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 (eg, 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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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

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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

• A Marriott Hotel used to be a Marriott Hotel An
  upscale hotel catering to business people,
  pleasure seekers and international and group
  travelers. Today, besides the Marriott Hotels
  (the companys major business), there are
  Marriott Suites, Residence Inns, Courtyards by
  Marriott and Fairfield Innseach serving a
  smaller, targeted segment of the market.
• Lenneman and Stanton, Mining forNiches,
  Business Horizons.

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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)

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Customers Diverse Needs Require Diverse Channels

• Kodak increases customer contact and support with
  a three tiered distribution system.
• . . . Business Imaging Division created three
  avenues for marketing microfilm, supplies and
  imaging systems and software
• direct sales reps (for more complex systems)
• brokers and distributors (for film sales and
  delivery)
• Components Marketing Division (to sell to system
  integrators and VARs).
• Business Marketing

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Ad in London Newspapers, 1900

• 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 Shakleton, Arctic Explorer
• Did it work?

13
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

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Segmentation Marketing Implies a Market

• A market consists of all the potential customers
  sharing a particular need or want who might be
  willing and able to engage in exchange to satisfy
  that need or want.
• Kotler, Marketing Management

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Market Definition
Customer-Need Set 1 (Market 1)
Product 1
Technology A
Customer-Need Set 2 (Market 2)
Technology B

• ð Common customer needs define a market not a
  product.

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Implications

• 1. Segmentation defines common customer needs.
• 2. Those common needs may be satisfied by
  similar or dissimilar technologies or have
  different solutions.
• Ex Customer dissatisfaction at long delays at
  supermarket checkout.
• Solution 1 Faster UPC scanner systems.
• Solution 2 Entertainment/Sales systems on
  checkout lines.
• Note Total solution defines (competitive)
  market, not product or technology.

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Market Definition Approaches

• Customer-Behavior
• Demand cross elasticity
• Brand/product switching
• Perception/Judgment
• Engineering/technological substitution
• Customer judgments/perceptual mapping

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Why is Market Definition Important?

• Strategy(What to focus on).
• Resource allocation(How much/where/when?).
• Feedback/performance measurement(How well are we
  doing? How can we learn from our actions?).

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Electric Typewriter Market

• 1980 1981 1982 1983 1984 1985
• Shipments
• A (Us) 403,027 495,192 548,905 550,351 541,388 515
  ,000B 369,916 388,520 349,396 323,005 342,197 297
  ,000Other 367,057 324,010 343,885 370,374 202,495
  129,070Total 1,140,000 1,207,722 1,242,186 1,243
  ,730 1,086,080 941,070
• Market Shares ()
• A (Us) 35.4 41.0 44.2 44.2 49.8 54.7B 32.4 32.2 2
  8.1 26.0 31.5 31.6Other 32.2 26.8 27.7 29.8 18.6
  13.7

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Word Processor Market
1980 1981 1982 1983 1984 1985
Shipments A (Us) 403,027 495,192 548,905 550,
351 541,388 515,000B 369,916 388,520 349,396 323,
005 342,197 297,000Other Electric 367,057 324,010
343,885 370,374 202,495 129,070 Electronic
WordProcessors 60,040 112,220 209,800 392,352 733
,699 1,372,016 Total 1,200,040 1,319,942 1,451,986
1,636,082 1,819,778 2,313,086 Market Shares ()
A (Us) 33.6 37.5 37.8 33.6 29.8 22.3B 30.8 29.
4 24.1 19.7 18.8 12.8Other Electric
30.6 24.5 23.7 22.6 11.1 5.6 Electronic
WordProcessors 5.0 8.5 14.4 24.0 40.3 59.3
21
Market Definitionby Switching Behavior

• Current
  Purchase Occasion
• Diet Diet Diet Coke Coke Pepsi Pepsi Spr
  ite Sprite Total
• Coke 53 9 27 4 5 2 100Last Diet
  Coke 12 61 4 15 2 5 100Purchase Pepsi 24
  3 58 9 5 1 100Occasion Diet
  Pepsi 4 14 11 63 2 6 100 Sprite 21 2 17
  3 52 6 100 Diet Sprite 2 15 2 12 7 61
  100

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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

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STPing the Market for Eggs

• Segments Disinterested consumers Casual Egg
  UsersHealth conscious consumers Enthusiastic
  users
• Profiles BeliefsAttitudesLifestylesHealth/Nutr
  ition consciousnessMedia habitsConsumption
  habitsDemographics
• Frank and Phillips,Agribusiness, July 1990

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Targeting and Positioning
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Overview of Marketing Engineering Methods for STP

• Clustering and discriminantanalysis (PDA
  exercise)
• Choice-based segmentation(ABB Electric)
• Perceptual mapping(G20 exercise)

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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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Water Resistance (Importance)
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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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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?

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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.

31
Total Customer Value
Price/Performance

• Functional Value
• (What does this product do for me?)
• Supplier/Service Value
• What does the product mean to me?
• (What is the insurance? service? psychological?
  value of the product or supplier?)

Advertising Selling Service Efforts
32
Customer Value Assessment Procedures

• Attitude-Based Behaviour-Based Inferential/Value
  Based Choice models Internal
  engineering assessment Neural networks
  Indirect survey questions Discriminant
  analysis Field value-in-use assessment
• Indirect/(Decompositional Methods) Direct
  Questions Conjoint analysis Preference
  Regression
• Unconstrainted Constrained/Compositional
  Methods Focus groups Multiattribute value
  analysis Direct survey questions
  Benchmarking Importance and attitude ratings
  Rule-based system/AI/expert systems

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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.

34
Cluster Analysis forSegmenting Markets

• Define a measure to assess the similarity of
  customers on the basis of their needs.
• 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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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

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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
38
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 (eg, professionals, techno-savvy, etc.)?
• Segment the market independently of your ability
  to reach the segments (ie, separately evaluate
  segmentation and discriminant analysis results).

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Profiling Clusters
Two Cluster Solution for PC Data Need-Based
Variables
1
Design
Means of Variables
0
Business
1
size
power
office use
LAN
storage needs
color
periph.
wide connect.
budget
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Which Segments to Serve?Segment Attractiveness
Criteria
43
Selecting Segments to Serve
E
Strong
Firms Competitive Position
B
Medium
D
A
C
Weak
Low
Average
High
Segment Attractiveness
44
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
46
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).

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Behavior-Based Segmentation

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

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Choosing aValue Assessment Method

• Method
• Criterion Value Behavior Compositional
  or Unconstrained Based Based Decompositional
• Amount of customer High Low Medium Low
  information needed
• Number of customers Low High Medium Any
• Good in dynamic/ Yes No Partly Partly
  changing markets?
• Past purchase data Not Needed Not Not
  available? necessary necessary necessary
• Analysis time frame Long Medium Long/Medium Short
• Cost Very high/ Medium High Low respondent
• Insight Very high Medium High Low
• Appropriate for lead users? Yes No Yes No
• Predictive of behavior? High Moderate Moderate Low
  
• If customers can reliably report how they will
  behave after change.

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Choice Models

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

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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

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(ABB) 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.

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Attributes in ABBsChoice-Segmentation Model

• Invoice price
• Energy losses
• Overall product quality
• Availability of spare parts
• Clarity of bid document
• Knowledgeable salespeople
• Maintenance requirement
• Ease of installation
• Warranty

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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.

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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
• å eVik
• k
• 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.

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What Does This Result Imply?

• Interval-level utility measurements are good
  enough. That is
• eVij eVij a pij
• å eVik å eVik a
• k k
• The marginal impact of an attribute is highest
  when the probability of choosing an option j is
  0.5.

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What Does This Result Imply? (contd)
Marginal Impact of an Attribute on the
Probability of Choosing an Option
0.5
Probability of Choosing the Option
57
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
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Switchability Segmentation
Loyal to Us
Losable
Winnable Customers (business to gain)
Loyal toCompetitor
Current Product-Market by Switchability (ABB
Procedure) Questions Where should your marketing
efforts be focused?How can you segment the
market this way?
59
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

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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.

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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?

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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).

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Related Models Described in the Marketing
Engineering Book

• To develop needs variables
• Conjoint Analysis (Chapter 7)
• Other segmentation methods
• Preference-based segmentation (PREFMAP in Chapter
  4)
• To help evaluate and select segments
• Analytic Hierarchy Process (Chapter 6)
• GE Planning Matrix (Chapter 6)