Segmentation and Targeting

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Segmentation and Targeting

• STP (Segmentation, Targeting, and Positioning) as
  the core components of marketing strategy
• Needs-based segmentation
• Cluster Analysis

MBA, Session 2 1
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Segmentation Discussion

• Present overview of method (today)
• Discuss managerial issues/extensions (next Tue)

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Currently, Most Segmentation Projects Provide.
Insights
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SAMPLE SEGMENTATION STUDY FOR A HEADACHE REMEDY


Savvy Functionalists
Disciplined Bodies
Reluctantly Reliants
Super-Stressed Reactionaries
ChronicCombatants
Adults
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10
17
14
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Past 30 Day HeadacheSufferersVolumeMed
TreatersMed Volume
132513 25
100 100 100 100
131613 20
161816 15
191621 17
13813 10
SufferReg/OcclyRegularly
8235
5820
7523
458
6310
Severity of Headache(6 pt scale)
4.0
4.4
3.7
3.3
3.1
of All Headaches Mild / Moderate 100
Severe 100 Migraine
100
163133
12912
182018
12911
13 1013
Share of Pain Days Mild / Moderate Severe
Migraine
100 453322
100652015
100602515
100681814
10065 2015
Treat Judiciously
Concern
Reaction toHeadache
Upsetting,Distracts From Responsibilities
Sharply Limits Life
Reach forthe Cure (Med) Quickly
Move AboutOTC MedsIf Gets Worse
OTC Meds atFirst Twinge
Mostly Rx, OTC With Non-meds
Rx and OTC ...Relaxation
Put off Till Severe,Then OTC Meds
Treatment
Desired MedAspects
Fast
Powerful,Long-lasting Relief
No Side Effects
Safe
Immediacy
Targeting Opportunities
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In the Final Analysis.
Little Measurable Value Most segmentation
projects are one-time Projects that drain
resources
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STP is a Core Business Process
STP - (Segmentation, Targeting, Positioning) is a
Decision Process

• To identify and select groups of potential
  buyers...
• Organizations, Buying Centers, Individuals
• Whose needs within-groups are similar and whose
  needs between-groups are different (S)
• Who can be reached profitably (T)
• With a focused marketing program (P)

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How We Think About Segmentation

• Instinctively, we think about target market
  segments, that are
• Easily defined
• Clear-cut
• And reachable . . .

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• So you think you know your customers? Meet Stella
  Burns. For two years, weve been mailing her
  coupons for cosmetics, and she hasnt redeemed a
  single one! Perhaps you should take a closer look
  at your customer profiles and buying habitsRight
  Stella? Good Puppy!
• Data General advertisement in Forbes Magazine,
  1998

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How Many DifferentGroups Are Here?
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On the Other HandIs this Segmentation?

• Ad in London Newspapers, 1913
• Men wanted for hazardous journey. Small wages,
  bitter cold, long months of complete darkness,
  constant danger, safe return doubtful. Honor and
  recognition in case of success.
• Ernest Shackleton,
• Did it work?

It is not entirely clear whether Ernest
Shackleton actually placed such an ad. At worst,
this makes for an interesting apocryphal story.
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A Four-Phase Process for Successful Segmentation
Analysis Project
Phase IV Analysis and Implementation
Phase I Planning and Design
Phase II Qualitative Assessment
Phase III Quantitative Measurement
Internal Assessment Planning
Qualitative Research
Quantitative Survey

• Objective(s) of segmentation
• Resources
• Constraints

• Interview Materials Development
• Qualitative Data Collection
• Deep needsIdentification
• Decision-Making Process Assessment

• Sample Design
• Questionnaire Development
• Data Collection

Segmentation Analysis

• Cluster Analysis
• Portfolio Analysis
• Positioning Analysis

Implementation Through Database Tools
Database Review

• Call Center
• Web
• Sales call patterns
• Promotion
• .

• Primary data already available
• Secondary data

Classification Tool Development

• Discriminant function
• Binary (CART) tree

Prototype Implementation Exercises

• What ifs?
• Relevant groups involved?
• ..

Basic Idea Do segmentation analysis on a small
(random) sample of customers, but leverage the
insights across the entire customer base.
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Needs-Based Segmentation Distinguish Between
Bases and Descriptors

• 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

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Variables to Segmentand Describe Markets
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Segmentation (for Carpet Fibers)
Perceptions/Ratings for one respondent Customer
Values
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Strength (Importance)
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Distance between segments C and D
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A,B,C,D Location of segment
centers. Typical members A schools B light
commercial C indoor/outdoorcarpeting
D health clubs
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Water Resistance (Importance)
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Targeting
Segment(s) to serve
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Strength(Importance)
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Water Resistance (Importance)
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Which Segments to Serve?Segment Attractiveness
Criteria
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Selecting Segments to Serve
E
Strong
Firms Competitive Position
B
Medium
D
A
C
Weak
Low
Average
High
Segment Attractiveness
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Segment Economics forDriving Segment Selection
Source McKinsey Co. Report, October 2001
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Positioning
Product Positioning
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Us
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Comp 1
Comp 2
Strength(Importance)
Water Resistance (Importance)
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Segmentation Process Summary

• Articulate a strategic rationale for segmentation
  (i.e., 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.

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Segmentation Methods Overview

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

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Cluster Analysis forSegmenting Markets

• Select variables for analysis these should be
  based on them providing meaningful ways to define
  the needs of customers.
• Define an overall measure to assess the
  similarity of customers on the basis of the needs
  variables.
• Group customers with similar needs. The software
  uses 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).

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Doing Cluster Analysis
a distance from member to cluster
center b distance from I to III
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Single Linkage Cluster Example

• Distance Matrix
• Co1 Co2 Co3 Co4 Co5
• Company 1 0.00Company 2 1.49 0.00Company
  3 3.42 2.29 0.00Company 4 1.81 1.99 1.48 0.00C
  ompany 5 5.05 4.82 4.94 4.83 0.00

ResultingDendogram
1
2
3
Company
4
5
1
2
3
4
5
Distance
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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

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Wards Minimum Variance Agglomerative Clustering
Procedure
98.80
25.18
5.00
0.50
A
B
C
D
E
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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 (e.g., professionals, techno-savvy,
  etc.)?
• Typically, the market is segmented first, and
  then you independently evaluate your ability to
  reach the segments (i.e., separately evaluate
  segmentation and discriminant analysis results).
  However, such an approach may not necessarily
  result in segments that can be targeted.

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Profiling Clusters (PDA Example)
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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.

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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
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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).