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Segmentation and Targeting

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Title: 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 Characteristics of 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

5
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

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 Segment and 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 for Niches,
    Business Horizons.

10
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)

11
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

12
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

14
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

15
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.

16
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.

17
Market Definition Approaches
  • Customer-Behavior
  • Demand cross elasticity
  • Brand/product switching
  • Perception/Judgment
  • Engineering/technological substitution
  • Customer judgments/ perceptual mapping

18
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?).

19
Electric Typewriter Market
  • 1980 1981 1982 1983 1984 1985
  • Shipments
  • A (Us) 403,027 495,192 548,905 550,351 541,388 515
    ,000 B 369,916 388,520 349,396 323,005 342,197 297
    ,000 Other 367,057 324,010 343,885 370,374 202,495
    129,070 Total 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 54.7 B 32.4 32.2 2
    8.1 26.0 31.5 31.6 Other 32.2 26.8 27.7 29.8 18.6
    13.7

20
Word Processor Market
1980 1981 1982 1983 1984 1985
Shipments A (Us) 403,027 495,192 548,905 550,
351 541,388 515,000 B 369,916 388,520 349,396 323,
005 342,197 297,000 Other Electric 367,057 324,010
343,885 370,374 202,495 129,070 Electronic
Word Processors 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 22.3 B 30.8 29.
4 24.1 19.7 18.8 12.8 Other Electric
30.6 24.5 23.7 22.6 11.1 5.6 Electronic
Word Processors 5.0 8.5 14.4 24.0 40.3 59.3
21
Market Definition by Switching Behavior
  • Current
    Purchase Occasion
  • Diet Diet Diet Coke Coke Pepsi Pepsi Spr
    ite Sprite Total
  • Coke 53 9 27 4 5 2 100 Last Diet
    Coke 12 61 4 15 2 5 100 Purchase Pepsi 24
    3 58 9 5 1 100 Occasion 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

22
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

23
STPing the Market for Eggs
  • Segments Disinterested consumers Casual Egg
    Users Health conscious consumers Enthusiastic
    users
  • Profiles Beliefs Attitudes Lifestyles Health/Nutr
    ition consciousness Media habits Consumption
    habits Demographics
  • Frank and Phillips, Agribusiness, July 1990

24
Targeting and Positioning
25
Overview of Marketing Engineering Methods for STP
  • Clustering and discriminant analysis (PDA
    exercise)
  • Choice-based segmentation (ABB Electric)
  • Perceptual mapping (G20 exercise)

26
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/outdoor carpeting
D health clubs
.
.
.
.
Water Resistance (Importance)
27
Targeting
Segment(s) to serve
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Strength (Importance)
.
.
.
.
.
.
.
.
.
.
.
.
Water Resistance (Importance)
28
Positioning
Product Positioning
.
.
Us
.
Comp 1
Comp 2
Strength (Importance)
Water Resistance (Importance)
29
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?

30
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

33
Segmentation Methods Overview
  • Factor analysis (to reduce data before cluster
    analysis).
  • Cluster analysis to form segments.
  • Discriminant analysis to describe segments.

34
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. 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).

35
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

36
Doing Cluster Analysis
a distance from member to cluster
center b distance from I to III
37
Single Linkage Cluster Example
  • Distance Matrix
  • Co1 Co2 Co3 Co4 Co5
  • Company 1 0.00 Company 2 1.49 0.00 Company
    3 3.42 2.29 0.00 Company 4 1.81 1.99 1.48 0.00 C
    ompany 5 5.05 4.82 4.94 4.83 0.00

Resulting Dendogram
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

39
Wards Minimum Variance Agglomerative Clustering
Procedure
98.80
25.18
5.00
0.50
A
B
C
D
E
40
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).

41
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
42
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 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.

45
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).

47
Behavior-Based Segmentation
  • Traditional segmentation
  • (eg, demographic, psychographic)
  • Needs-based segmentation
  • Behavior-based segmentation
  • (choice models)

48
Choosing a Value 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.

49
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

50
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

51
(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.

52
Attributes in ABBs Choice-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

53
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.

54
A Key Result from this Specification The
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.




55
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.





56
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
58
Switchability Segmentation
Loyal to Us
Losable
Winnable Customers (business to gain)
Loyal to Competitor
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 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 0.5
    6 1.03 10 4 72.00 0.65 1.87

60
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.

61
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?

62
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).

63
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)
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