXOXOOOO
                                  XXOXOOOOOOO

       X-segment                     Need for Data Storage




                                                     O-segment
x = high propensity to buy
o = low propensity to buy
         Interpreting Discriminant
              Analysis Results
• What proportion of the total variance in the
  descriptor data is explained by the statistically
  significant discriminant axes?

• Does the model have good predictability (“hit rate”)
  in each cluster?

• Can you identify good descriptors to find differences
  between clusters? (Examine correlations between
  discriminant axes and each descriptor variable).
PDA Example
           PDA – Segmentation
• Performs Wards method - Code:
proc cluster data=hold.pda method=wards standard
   outtree=treedat pseudo;
    var Innovator       Use_Message Use_Cell Use_PIM
   Inf_Passive Inf_Active     Remote_Acc Share_Inf Monitor
   Email Web M_Media Ergonomic Monthly Price;
  run;

proc tree data=treedat;
run;
   PDA – Segmentation (alternative)
• Performs K-means method - Code:

proc fastclus data=hold.pda maxc=4 maxiter=10 random=41 maxiter=50 out=clus;
    var Innovator Use_Message Use_Cell Use_PIM Inf_Passive
    Inf_ActiveRemote_Acc Share_Inf Monitor Email Web M_Media Ergonomic ;
  run;


proc means data =clus;
 var Innovator  Use_Message          Use_Cell Use_PIM Inf_Passive
    Inf_Active  Remote_Acc           Share_Inf Monitor Email Web
    M_Media
Ergonomic Monthly      Price;
by cluster;
run;
                                 Output
•   The following clusters are quite close
    together and can be combined with a            Distance
    small loss in consumer                         (not to scale)

•   grouping information:
•   i) clusters 7 and 5 at 0.27,                            3.13

•   ii) clusters 1 and 6 at 0.28, ii)
•   fused cluster 7-5 and cluster 2 (0.34).                 1.45



•   However, when going from a four-cluster                 1.11
    solution to a three-cluster solution, the
    distance to be bridged is much larger
                                                            0.34
    (1.11);
•   thus, the four-cluster solution is indicated
    by the ESS.                                             0.28

•   In addition, four seems a reasonable
    number of segments to handle based on
    managerial judgment.                                    0.27




                                                                    1   6   4   2   5   7   3 Cluster
  Four Cluster Solution – profile code;
proc tree data = treedata nclusters=4 out=outclus no print;
run;

** create new data set;

data temp;
    merge hold.pda outclus;
run;
** profile these segments;

proc means data =temp;
 var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_Active
           Remote_Acc Share_Inf Monitor Email Web M_MedErgonomic Monthly Price;
by cluster;
run;
PDA profiles
   IN
     NO




                          0
                              0.5
                                      1
                                                                  1.5
                                                                            2
                                                                                2.5
             VA
US              T  O
   E   _M           R
            ES
              SA
                   G
                    E
       US
          E   _C
                EL
                  L
        US
           E     _P
  IN                IM
    F_
      P      AS
               SI
              VE
   IN
     F_
        AC
           TI
 RE           VE
    M
      O
       TE
          _A
              CC
   SH
       AR
          E_
             IN
               F
      M
        O
         NI
            TO
               R

              EM
                   AI
                      L

               W
                EB
        M
            _M
                 ED
  ER               IA
    G
     O
      N
              O
               M
                    IC
                                                                                      PDA Visual profile




        M
            O
             NT
                H
                    LY

              PR
                IC
                  E
                                    Series4
                                              Series3
                                                        Series2
                                                                  Series1
PDA Visual profile…
                               INNOVATOR
                                 2.5
                PRICE                          USE_MESSAGE
                                 2
      MONTHLY                                          USE_CELL
                               1.5

                                 1
ERGONOMIC                                                  USE_PIM
                               0.5

                                 0
 M_MEDIA                                                     INF_PASSIVE




       WEB                                               INF_ACTIVE



             EMAIL                                 REMOTE_ACC

                     MONITOR               SHARE_INF
                                                                     Series1
                                                                     Series2
                                                                     Series3
                                                                     Series4
                PDA profiles
• Cluster 1. Phone users who use Personal
  Information Management software, to whom
  Email and Web access, as well as Multimedia
  capabilities are important.

• Cluster 2. People who use messaging services and
  cell phones, need remote access to information,
  appreciate better monitors, but not for multi-media
  usage.
               PDA profiles..
Cluster 3. Pager users who have a high need for fast
  information sharing (receiving as well as sending)
  and also remote access. They use neither email
  extensively, nor the Web, nor Multi-media, but do
  require a handy, non-bulky device.

Cluster 4. Innovators who use cell phones a lot,
  have a high need for Email, Web, and Multi-media
  use. They also require a sleek device.
Profile based on Demos/behaviour
               Name the segments
Cluster 1 - Sales Pros:
Cluster 1 consists mainly of sales professionals: 54% of the cluster members
indicated Sales as their occupation. They use the cell phone heavily, and many
(45%) own a PDA already; practically all have access to a PC. Their work often
takes them away from the office. They mostly read two of the selected
magazines: 30% read BW. From the needs data, we see that they are quite price
sensitive.


Cluster 2 – Service Pros:
Cluster 2 is made up primarily of service personnel (39%) and secondarily of
sales personnel (23%). They use cell phones heavily, but only about one fifth
currently use a PDA. They spend much time on the road and in remote locations.
They read PC Magazine, 29%. From the needs data, we see that they are quite
price sensitive.
           Name the segments…
Cluster 3 – Hard Hats:
  Cluster 3 is made up predominantly of construction (31%) and
  emergency (19%)
  workers. They use cell phones, but usually do not own a PDA.
  By the nature of their work, they have high information relay
  needs and generally work in remote locations.

  They exchange information with colleagues in the field (e.g.
  construction workers on the site). Many read Field & Stream
  (31%) and also PC Magazine. Note also from the needs data,
  that they are the least price sensitive (willing to pay highest price
  plus monthly fee) and also have the lowest income.

  This apparent anomaly occurs because these folks are less likely
  to have to pay for the device themselves, raising the question of
  whose preferences—their own or their employers’—will drive
  the adoption decision
         Name the segments…
Cluster 4 – Innovators:
Cluster 4 represents early adopters (see needs data),
  predominantly professionals (lawyers, consultants,
  etc.).
  Every cluster member has access to a PC, 89
  percent already own PDAs.
  They read many magazines, especially BW 49%,
  PCMag 32%. Most are highly paid and highly
  educated.
             Who to target…
• Discuss.
     Interpreting Cluster Analysis
                Results
• Select the appropriate number of clusters:

    – Are the bases variables highly correlated? (Should we reduce the data
      through factor analysis before clustering?)

    – Are the clusters separated well from each other?

    – Should we combine or separate the clusters?

    – Can you come up with descriptive names for each cluster (eg, professionals,
      techno-savvy, etc.)?


• Segment the market independently of your ability to reach
  the segments (i.e., separately evaluate segmentation and
  discriminant analysis results).
             Discrimination based on
             demographics/behaviour
proc discrim data=temp outstat=outdisc method=normal pool=yes list
    crossvalidate;
   class cluster; priors prop;
vars age education etc… ;
  run;



** all relevant vars. not used to create segment solutions;
        Discrimination based on
        demographics/behaviour
This allows us a way to
target and profile future
customers:
Discrimination based on
demographics/behaviour
               Discrimination based on
               demographics/behaviour
•The first discriminant function above explains 51% the variation.
According to its coefficients, i.e., the four groups are particularly
different with respect to the amount away from the office.

•In addition, the function shares high correlation with the level of
education, possession of a PDA, and income.

•The second function explains 32% of the variance and primarily
distinguishes the occupation types construction/emergency from
sales/service, and the third function separates Sales and Service
types.
Visualising relationships
       Correspondence Analysis
• Provides a graphical summary of the interactions in a table
• Also known as a perceptual map
   – But so are many other charts
• Can be very useful
   – E.g. to provide overview of cluster results
• However the correct interpretation is less than intuitive,
  and this leads many researchers astray
                                 Four Clusters (imputed, normalised)




                                                      Usage 9
                                                                   Usage 7
                                                     Usage 8
                                                                   Usage 4
                                          Usage 10                                          Cluster 2
                            Reason 2                           Reason 9
Cluster 3                                                                 Reason 13
                                     Reason 6
                                       Usage 5

                                                                                       Reason 10
                       Usage 6       Usage 2                                                                   Reason 4
                                                                           Cluster 1               Reason 12
                           Usage 1
                                                    Usage 3
                                           Reason 11                                                                       Reason 7
                         Reason 3

                                                                                                         Reason 5
                                                                                                               Reason 14


                                                          Cluster 4                    Reason 8
     Reason 1




                                     Reason 15
25.3%


            53.8%
    = Correlation < 0.50 2D Fit = 79.1%
                     Interpretation
• Correspondence analysis plots should be interpreted by
  looking at points relative to the origin
   – Points that are in similar directions are positively associated
   – Points that are on opposite sides of the origin are negatively
     associated
   – Points that are far from the origin exhibit the strongest associations
• Also the results reflect relative associations, not just which
  rows are highest or lowest overall
                   Software for
             Correspondence Analysis
• Earlier chart was created using a specialised package called
  BRANDMAP
• Can also do correspondence analysis in most major statistical packages
• For example, using PROC CORRESP in SAS:

 *---Perform Simple Correspondence Analysis—Example 1 in SAS OnlineDoc;
 proc corresp all data=Cars outc=Coor;
   tables Marital, Origin;
 run;

 *---Plot the Simple Correspondence Analysis Results---;
 %plotit(data=Coor, datatype=corresp)
Cars by Marital Status
Segmentations

  Other details
         Tandem Segmentation
• One general method is to conduct a factor
  analysis, followed by a cluster analysis
• This approach has been criticised for losing
  information and not yielding as much
  discrimination as cluster analysis alone
• However it can make it easier to design the
  distance function, and to interpret the results
         Tandem k-means Example
proc factor data=datafile n=6 rotate=varimax round reorder flag=.54 scree out=scores;
  var reasons1-reasons15 usage1-usage10;
run;

proc fastclus data=scores maxc=4 seed=109162319 maxiter=50;
  var factor1-factor6;
run;

• Have used the default unweighted Euclidean distance
  function, which is not sensible in every context
• Also note that k-means results depend on the initial cluster
  centroids (determined here by the seed)
• Typically k-means is very prone to local maxima
     – Run at least 20 times to ensure reasonable maximum
         Cluster Analysis Options
• There are several choices of how to form clusters in
  hierarchical cluster analysis
   –   Single linkage
   –   Average linkage
   –   Density linkage
   –   Ward’s method
   –   Many others
• Ward’s method (like k-means) tends to form equal sized,
  roundish clusters
• Average linkage generally forms roundish clusters with
  equal variance
• Density linkage can identify clusters of different shapes
FASTCLUS
Density Linkage
            Cluster Analysis Issues
• Distance definition
    – Weighted Euclidean distance often works well, if weights are chosen
      intelligently
• Cluster shape
    – Shape of clusters found is determined by method, so choose method
      appropriately
• Hierarchical methods usually take more computation time than k-
  means
• However multiple runs are more important for k-means, since it can be
  badly affected by local minima
• Adjusting for response styles can also be worthwhile
    – Some people give more positive responses overall than others
    – Clusters may simply reflect these response styles unless this is adjusted
      for, e.g. by standardising responses across attributes for each respondent
             MVA - FASTCLUS
• PROC FASTCLUS in SAS tries to minimise the root mean
  square difference between the data points and their
  corresponding cluster means
   – Iterates until convergence is reached on this criterion
   – However it often reaches a local minimum
   – Can be useful to run many times with different seeds and choose
     the best set of clusters based on this RMS criterion
• See http://en.wikipedia.org/wiki/K-means_clustering for
  more k-means issues
        Iteration History from FASTCLUS
Relative Change in Cluster Seeds
    Iteration    Criterion            1          2           3           4           5
    ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
           1        0.9645       1.0436     0.7366      0.6440      0.6343      0.5666
           2        0.8596       0.3549     0.1727      0.1227      0.1246      0.0731
           3        0.8499       0.2091     0.1047      0.1047      0.0656      0.0584
           4        0.8454       0.1534     0.0701      0.0785      0.0276      0.0439
           5        0.8430       0.1153     0.0640      0.0727      0.0331      0.0276
           6        0.8414       0.0878     0.0613      0.0488      0.0253      0.0327
           7        0.8402       0.0840     0.0547      0.0522      0.0249      0.0340
           8        0.8392       0.0657     0.0396      0.0440      0.0188      0.0286
           9        0.8386       0.0429     0.0267      0.0324      0.0149      0.0223
          10        0.8383       0.0197     0.0139      0.0170      0.0119      0.0173

                            Convergence criterion is satisfied.

                         Criterion Based on Final Seeds = 0.83824
         Results from Different Initial Seeds
19th run of 5 segments

 Cluster Means

 Cluster       FACTOR1       FACTOR2       FACTOR3       FACTOR4       FACTOR5       FACTOR6
 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
    1         -0.17151       0.86945      -0.06349       0.08168       0.14407       1.17640
    2         -0.96441      -0.62497      -0.02967       0.67086      -0.44314       0.05906
    3         -0.41435       0.09450       0.15077      -1.34799      -0.23659      -0.35995
    4          0.39794      -0.00661       0.56672       0.37168       0.39152      -0.40369
    5          0.90424      -0.28657      -1.21874       0.01393      -0.17278      -0.00972


20th run of 5 segments

                                  Cluster Means

 Cluster       FACTOR1       FACTOR2       FACTOR3       FACTOR4       FACTOR5       FACTOR6
 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
    1          0.08281      -0.76563       0.48252      -0.51242      -0.55281       0.64635
    2          0.39409       0.00337       0.54491       0.38299       0.64039      -0.26904
    3         -0.12413       0.30691      -0.36373      -0.85776      -0.31476      -0.94927
    4          0.63249       0.42335      -1.27301       0.18563       0.15973       0.77637
    5         -1.20912       0.21018      -0.07423       0.75704      -0.26377       0.13729
       Howard-Harris Approach
• Provides automatic approach to choosing seeds for k-
  means clustering
• Chooses initial seeds by fixed procedure
   – Takes variable with highest variance, splits the data at the mean,
     and calculates centroids of the resulting two groups
   – Applies k-means with these centroids as initial seeds
   – This yields a 2 cluster solution
   – Choose the cluster with the higher within-cluster variance
   – Choose the variable with the highest variance within that cluster,
     split the cluster as above, and repeat to give a 3 cluster solution
   – Repeat until have reached a set number of clusters
• I believe this approach is used by the ESPRI software
  package (after variables are standardised by their range)
   Another “Clustering” Method
• One alternative approach to identifying clusters is to fit a
  finite mixture model
   – Assume the overall distribution is a mixture of several normal
     distributions
   – Typically this model is fit using some variant of the EM algorithm
       • E.g. weka.clusterers.EM method in WEKA data mining package
       • See WEKA tutorial for an example using Fisher’s iris data
• Advantages of this method include:
   – Probability model allows for statistical tests
   – Handles missing data within model fitting process
   – Can extend this approach to define clusters based on model
     parameters, e.g. regression coefficients
• Also known as latent class modeling
Segmentations via Choice Modelling
                Choice Models
1. Observe choice:
      (Buy/not buy =>          direct marketers
      Brand bought =>        packaged goods, ABB)

2. Capture related data:
    – demographics
    – attitudes/perceptions
    – market conditions (price, promotion, etc.)

3. Link
      1 to 2 via “choice model” => model reveals
      importance weights of characteristics
               Choice Models vs Surveys
With standard survey methods . . .
   preference/           importance
      choice              weights         perceptions
                                              
     predict            observe/ask         observe/ask



But with choice models . . .
                           importance
      choice                weights       perceptions
                                              
     observe                  infer         observe/ask
         Behavior-Based Segmentation
                   Model
Stage 1: Screen products using key attributes to identify the
         “consideration set of suppliers” for each type of customer.

Stage 2: Assume that customers (of each type) will choose
         suppliers to maximize their utility via a random utility
         model.


Uij = Vij + eij

where:
  Uij    = Utility that customer i has for supplier j’s product.
  Vij    = Deterministic component of utility that is a function of product and supplier
           attributes.
  eij    = An error term that reflects the non-deterministic component of utility.
          Specification of the
      Deterministic Component of
                Utility
                           Vij =  Wk bijk
                                  k



where: i       =      an index to represent customers, j is an index to
        represent suppliers, and k is an index to represent attributes.
 bijk = i’s perception of attribute k for supplier j.

 wk = estimated coefficient to represent the impact of bijk on the
      utility realized for attribute k of supplier j for customer i.
A Key Result from this Specification:
The Multinomial Logit (MNL) Model
If customer’s past choices are assumed to reflect the principle
of utility maximization and the error (eij) has a specific form
called double exponential, then:
                                    ^


                            eVij
                    pij = ––––––        ^

                          k eVik
               where:
        ^
 pij = probability that customer i chooses supplier j.
 Vij = estimated value of utility (ie, based on estimates of bijk)
       obtained from maximum likelihood estimation.
Applying the MNL Model in
  Segmentation Studies
  Key idea: Segment on the basis of
            probability of choice—


        1. Loyal to us

        2. Loyal to competitor

        3. Switchables:
           loseable/winnable
           customers
 Switchability Segmentation

                Loyal to Us          Losable



                 Winnable            Loyal to
                 Customers
               (business to gain)   Competitor


Current Product-Market by Switchability
Questions: Where should your marketing efforts be focused?
           How can you segment the market this way?
Using Choice-Based Segmentation
     for Database Marketing
                  A             B        C           D
                             Average             Customer
               Purchase      Purchase            Profitability
    Customer   Probability   Volume     Margin   =ABC

       1         30%          $31.00     0.70       $6.51
       2          2%         $143.00     0.60       $1.72
       3         10%          $54.00     0.67       $3.62
       4          5%          $88.00     0.62       $2.73
       5         60%          $20.00     0.58       $6.96
       6         22%          $60.00     0.47       $6.20
       7         11%          $77.00     0.38       $3.22
       8         13%          $39.00     0.66       $3.35
       9          1%         $184.00     0.56       $1.03
      10          4%          $72.00     0.65       $1.87
     Managerial Uses of
    Segmentation Analysis
• Select attractive segments for focused effort (Can
  use models such as Analytic Hierarchy Process or
  GE Planning Matrix).

• Develop a marketing plan (4P’s and positioning)
  to target selected segments.

    – In consumer markets, we typically rely on advertising and
      channel members to selectively reach targeted segments.

    – In business markets, we use sales force and direct marketing.
      You can use the results from the discriminant analysis to
      assign new customers to one of the segments.
          Checklist for Segmentation
                    Studies
• Is it values, needs, or choice-based? Whose values and needs?

• Is it a projectable sample?

• Is the study valid? (Does it use multiple methods and multiple
  measures)

• Are the segments stable?

• Does the study answer important marketing questions (product
  design, positioning, channel selection, sales force strategy, sales
  forecasting)

• Are segmentation results linked to databases?

• Is this a one-time study or is it a part of a long-term program?
               Concluding Remarks

In summary,

• Use needs variables to segment markets.

• Select segments taking into account both the attractiveness of
  segments and the strengths of the firm.

• Use descriptor variables to develop a marketing plan to reach
  and serve chosen segments.

• Develop mechanisms to implement the segmentation strategy on
  a routine basis (one way to do this is through information
  technology).

								
To top