Segmentation and Targeting

1
Segmentation and Targeting

• Basics
• Market Definition
• Segmentation Research and Methods
• Behavior-Based Segmentation

2
Market Segmentation

• Market segmentation is the subdividing of a
  market into distinct subsets of customers.
• Segments
• Members are different between segments but
  similar within.

3
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.

4
Primary Characteristicsof Segments

• Basescharacteristics that tell us why segments
  differ (e.g. needs, preferences, decision
  processes).
• Descriptorscharacteristics that help us find and
  reach segments.
• (Business markets) (Consumer markets)
• Industry Age/Income Size Education Locati
  on Profession Organizational Life styles
  structure Media habits

5
A Two-Stage Approachin 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

6
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)
7
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)
8
Variables to Segmentand Describe Markets
9
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.
• Kathleen Synnot, VP, Worldwide Marketing
  Mailing Systems Division, Pitney Bowes, Inc.
• quoted in Marketing Masters (Walden and Lawler)

10
Segmentation

• If youre not thinking segments, youre 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

11
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

12
Overview of Methods for STP

• Clustering and discriminantanalysis
• Choice-based segmentation
• Perceptual mapping- later

13
Segmentation (for Carpet Fibers)
Perceptions/Ratings for one respondent Customer
Values
..
..
.
.
.
.
.
.
.
.
.
.
.
.
D
.
.
.
.
.
A
.
.
.
.
.
.
Strength (Importance)
..
..
Distance between segments C and D
.
.
.
.
.
.
.
.
.
.
B
.
.
.
C
.
.
.
.
.
.
.
A,B,C,D Location of segment
centers. Typical members A schools B light
commercial C indoor/outdoorcarpeting
D health clubs
.
.
.
.
Water Resistance (Importance)
14
Targeting
Segment(s) to serve
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Strength(Importance)
.
.
.
.
.
.
.
.
.
.
.
.
Water Resistance (Importance)
15
Positioning
Product Positioning
.
.
Us
.
Comp 1
Comp 2
Strength(Importance)
Water Resistance (Importance)
16
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?

17
Behavior-Based Segmentation

• Traditional segmentation
• (eg, demographic,psychographic)
• Needs-based segmentation
• Behavior-based segmentation
• (choice models)

18
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
  firms strategy, given its capabilities and the
  likely reactions of competitors.

19
Segmentation Methods Overview

• Factor analysis (to reduce data before cluster
  analysis).
• Cluster analysis to form segments.
• Discriminant analysis to describe segments.

20
Cluster Analysis forSegmenting Markets

• Define a measure to assess the similarity of
  customers on the basis of their needs.
• Group customers with similar needs. Recommend
  the Wards 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).

21
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

22
Doing Cluster Analysis
a distance from member to cluster
center b distance from I to III
23
Wards 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

24
Wards Minimum Variance Agglomerative Clustering
Procedure
98.80
25.18
5.00
0.50
A
B
C
D
E
25
Discriminant Analysis forDescribing 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.

26
Two-Group Discriminant Analysis
XXOXOOO XXXOXXOOOO
XXXXOOOXOOO XXOXXOXOOOO XXOXOOOOOOO
Price Sensitivity
X-segment
Need for Data Storage
O-segment
x high propensity to buy o low propensity
to buy
27
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).

28
PDA Example
29
PDA Segmentation

• Performs Wards method - Code
• proc cluster datahold.pda methodwards standard
  outtreetreedat 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 datatreedat
• run

30
PDA Segmentation (alternative)

• Performs K-means method - Code
• proc fastclus datahold.pda maxc4 maxiter10
  random41 maxiter50 outclus
• 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

31
Output

• The following clusters are quite close together
  and can be combined with a small loss in consumer
• grouping information
• i) clusters 7 and 5 at 0.27,
• ii) clusters 1 and 6 at 0.28, ii)
• fused cluster 7-5 and cluster 2 (0.34).
• However, when going from a four-cluster
• solution to a three-cluster solution, the
  distance to be bridged is much larger
• (1.11)
• thus, the four-cluster solution is indicated by
  the ESS.
• In addition, four seems a reasonable number of
  segments to handle based on managerial judgment.

32
Four Cluster Solution profile code

• proc tree data treedata nclusters4 outoutclus
  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

33
PDA profiles
34
PDA Visual profile
35
PDA Visual profile
36
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.

37
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.

38
Profile based on Demos/behaviour
39
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.

40
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
  preferencestheir own or their employerswill
  drive the adoption decision

41
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.

42
Who to target

• Discuss.

43
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).

44
Discrimination based on demographics/behaviour

• proc discrim datatemp outstatoutdisc
  methodnormal poolyes list crossvalidate
• class cluster priors prop
• vars age education etc
• run
• all relevant vars. not used to create segment
  solutions

45
Discrimination based on demographics/behaviour

• This allows us a way to target and profile
  future customers

46
Discrimination based on demographics/behaviour
47
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.

48
Visualising relationships
49
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

50
(No Transcript)
51
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

52
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
  AnalysisExample 1 in SAS OnlineDoc
• proc corresp all dataCars outcCoor
• tables Marital, Origin
• run
• ---Plot the Simple Correspondence Analysis
  Results---
• plotit(dataCoor, datatypecorresp)

53
Cars by Marital Status
54
Segmentations

• Other details

55
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

56
Tandem k-means Example

• proc factor datadatafile n6 rotatevarimax
  round reorder flag.54 scree outscores
• var reasons1-reasons15 usage1-usage10
• run
• proc fastclus datascores maxc4 seed109162319
  maxiter50
• 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

57
Cluster Analysis Options

• There are several choices of how to form clusters
  in hierarchical cluster analysis
• Single linkage
• Average linkage
• Density linkage
• Wards method
• Many others
• Wards 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

58
FASTCLUS
59
Density Linkage
60
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

61
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_clusterin
  g for more k-means issues

62
Iteration History from FASTCLUS

• Relative Change in Cluster Seeds
• Iteration Criterion 1
  2 3 4 5
• ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
  ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
• 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

63
Results from Different Initial Seeds

• 19th run of 5 segments
• Cluster Means
• Cluster FACTOR1 FACTOR2
  FACTOR3 FACTOR4 FACTOR5 FACTOR6
• ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
  ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
• 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
• ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ
  ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

64
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)

65
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 Fishers
  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

66
Segmentations via Choice Modelling
67
Choice Models

• 1. Observe choice
• (Buy/not buy gt direct marketers Brand
  bought gt packaged goods, ABB)
• 2. Capture related data
• demographics
• attitudes/perceptions
• market conditions (price, promotion, etc.)
• 3. Link
• 1 to 2 via choice model gt model
  reveals importance weights of characteristics

68
Choice Models vs Surveys

• With standard survey methods . . .
• preference/ importance choice ï weights
  perceptions ñ ñ ñ predict observe/ask observ
  e/ask
• But with choice models . . .
• importance choice ï weights
  perceptions ñ ñ ñ observe infer observe/ask

69
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
  js 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.

70
Specification of the Deterministic Component of
Utility

• Vij å 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 is 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.

71
A Key Result from this SpecificationThe
Multinomial Logit (MNL) Model

• If customers 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
• åk 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.

72
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
73
Switchability Segmentation
Loyal to Us
Losable
Winnable Customers (business to gain)
Loyal toCompetitor
Current Product-Market by Switchability Questions
Where should your marketing efforts be
focused?How can you segment the market this way?
74
Using Choice-Based Segmentation for Database
Marketing

• A B C D Average Cus
  tomer Purchase Purchase ProfitabilityCustomer
  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 0.5
  6 1.03 10 4 72.00 0.65 1.87

75
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 (4Ps 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.

76
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?

77
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).