Abstract image of a woman sitting on books and studying on a laptop

Principles - Ch3 - Segmentation and Targeting

Download as PPT, PDF
German Jordanian University, profile picture
German Jordanian University

This document discusses segmentation, targeting, and positioning (STP) as core marketing processes. It defines market segmentation as subdividing a market into distinct customer subgroups. Effective segmentation involves identifying segments based on meaningful customer characteristics or needs, then targeting specific segments to reach profitably with tailored marketing programs. The goal of STP is to create and capture more customer value through a focused approach.

Marketing
1 of 69
Download to read offline
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
Segmentation and Targeting  STP (Segmentation, Targeting, and Positioning)  Needs-based segmentation  Cluster Analysis  Discriminant Analysis
Principles Chapter 3: Segmentation and Targeting - 2 © DecisionPro 2007 Market Segmentation  Market segmentation is the subdividing of a market into distinct subsets of customers. Segments  Members are different between segments but similar within.
Principles Chapter 3: Segmentation and Targeting - 3 © DecisionPro 2007 STP is a Core Business Process  To identify and select groups of potential customers...  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) STP - (Segmentation, Targeting, Positioning) is a Decision Process
Principles Chapter 3: Segmentation and Targeting - 4 © DecisionPro 2007 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.
Principles Chapter 3: Segmentation and Targeting - 5 © DecisionPro 2007 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 28.1 26.0 31.5 31.6 Other 32.2 26.8 27.7 29.8 18.6 13.7
Principles Chapter 3: Segmentation and Targeting - 6 © DecisionPro 2007 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 Word Processor Market
Principles Chapter 3: Segmentation and Targeting - 7 © DecisionPro 2007 Segmentation Identify segments Targeting Select segments Positioning Create competitive advantage Marketing resources are focused to better meet customers needs and deliver more value to them Customers develop preference for brands that better meet their needs and deliver more value Customers become brand/supplier loyal, repeat purchase, communicate favorable experiences Brand/supplier loyalty leads to increased market share and creates a barrier to competition Fewer marketing resources needed over time to maintain share due to brand or supplier loyalty Profitability (value to the firm) increases How STP Creates Value
Principles Chapter 3: Segmentation and Targeting - 8 © DecisionPro 2007 The Many Uses of Segmentation Short term segmentation applications:  Salesforce allocation/call planning  Channel assignment  Communication program  Pricing  Today’s competitors and my current relative advantage to the customer
Principles Chapter 3: Segmentation and Targeting - 9 © DecisionPro 2007 The Many Uses of Segmentation Longer term:  Emerging needs  New and evolving market segments to serve  Planning for segment development/growth  Not in kind competition/threats (satisfying customer needs in different ways)  Lead user identification and management  Market driving (vs. customer focused)
A Four-Phase Process for Conducting a Successful Segmentation Project Internal Assessment & Planning • Objective(s) of segmentation • Resources • Constraints Database Review • Primary data already available • Secondary data • … Prototype Implementation Exercises • What ifs? • Relevant groups involved? • ….. Qualitative Research • Interview Materials Development   • Qualitative Data Collection   • “Deep needs” Identification   • Decision- Making Process Assessment   Quantitative Survey • Sample Design    • Questionnaire Development    • Data Collection    Segmentation Analysis • Cluster Analysis • Portfolio Analysis • Positioning Analysis Classification Tool Development • Discriminant function • Binary (CART) tree • … Implementation Through Database Tools • Call Center • Web • Sales call patterns • Promotion • …. Phase I Planning and Design Phase II Qualitative Assessment Phase III Quantitative Measurement Phase IV Analysis and Implementation Basic Idea: Do segmentation analysis on a small (random) sample of customers, but leverage the insights across the entire customer base.
Principles Chapter 3: Segmentation and Targeting - 11 © DecisionPro 2007 Needs-Based Segmentation Distinguish Between Bases and Descriptors  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
Principles Chapter 3: Segmentation and Targeting - 12 © DecisionPro 2007 Relevant Segmentation Descriptor Variable A: Climatic Region 1. Snow Belt 2. Moderate Belt 3. Sun Belt Fraction of Customers Likelihood of Purchasing Solar Water Heater (a) 0 100% Segment 1 Segment 2 Segment 3
Principles Chapter 3: Segmentation and Targeting - 13 © DecisionPro 2007 Likelihood of Purchasing Solar Water Heater (b) Irrelevant Segmentation Descriptor Fraction of Customers 0 100% Variable B: Education 1. Low Education 2. Moderate Education 3. High Education Segment 1 Segment 2 Segment 3
Principles Chapter 3: Segmentation and Targeting - 14 © DecisionPro 2007 Variables to Segment and Describe Markets Consumer Markets B2B Markets Segmentation Needs, wants, benefits, Needs, wants, benefits, solutions to Bases solutions to problems, problems, usage situation, usage rate, usage situation, usage rate. size*, industry*. 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.
Principles Chapter 3: Segmentation and Targeting - 15 © DecisionPro 2007 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
Principles Chapter 3: Segmentation and Targeting - 16 © DecisionPro 2007 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?
Principles Chapter 3: Segmentation and Targeting - 17 © DecisionPro 2007 .. . D . . .. . . Segmentation (for Carpet Fibers) A,B,C,D: Location of segment centers. Typical members: A: schools B: light commercial C: indoor/outdoor carpeting D: health clubs Distance between segments C and D . .. . . . Strength (Importance) Water Resistance (Importance) ..... . .. A ..... . .. . . . . ..... . .. . . . . ... .. .. C B Perceptions/Ratings for one respondent: Customer Values .
Principles Chapter 3: Segmentation and Targeting - 18 © DecisionPro 2007 ..... . .. . . . . ..... . .. . . . . Water Resistance (Importance) Targeting Segment(s) to serve ..... . .. . . ..... . .. . . . . Strength (Importance)
Principles Chapter 3: Segmentation and Targeting - 19 © DecisionPro 2007 Example Criteria for Determining Target Segments to Serve Criterion Examples of Considerations I. Size and Growth 1. Size • Market potential, current market penetration 2. Growth • Past growth forecasts of technology change II. Structural Characteristics 3. Competition • Barriers to entry, barriers to exit, position of competitors, ability to retaliate 4. Segment saturation • Gaps in the market 5. Protectability • Patentability of products, barriers to entry 6. Environmental risk • Economic, political, and technological change III. Product-Market Fit 7. Fit • Coherence with company’s strengths and image 8. Relationships with • Synergy, cost interactions, image transfers, segments cannibalization 9. Profitability • Entry costs, margin levels, return on investment
Principles Chapter 3: Segmentation and Targeting - 20 © DecisionPro 2007 Variable Typical Breakdown Age Under 6, 6-11, 12-19, 20-34, 35-49, 50-64, 65+ Gender Male, female Family size 1-2, 3-4, 5+ Family life Young, single; young, married, no children; young, married, youngest child under six; cycle young, married, oldest child over six; older, married, with children; older, married, no children under 18; older, single; other Income Under $10,000; $10,001-$15,000; $15,001-$20,000; $20,001-$30,000; $30,001-$50,000; $50,001-$100,000; over $100,000 Demographic Segmentation Examples
Principles Chapter 3: Segmentation and Targeting - 21 © DecisionPro 2007 Variable Typical Breakdown Occupation Professional and technical; managers, officials, and proprietors; clerical, sales; craftspeople; farmers; laborers; retired; students; housewives; unemployed Education Grade school or less; some high school; high school; some college; college graduate; graduate degree Religion Catholic, Protestant, Jewish, Muslim, Hindu, other Race White, black, Asian, Hispanic Nationality American, British, French, German, Italian, Japanese Demographic Segmentation Examples
Principles Chapter 3: Segmentation and Targeting - 22 © DecisionPro 2007 Variable Typical Breakdown Lifestyle Activities, interests, opinions Personality Compulsive, gregarious, authoritarian, ambitious Values Sense of belonging, excitement, warm relationships with others, self-fulfillment, security, being well respected, fun and enjoyment of life, self-respect, sense of accomplishment Psychographic Segmentation Examples
Principles Chapter 3: Segmentation and Targeting - 23 © DecisionPro 2007 Variable Typical Breakdown Usage situation Regular occasion, special occasion Benefits Quality, service, economy, speed User status Nonuser, ex-user, potential user, first-time user, regular user Usage rate Light user, medium user, heavy user Loyalty status None, medium, strong, absolute Readiness stage Unaware, aware, informed, interested, desirous, intending to buy Attitude toward Enthusiastic, positive, indifferent, product negative, hostile Behavioral Segmentation Examples
Principles Chapter 3: Segmentation and Targeting - 24 © DecisionPro 2007 Respondent Selection/Aggregation Issues  Who makes the purchasing decision:? DMU (decision making unit)?  Roles of individuals  Purchasing agent?  User?  Specifier?  Gatekeeper?  Financial analyst?  How many respondents per unit?  If more than one, how to aggregate?
Principles Chapter 3: Segmentation and Targeting - 25 © DecisionPro 2007 4. Define Market Segments  Assume the following data matrix includes the responses from six customers on four key components of value (10 point rating scale of importance)  How would you segment this “market”? Customer Importance of Durability Importance of Service Importance of Ease of Use Importance of Price Cluster Assignment 1 9 4 5 6 2 3 5 10 5 3 4 5 5 10 4 10 6 7 6 5 5 5 7 10 6 6 3 9 5 “Market” 6.2 4.7 6.3 7.0 X
Principles Chapter 3: Segmentation and Targeting - 26 © DecisionPro 2007 Segmentation Process Summary 1. Articulate a strategic rationale for segmentation (i.e., why are we segmenting this market?). 2. Select a set of needs-based segmentation variables most useful for achieving the strategic goals. 3. Select a cluster analysis procedure for aggregating (or disaggregating customers) into segments. 4. Group customers into a defined number of different segments and describe them…and target them with descriptor data. 5. Target the segments that will best serve the firm’s strategy, given its capabilities and the likely reactions of competitors.
Principles Chapter 3: Segmentation and Targeting - 27 © DecisionPro 2007 Segmentation: Methods Overview  Factor analysis (to reduce data before cluster analysis).  Cluster analysis to form segments.  Discriminant analysis to describe segments.
Principles Chapter 3: Segmentation and Targeting - 28 © DecisionPro 2007 Cluster Analysis for Segmenting Markets  Select variables for analysis – these should be based on their potential for 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 “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).
Principles Chapter 3: Segmentation and Targeting - 29 © DecisionPro 2007 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.  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).
Principles Chapter 3: Segmentation and Targeting - 30 © DecisionPro 2007 Doing Cluster Analysis Dimension 2 Dimension 1 • • • • • • • • • • • • Perceptions or ratings data from one respondent III a I II b a = distance from member to cluster center b = distance from I to III
Principles Chapter 3: Segmentation and Targeting - 31 © DecisionPro 2007 Cluster Analysis: Measures of Association  Choosing Variables  Variables with similar values  Variables with different values  Defining Measures of similarity between individuals  Scaled data  distance type measures  Nominal data  matching-type measures  Mixed data  Automatic Interaction
Principles Chapter 3: Segmentation and Targeting - 32 © DecisionPro 2007 Matching Coefficients Essential Features? (Yes or No) F1 F2 F3 F4 F5 F6 F7 F8 Org. A Y Y N N Y Y Y Y Org. B N Y N N N Y Y Y Org. C Y N Y Y Y N N N Org. D Y N N N Y Y Y Y
Principles Chapter 3: Segmentation and Targeting - 33 © DecisionPro 2007 ________________ Number of Matches Total Possible Matches Similarity Coefficient = A B C D A 1 B 6/8 1 C 2/8 0/8 1 D 7/8 5/8 3/8 1
Principles Chapter 3: Segmentation and Targeting - 34 © DecisionPro 2007 Distance Measures  Measures of Dissimilarity  Euclidean distance  Absolute distance  Measures of Similarity  Correlation coefficient rxy 2 2 1 1 ) ( .... ) ( nj ni j i x x x x      nj ni j i x x x x      ...... 1 1
Principles Chapter 3: Segmentation and Targeting - 35 © DecisionPro 2007 Euclidean Distance Measure Scaling Problem Standardize Data : To subtract mean and divide by Std Dev 2 2 1 1 ) ( ... ) ( tan nj ni j i x x x x ce is EuclideanD     
Principles Chapter 3: Segmentation and Targeting - 36 © DecisionPro 2007 Euclidean Distance Measure Income (Thousands) Age (years) A 34 27 B 23 34 C 55 38 A B C A 1.00 B 13.04 1.00 C 23.71 32.25 1 mean 37.33 33.00 Std Dev 16.26 5.57 Income (Thousands) Age (years) A -0.205 -1.078 B -0.882 0.180 C 1.087 0.898 A B C A 1.00 B 1.43 1.00 C 2.36 2.10 1  Normal Data Standardized data  Euclidean Distance
Principles Chapter 3: Segmentation and Targeting - 37 © DecisionPro 2007 Scaling Problem Income (Thousands) Age (Months) A -0.205 -1.078 B -0.882 0.180 C 1.087 0.898 A B C A 1.00 B 1.43 1.00 C 2.36 2.10 1 Income (Thousands) Age (Months) A 34 324 B 23 408 C 55 456 A B C A 1.00 B 84.72 1.00 C 133.66 57.69 1  Normal Data Standardized data  Euclidean Distance mean 37.33 33.00 Std Dev 16.26 5.57
Principles Chapter 3: Segmentation and Targeting - 38 © DecisionPro 2007 Clustering Methods  Hierarchical Methods  Build-up (Agglomerative) ► Joining clusters using: • Single Linkage • Complete • Average • Ward’s  Split-down (Divisive) ► Automatic Interaction Detection (AID)  Partitioning Methods  K-means clustering
Principles Chapter 3: Segmentation and Targeting - 39 © DecisionPro 2007  The single linkage method is based on minimum distance, or the nearest neighbor rule. At every stage, the distance between two clusters is the distance between their two closest points.  The complete linkage method is similar to single linkage, except that it is based on the maximum distance or the furthest neighbor approach. In complete linkage, the distance between two clusters is calculated as the distance between their two furthest points.  The average linkage method works similarly. However, in this method, the distance between two clusters is defined as the average of the distances between all pairs of objects, where one member of the pair is from each of the clusters. Conducting Cluster Analysis Select a Clustering Procedure – Linkage Method
Principles Chapter 3: Segmentation and Targeting - 40 © DecisionPro 2007 Linkage Methods of Clustering Single Linkage Minimum Distance Complete Linkage Maximum Distance Average Linkage Average Distance Cluster 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2
Principles Chapter 3: Segmentation and Targeting - 41 © DecisionPro 2007 Other Agglomerative Clustering Methods Ward’s Procedure Centroid Method
Principles Chapter 3: Segmentation and Targeting - 42 © DecisionPro 2007 Single Linkage Cluster Example Distance Matrix Co#1 Co#2 Co#3 Co#4 Co#5 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 Company #5 5.05 4.82 4.94 4.83 0.00 Resulting Dendogram 1 2 3 4 5 1 2 3 4 5 Company Distance
Principles Chapter 3: Segmentation and Targeting - 43 © DecisionPro 2007 Ward’s 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
Principles Chapter 3: Segmentation and Targeting - 44 © DecisionPro 2007 A 98.80 Ward’s Minimum Variance Agglomerative Clustering Procedure B C D E 25.18 5.00 0.50
Principles Chapter 3: Segmentation and Targeting - 45 © DecisionPro 2007 Segmentation Example  The single linkage method was used to create the following dendogram. Group the elements into segments as follows: 1. Two segments. List elements in each segment 2. Three segments. List elements in each segment 3. Five segments. List elements in each segment
Principles Chapter 3: Segmentation and Targeting - 46 © DecisionPro 2007 Segmentation Example
Principles Chapter 3: Segmentation and Targeting - 47 © DecisionPro 2007 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 we 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.
Principles Chapter 3: Segmentation and Targeting - 48 © DecisionPro 2007 Profiling Clusters  Give a description of the clusters based on:  Variables used for clustering (bases)  Variables withheld from clustering, but will be used to identify and target segments (the descriptors)  Report the average value of both
Principles Chapter 3: Segmentation and Targeting - 49 © DecisionPro 2007 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.
Principles Chapter 3: Segmentation and Targeting - 50 © DecisionPro 2007  Discriminant score (Z-score) – basis for predicting to which group the particular individual belongs and is determined by a linear function. Each respondent is assigned a score by the calculated discriminant function. Zi = b1X1i + b2X2i ⋅ ⋅ ⋅ + bnXni  Zi = ith individual’s discriminant score  bn = discriminant coefficient for the nth variable  Xni = individual’s value on the nth independent variable Analysis of Dependence 17-50
Principles Chapter 3: Segmentation and Targeting - 51 © DecisionPro 2007 Behavior-Based Segmentation: Choice Models Expected (gross) customer profitability = Probability of purchase x Likely purchase volume if a purchase is made x Profit margin (for this customer)
Principles Chapter 3: Segmentation and Targeting - 52 © DecisionPro 2007 Behavior-Based Segmentation: Choice Models 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 $1.87
Principles Chapter 3: Segmentation and Targeting - 53 © DecisionPro 2007 A Key Result from this Specification: The Multinomial Logit (MNL) Model eVij pij = ––––––  eVik k where: pij = probability that customer i chooses supplier j. Vij = estimated value of utility (ie, based on estimates of bijk) obtained from maximum likelihood estimation. ^ ^ ^
Principles Chapter 3: Segmentation and Targeting - 54 © DecisionPro 2007 Key idea: Segment on the basis of probability of choice— 1. Loyal to us 2. Loyal to competitor 3. Switchables: loseable/winnable customers Applying the MNL Model in Segmentation Studies
Principles Chapter 3: Segmentation and Targeting - 55 © DecisionPro 2007 Switchability Segmentation Current Product-Market by Switchability (ABB Procedure) Questions: Where should your marketing efforts be focused? How can you segment the market this way? Loyal to Us Losable Winnable Customers (business to gain) Loyal to Competitor
Principles Chapter 3: Segmentation and Targeting - 56 © DecisionPro 2007 Switchability Segmentation  Loyal, if the firm is a current customer of and its probability of choosing us is greater than 0.80.  Competitive, if the firm is a current customer of us and its choice probability is less than 0.80.  Lost, if the firm is: (1) not a current customer, and (2) its probability of buying from one of the competitors is greater than 0.80, or its probability of buying from ABB is less than 0.15.  Switchable, if none of the above applies.
Principles Chapter 3: Segmentation and Targeting - 57 © DecisionPro 2007 Cus. ID Purchase Volume ($) District Choice A (US) B C D Type 1 761 1 B 15.30% 82.27% 2.42% 0.01% Loyal to B 2 627 1 D 0.00% 0.00% 2.61% 97.39% Loyal to D 3 643 2 A 74.70% 25.29% 0.01% 0.00% Comp 4 562 3 D 48.79% 39.73% 0.00% 11.48% Losable 5 469 3 C 1.97% 0.01% 98.02% 0.00% Loyal to C 6 233 1 B 0.01% 96.85% 3.09% 0.04% Loyal to B 7 664 3 D 40.47% 7.69% 0.08% 51.76% Winnable
Principles Chapter 3: Segmentation and Targeting - 58 © DecisionPro 2007 Cus. ID Purchase Volume ($) District Choice A (US) B C D Type 1 761 1 B 15.30% 82.27% 2.42% 0.01% 2 627 1 D 0.00% 0.00% 2.61% 97.39% 3 643 2 A 74.70% 25.29% 0.01% 0.00% 4 562 3 D 48.79% 39.73% 0.00% 11.48% 5 469 3 C 1.97% 0.01% 98.02% 0.00% 6 233 1 B 0.01% 96.85% 3.09% 0.04% 7 664 3 D 40.47% 7.69% 0.08% 51.76%
Principles Chapter 3: Segmentation and Targeting - 59 © DecisionPro 2007 Profiling Clusters Two Cluster Solution for PC Data: Need-Based Variables size power office use LAN storage needs color periph. wide connect. budget –1 1 0 Business Design Means of Variables
Principles Chapter 3: Segmentation and Targeting - 60 © DecisionPro 2007 PDA Visual profile 0 0.5 1 1.5 2 2.5 INNO VATO R USE_M ESSAG E USE_CELL USE_PIM INF_PASSIVE INF_ACTIVE REM O TE_ACC SHARE_INF M O NITO R EM AIL W EB M _M EDIA ERG O N O M IC M O NTH LY PRICE Series1 Series2 Series3 Series4
Principles Chapter 3: Segmentation and Targeting - 61 © DecisionPro 2007 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.
Principles Chapter 3: Segmentation and Targeting - 62 © DecisionPro 2007 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.
Principles Chapter 3: Segmentation and Targeting - 63 © DecisionPro 2007 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.
Principles Chapter 3: Segmentation and Targeting - 64 © DecisionPro 2007 Two-Group Discriminant Analysis Firm Profitability Number of Employees XXOXOOO XXXOXXOOOO XXXXOOOXOOO XXOXXOXOOOO XXOXOOOOOOO X-segment O-segment x = High need for wireless internet o = Low need for wireless internet Discriminant function
Principles Chapter 3: Segmentation and Targeting - 65 © DecisionPro 2007 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).
Principles Chapter 3: Segmentation and Targeting - 66 © DecisionPro 2007 Successful Segmentation Results in a Simple-to-Use Targeting Tool Classify a potential customer/target into segments. Discriminant Function Approach Compute discriminant function scores for each customer/ prospect on each discriminant function, and assign the customer to the segment that has its centroid closest to that customer. Other Approaches (e.g., Tree-based) for Assignment Classification and Regression Trees (CART)
Principles Chapter 3: Segmentation and Targeting - 67 © DecisionPro 2007 Obtaining Data for Needs-Based Segmentation Studies  Conduct traditional marketing research surveys for gathering needs data from a random sample of respondents. Often, a qualitative study (e.g., focus group) is conducted to determine the appropriate needs variables.  Gather data through a “Conjoint” study.  Obtain descriptor data through a combination of secondary and primary data sources.
Principles Chapter 3: Segmentation and Targeting - 68 © DecisionPro 2007 A Good Segmentation Study …  Identifies segments of customers with differentiated needs.  How many different segments?  How do their needs differ?  Enables segments to be separately targeted/reached (this can be problematic even if segments have distinctly different needs).  Finds one or more attractive segments (i.e. a profitable and separate marketing program can be designed for selected segments).  Facilitates the implementation of the segmentation scheme as an ongoing process, not a discrete project.
Principles Chapter 3: Segmentation and Targeting - 69 © DecisionPro 2007 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).

More Related Content

PPT
RANJEET NIRMAL FINALwelingkar project on new fearuter.ppt
firstlevel
 
PPT
04 marketingsegmentationtargetingandpositioning-110403205125-phpapp02
Khaled Tarawneh
 
PPT
Market Segmentation, Targeting and Positioning.ppt
shruti334315
 
PPT
Marketing channel selection
kongara
 
PPT
Mba i mm-1 u-3.1 stp
Rai University
 
PPTX
PrinciplesofMarketing_03_SegmentationandTargeting.pptx
YldRe
 
PDF
Segmentasi, Targeting & Positioning
Nur Agustinus
 
PPT
04 marketing segmentation,targeting and positioning
Thejus Jayadev
 
RANJEET NIRMAL FINALwelingkar project on new fearuter.ppt
firstlevel
 
04 marketingsegmentationtargetingandpositioning-110403205125-phpapp02
Khaled Tarawneh
 
Market Segmentation, Targeting and Positioning.ppt
shruti334315
 
Marketing channel selection
kongara
 
Mba i mm-1 u-3.1 stp
Rai University
 
PrinciplesofMarketing_03_SegmentationandTargeting.pptx
YldRe
 
Segmentasi, Targeting & Positioning
Nur Agustinus
 
04 marketing segmentation,targeting and positioning
Thejus Jayadev
 

Similar to Principles - Ch3 - Segmentation and Targeting (20)

PPT
Session_6_Segmentation, Targeting, Positioning
PhD.c. Seymur M. GULIYEV
 
PPTX
Chapter 4. segmentation, targeting, positioning
Jags Jagdish
 
PDF
PA240_-_L3_-_Market_segmentation (1).pdf
ABHIJEETN4
 
PPTX
Segmentation, Targeting, and Positioning
Manish Kumar
 
PPTX
marketing (BBA)
Ravi Anand
 
PPTX
Market Segmentation, Targeting, and Positioning - Leonard.pptx
VisualSoft1
 
PDF
Identifying market segments
shaunakbajpai
 
PPT
Segmentation & Targeting in entrepreneurship and management
CoordinacinIngls7
 
PPT
Segmentation & Targetinggggggggggggg.ppt
Linh376544
 
PPT
Segmentation & Targetingsdrtytujuki,m.ppt
Lucy Buitron
 
PPT
Markets Segmentation and Product Positioning
ParamesswariNataraja
 
PPT
Segmentation & Targeting.ppt that focuses
gauravmishra112255
 
PPT
Marketing Management - STP - Segmentation, Trageting and positioning.ppt
SabbineniPoojitha1
 
PPT
Segmentation, Positioning & Targeting.ppt
ripalmadhani2
 
PPT
#5 Market Segmentation, Targeting & Positioning.ppt
Alka392097
 
PPTX
Chapter 3, 4 and 5 - Segmentation, Targeting and Positioning.pptx
Carla Kristina Cruz
 
PPTX
STP Analysis under Marketing Management 1
MAbdulMoid
 
PPTX
STP
StudsPlanet.com
 
PPT
Segmentation, Targeting & Positioning
Chaitu Royal
 
PPTX
Strategic issues in service marketing
deepu2000
 
Session_6_Segmentation, Targeting, Positioning
PhD.c. Seymur M. GULIYEV
 
Chapter 4. segmentation, targeting, positioning
Jags Jagdish
 
PA240_-_L3_-_Market_segmentation (1).pdf
ABHIJEETN4
 
Segmentation, Targeting, and Positioning
Manish Kumar
 
marketing (BBA)
Ravi Anand
 
Market Segmentation, Targeting, and Positioning - Leonard.pptx
VisualSoft1
 
Identifying market segments
shaunakbajpai
 
Segmentation & Targeting in entrepreneurship and management
CoordinacinIngls7
 
Segmentation & Targetinggggggggggggg.ppt
Linh376544
 
Segmentation & Targetingsdrtytujuki,m.ppt
Lucy Buitron
 
Markets Segmentation and Product Positioning
ParamesswariNataraja
 
Segmentation & Targeting.ppt that focuses
gauravmishra112255
 
Marketing Management - STP - Segmentation, Trageting and positioning.ppt
SabbineniPoojitha1
 
Segmentation, Positioning & Targeting.ppt
ripalmadhani2
 
#5 Market Segmentation, Targeting & Positioning.ppt
Alka392097
 
Chapter 3, 4 and 5 - Segmentation, Targeting and Positioning.pptx
Carla Kristina Cruz
 
STP Analysis under Marketing Management 1
MAbdulMoid
 
STP
StudsPlanet.com
 
Segmentation, Targeting & Positioning
Chaitu Royal
 
Strategic issues in service marketing
deepu2000
 
Ad

Recently uploaded (20)

PPTX
CH 2 The Role of IMC in the Marketing Process (combined)
jaymundharikar4075
 
PPTX
You_Exec_-_Root_Cause_Analysis_Toolbox_Light_Free (1).pptx
shooxruxb
 
PDF
Salmanubnu Zakariya P – Digital Marketer & Frontend Developer Portfolio
salmanzak07
 
PDF
The Role of Search Intent in Shaping SEO Strategies in 2025
Jomer Gregorio
 
PPTX
APA Examples Reference Examples Style and
fliwertyfw154
 
PPTX
Fixing AI Hallucinations: The NeuroRank™ Approach
Pulp Strategy
 
PDF
Dream Powell - Project and Portfolio 3: Marketing
Dream Powell
 
PPTX
Best Mobile App Development Company in Lucknow
learningstudio62
 
PPTX
Unit 2 - Architects Act, COA n competitions.pptx
smoin914
 
PDF
AI powered Digital Marketing- How AI changes
AJAYVELIVOLU
 
PDF
6 AI Marketing Myths That Are Slowing You Down & Draining Your Budgets
Search Engine Journal
 
PPTX
Mastering in Website Competitor Analysis
MdAbdusSalamKhan3
 
PPTX
AI Search Demystified: Future‑Proof Your Brand For AI‑Driven Queries
Search Engine Journal
 
PDF
Social Media Optimization Basic Introduction
muhammadshaa7002
 
PDF
EX Kathmandu _Kailash Mansarovar Yatra 2025 by Nagarjuna Travels.pdf
Nagarjuna_Travels
 
PDF
Top-Rated Marketplaces to Buy Verified Cash App Accounts Safely.pdf
How To Buy Verified Cash App Accounts
 
PPTX
Opening presentation of Sangam Hospital Bodeli
shailendrasinh1
 
PDF
It Takes a Village Campaign Plan Book; Sidra Medicine
mahnoorahmeransari
 
PPT
Market Segmentation and Positioning(3).ppt
KartikeyaThadani1
 
PPTX
Best LLM SEO Tools for B2B Brands in 2025
Pulp Strategy
 
CH 2 The Role of IMC in the Marketing Process (combined)
jaymundharikar4075
 
You_Exec_-_Root_Cause_Analysis_Toolbox_Light_Free (1).pptx
shooxruxb
 
Salmanubnu Zakariya P – Digital Marketer & Frontend Developer Portfolio
salmanzak07
 
The Role of Search Intent in Shaping SEO Strategies in 2025
Jomer Gregorio
 
APA Examples Reference Examples Style and
fliwertyfw154
 
Fixing AI Hallucinations: The NeuroRank™ Approach
Pulp Strategy
 
Dream Powell - Project and Portfolio 3: Marketing
Dream Powell
 
Best Mobile App Development Company in Lucknow
learningstudio62
 
Unit 2 - Architects Act, COA n competitions.pptx
smoin914
 
AI powered Digital Marketing- How AI changes
AJAYVELIVOLU
 
6 AI Marketing Myths That Are Slowing You Down & Draining Your Budgets
Search Engine Journal
 
Mastering in Website Competitor Analysis
MdAbdusSalamKhan3
 
AI Search Demystified: Future‑Proof Your Brand For AI‑Driven Queries
Search Engine Journal
 
Social Media Optimization Basic Introduction
muhammadshaa7002
 
EX Kathmandu _Kailash Mansarovar Yatra 2025 by Nagarjuna Travels.pdf
Nagarjuna_Travels
 
Top-Rated Marketplaces to Buy Verified Cash App Accounts Safely.pdf
How To Buy Verified Cash App Accounts
 
Opening presentation of Sangam Hospital Bodeli
shailendrasinh1
 
It Takes a Village Campaign Plan Book; Sidra Medicine
mahnoorahmeransari
 
Market Segmentation and Positioning(3).ppt
KartikeyaThadani1
 
Best LLM SEO Tools for B2B Brands in 2025
Pulp Strategy
 
Ad

Principles - Ch3 - Segmentation and Targeting

  • 1. Segmentation and Targeting  STP (Segmentation, Targeting, and Positioning)  Needs-based segmentation  Cluster Analysis  Discriminant Analysis
  • 2. Principles Chapter 3: Segmentation and Targeting - 2 © DecisionPro 2007 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. Principles Chapter 3: Segmentation and Targeting - 3 © DecisionPro 2007 STP is a Core Business Process  To identify and select groups of potential customers...  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) STP - (Segmentation, Targeting, Positioning) is a Decision Process
  • 4. Principles Chapter 3: Segmentation and Targeting - 4 © DecisionPro 2007 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.
  • 5. Principles Chapter 3: Segmentation and Targeting - 5 © DecisionPro 2007 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 28.1 26.0 31.5 31.6 Other 32.2 26.8 27.7 29.8 18.6 13.7
  • 6. Principles Chapter 3: Segmentation and Targeting - 6 © DecisionPro 2007 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 Word Processor Market
  • 7. Principles Chapter 3: Segmentation and Targeting - 7 © DecisionPro 2007 Segmentation Identify segments Targeting Select segments Positioning Create competitive advantage Marketing resources are focused to better meet customers needs and deliver more value to them Customers develop preference for brands that better meet their needs and deliver more value Customers become brand/supplier loyal, repeat purchase, communicate favorable experiences Brand/supplier loyalty leads to increased market share and creates a barrier to competition Fewer marketing resources needed over time to maintain share due to brand or supplier loyalty Profitability (value to the firm) increases How STP Creates Value
  • 8. Principles Chapter 3: Segmentation and Targeting - 8 © DecisionPro 2007 The Many Uses of Segmentation Short term segmentation applications:  Salesforce allocation/call planning  Channel assignment  Communication program  Pricing  Today’s competitors and my current relative advantage to the customer
  • 9. Principles Chapter 3: Segmentation and Targeting - 9 © DecisionPro 2007 The Many Uses of Segmentation Longer term:  Emerging needs  New and evolving market segments to serve  Planning for segment development/growth  Not in kind competition/threats (satisfying customer needs in different ways)  Lead user identification and management  Market driving (vs. customer focused)
  • 10. A Four-Phase Process for Conducting a Successful Segmentation Project Internal Assessment & Planning • Objective(s) of segmentation • Resources • Constraints Database Review • Primary data already available • Secondary data • … Prototype Implementation Exercises • What ifs? • Relevant groups involved? • ….. Qualitative Research • Interview Materials Development   • Qualitative Data Collection   • “Deep needs” Identification   • Decision- Making Process Assessment   Quantitative Survey • Sample Design    • Questionnaire Development    • Data Collection    Segmentation Analysis • Cluster Analysis • Portfolio Analysis • Positioning Analysis Classification Tool Development • Discriminant function • Binary (CART) tree • … Implementation Through Database Tools • Call Center • Web • Sales call patterns • Promotion • …. Phase I Planning and Design Phase II Qualitative Assessment Phase III Quantitative Measurement Phase IV Analysis and Implementation Basic Idea: Do segmentation analysis on a small (random) sample of customers, but leverage the insights across the entire customer base.
  • 11. Principles Chapter 3: Segmentation and Targeting - 11 © DecisionPro 2007 Needs-Based Segmentation Distinguish Between Bases and Descriptors  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
  • 12. Principles Chapter 3: Segmentation and Targeting - 12 © DecisionPro 2007 Relevant Segmentation Descriptor Variable A: Climatic Region 1. Snow Belt 2. Moderate Belt 3. Sun Belt Fraction of Customers Likelihood of Purchasing Solar Water Heater (a) 0 100% Segment 1 Segment 2 Segment 3
  • 13. Principles Chapter 3: Segmentation and Targeting - 13 © DecisionPro 2007 Likelihood of Purchasing Solar Water Heater (b) Irrelevant Segmentation Descriptor Fraction of Customers 0 100% Variable B: Education 1. Low Education 2. Moderate Education 3. High Education Segment 1 Segment 2 Segment 3
  • 14. Principles Chapter 3: Segmentation and Targeting - 14 © DecisionPro 2007 Variables to Segment and Describe Markets Consumer Markets B2B Markets Segmentation Needs, wants, benefits, Needs, wants, benefits, solutions to Bases solutions to problems, problems, usage situation, usage rate, usage situation, usage rate. size*, industry*. 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.
  • 15. Principles Chapter 3: Segmentation and Targeting - 15 © DecisionPro 2007 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
  • 16. Principles Chapter 3: Segmentation and Targeting - 16 © DecisionPro 2007 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. Principles Chapter 3: Segmentation and Targeting - 17 © DecisionPro 2007 .. . D . . .. . . Segmentation (for Carpet Fibers) A,B,C,D: Location of segment centers. Typical members: A: schools B: light commercial C: indoor/outdoor carpeting D: health clubs Distance between segments C and D . .. . . . Strength (Importance) Water Resistance (Importance) ..... . .. A ..... . .. . . . . ..... . .. . . . . ... .. .. C B Perceptions/Ratings for one respondent: Customer Values .
  • 18. Principles Chapter 3: Segmentation and Targeting - 18 © DecisionPro 2007 ..... . .. . . . . ..... . .. . . . . Water Resistance (Importance) Targeting Segment(s) to serve ..... . .. . . ..... . .. . . . . Strength (Importance)
  • 19. Principles Chapter 3: Segmentation and Targeting - 19 © DecisionPro 2007 Example Criteria for Determining Target Segments to Serve Criterion Examples of Considerations I. Size and Growth 1. Size • Market potential, current market penetration 2. Growth • Past growth forecasts of technology change II. Structural Characteristics 3. Competition • Barriers to entry, barriers to exit, position of competitors, ability to retaliate 4. Segment saturation • Gaps in the market 5. Protectability • Patentability of products, barriers to entry 6. Environmental risk • Economic, political, and technological change III. Product-Market Fit 7. Fit • Coherence with company’s strengths and image 8. Relationships with • Synergy, cost interactions, image transfers, segments cannibalization 9. Profitability • Entry costs, margin levels, return on investment
  • 20. Principles Chapter 3: Segmentation and Targeting - 20 © DecisionPro 2007 Variable Typical Breakdown Age Under 6, 6-11, 12-19, 20-34, 35-49, 50-64, 65+ Gender Male, female Family size 1-2, 3-4, 5+ Family life Young, single; young, married, no children; young, married, youngest child under six; cycle young, married, oldest child over six; older, married, with children; older, married, no children under 18; older, single; other Income Under $10,000; $10,001-$15,000; $15,001-$20,000; $20,001-$30,000; $30,001-$50,000; $50,001-$100,000; over $100,000 Demographic Segmentation Examples
  • 21. Principles Chapter 3: Segmentation and Targeting - 21 © DecisionPro 2007 Variable Typical Breakdown Occupation Professional and technical; managers, officials, and proprietors; clerical, sales; craftspeople; farmers; laborers; retired; students; housewives; unemployed Education Grade school or less; some high school; high school; some college; college graduate; graduate degree Religion Catholic, Protestant, Jewish, Muslim, Hindu, other Race White, black, Asian, Hispanic Nationality American, British, French, German, Italian, Japanese Demographic Segmentation Examples
  • 22. Principles Chapter 3: Segmentation and Targeting - 22 © DecisionPro 2007 Variable Typical Breakdown Lifestyle Activities, interests, opinions Personality Compulsive, gregarious, authoritarian, ambitious Values Sense of belonging, excitement, warm relationships with others, self-fulfillment, security, being well respected, fun and enjoyment of life, self-respect, sense of accomplishment Psychographic Segmentation Examples
  • 23. Principles Chapter 3: Segmentation and Targeting - 23 © DecisionPro 2007 Variable Typical Breakdown Usage situation Regular occasion, special occasion Benefits Quality, service, economy, speed User status Nonuser, ex-user, potential user, first-time user, regular user Usage rate Light user, medium user, heavy user Loyalty status None, medium, strong, absolute Readiness stage Unaware, aware, informed, interested, desirous, intending to buy Attitude toward Enthusiastic, positive, indifferent, product negative, hostile Behavioral Segmentation Examples
  • 24. Principles Chapter 3: Segmentation and Targeting - 24 © DecisionPro 2007 Respondent Selection/Aggregation Issues  Who makes the purchasing decision:? DMU (decision making unit)?  Roles of individuals  Purchasing agent?  User?  Specifier?  Gatekeeper?  Financial analyst?  How many respondents per unit?  If more than one, how to aggregate?
  • 25. Principles Chapter 3: Segmentation and Targeting - 25 © DecisionPro 2007 4. Define Market Segments  Assume the following data matrix includes the responses from six customers on four key components of value (10 point rating scale of importance)  How would you segment this “market”? Customer Importance of Durability Importance of Service Importance of Ease of Use Importance of Price Cluster Assignment 1 9 4 5 6 2 3 5 10 5 3 4 5 5 10 4 10 6 7 6 5 5 5 7 10 6 6 3 9 5 “Market” 6.2 4.7 6.3 7.0 X
  • 26. Principles Chapter 3: Segmentation and Targeting - 26 © DecisionPro 2007 Segmentation Process Summary 1. Articulate a strategic rationale for segmentation (i.e., why are we segmenting this market?). 2. Select a set of needs-based segmentation variables most useful for achieving the strategic goals. 3. Select a cluster analysis procedure for aggregating (or disaggregating customers) into segments. 4. Group customers into a defined number of different segments and describe them…and target them with descriptor data. 5. Target the segments that will best serve the firm’s strategy, given its capabilities and the likely reactions of competitors.
  • 27. Principles Chapter 3: Segmentation and Targeting - 27 © DecisionPro 2007 Segmentation: Methods Overview  Factor analysis (to reduce data before cluster analysis).  Cluster analysis to form segments.  Discriminant analysis to describe segments.
  • 28. Principles Chapter 3: Segmentation and Targeting - 28 © DecisionPro 2007 Cluster Analysis for Segmenting Markets  Select variables for analysis – these should be based on their potential for 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 “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).
  • 29. Principles Chapter 3: Segmentation and Targeting - 29 © DecisionPro 2007 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.  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).
  • 30. Principles Chapter 3: Segmentation and Targeting - 30 © DecisionPro 2007 Doing Cluster Analysis Dimension 2 Dimension 1 • • • • • • • • • • • • Perceptions or ratings data from one respondent III a I II b a = distance from member to cluster center b = distance from I to III
  • 31. Principles Chapter 3: Segmentation and Targeting - 31 © DecisionPro 2007 Cluster Analysis: Measures of Association  Choosing Variables  Variables with similar values  Variables with different values  Defining Measures of similarity between individuals  Scaled data  distance type measures  Nominal data  matching-type measures  Mixed data  Automatic Interaction
  • 32. Principles Chapter 3: Segmentation and Targeting - 32 © DecisionPro 2007 Matching Coefficients Essential Features? (Yes or No) F1 F2 F3 F4 F5 F6 F7 F8 Org. A Y Y N N Y Y Y Y Org. B N Y N N N Y Y Y Org. C Y N Y Y Y N N N Org. D Y N N N Y Y Y Y
  • 33. Principles Chapter 3: Segmentation and Targeting - 33 © DecisionPro 2007 ________________ Number of Matches Total Possible Matches Similarity Coefficient = A B C D A 1 B 6/8 1 C 2/8 0/8 1 D 7/8 5/8 3/8 1
  • 34. Principles Chapter 3: Segmentation and Targeting - 34 © DecisionPro 2007 Distance Measures  Measures of Dissimilarity  Euclidean distance  Absolute distance  Measures of Similarity  Correlation coefficient rxy 2 2 1 1 ) ( .... ) ( nj ni j i x x x x      nj ni j i x x x x      ...... 1 1
  • 35. Principles Chapter 3: Segmentation and Targeting - 35 © DecisionPro 2007 Euclidean Distance Measure Scaling Problem Standardize Data : To subtract mean and divide by Std Dev 2 2 1 1 ) ( ... ) ( tan nj ni j i x x x x ce is EuclideanD     
  • 36. Principles Chapter 3: Segmentation and Targeting - 36 © DecisionPro 2007 Euclidean Distance Measure Income (Thousands) Age (years) A 34 27 B 23 34 C 55 38 A B C A 1.00 B 13.04 1.00 C 23.71 32.25 1 mean 37.33 33.00 Std Dev 16.26 5.57 Income (Thousands) Age (years) A -0.205 -1.078 B -0.882 0.180 C 1.087 0.898 A B C A 1.00 B 1.43 1.00 C 2.36 2.10 1  Normal Data Standardized data  Euclidean Distance
  • 37. Principles Chapter 3: Segmentation and Targeting - 37 © DecisionPro 2007 Scaling Problem Income (Thousands) Age (Months) A -0.205 -1.078 B -0.882 0.180 C 1.087 0.898 A B C A 1.00 B 1.43 1.00 C 2.36 2.10 1 Income (Thousands) Age (Months) A 34 324 B 23 408 C 55 456 A B C A 1.00 B 84.72 1.00 C 133.66 57.69 1  Normal Data Standardized data  Euclidean Distance mean 37.33 33.00 Std Dev 16.26 5.57
  • 38. Principles Chapter 3: Segmentation and Targeting - 38 © DecisionPro 2007 Clustering Methods  Hierarchical Methods  Build-up (Agglomerative) ► Joining clusters using: • Single Linkage • Complete • Average • Ward’s  Split-down (Divisive) ► Automatic Interaction Detection (AID)  Partitioning Methods  K-means clustering
  • 39. Principles Chapter 3: Segmentation and Targeting - 39 © DecisionPro 2007  The single linkage method is based on minimum distance, or the nearest neighbor rule. At every stage, the distance between two clusters is the distance between their two closest points.  The complete linkage method is similar to single linkage, except that it is based on the maximum distance or the furthest neighbor approach. In complete linkage, the distance between two clusters is calculated as the distance between their two furthest points.  The average linkage method works similarly. However, in this method, the distance between two clusters is defined as the average of the distances between all pairs of objects, where one member of the pair is from each of the clusters. Conducting Cluster Analysis Select a Clustering Procedure – Linkage Method
  • 40. Principles Chapter 3: Segmentation and Targeting - 40 © DecisionPro 2007 Linkage Methods of Clustering Single Linkage Minimum Distance Complete Linkage Maximum Distance Average Linkage Average Distance Cluster 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2
  • 41. Principles Chapter 3: Segmentation and Targeting - 41 © DecisionPro 2007 Other Agglomerative Clustering Methods Ward’s Procedure Centroid Method
  • 42. Principles Chapter 3: Segmentation and Targeting - 42 © DecisionPro 2007 Single Linkage Cluster Example Distance Matrix Co#1 Co#2 Co#3 Co#4 Co#5 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 Company #5 5.05 4.82 4.94 4.83 0.00 Resulting Dendogram 1 2 3 4 5 1 2 3 4 5 Company Distance
  • 43. Principles Chapter 3: Segmentation and Targeting - 43 © DecisionPro 2007 Ward’s 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
  • 44. Principles Chapter 3: Segmentation and Targeting - 44 © DecisionPro 2007 A 98.80 Ward’s Minimum Variance Agglomerative Clustering Procedure B C D E 25.18 5.00 0.50
  • 45. Principles Chapter 3: Segmentation and Targeting - 45 © DecisionPro 2007 Segmentation Example  The single linkage method was used to create the following dendogram. Group the elements into segments as follows: 1. Two segments. List elements in each segment 2. Three segments. List elements in each segment 3. Five segments. List elements in each segment
  • 46. Principles Chapter 3: Segmentation and Targeting - 46 © DecisionPro 2007 Segmentation Example
  • 47. Principles Chapter 3: Segmentation and Targeting - 47 © DecisionPro 2007 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 we 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.
  • 48. Principles Chapter 3: Segmentation and Targeting - 48 © DecisionPro 2007 Profiling Clusters  Give a description of the clusters based on:  Variables used for clustering (bases)  Variables withheld from clustering, but will be used to identify and target segments (the descriptors)  Report the average value of both
  • 49. Principles Chapter 3: Segmentation and Targeting - 49 © DecisionPro 2007 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.
  • 50. Principles Chapter 3: Segmentation and Targeting - 50 © DecisionPro 2007  Discriminant score (Z-score) – basis for predicting to which group the particular individual belongs and is determined by a linear function. Each respondent is assigned a score by the calculated discriminant function. Zi = b1X1i + b2X2i ⋅ ⋅ ⋅ + bnXni  Zi = ith individual’s discriminant score  bn = discriminant coefficient for the nth variable  Xni = individual’s value on the nth independent variable Analysis of Dependence 17-50
  • 51. Principles Chapter 3: Segmentation and Targeting - 51 © DecisionPro 2007 Behavior-Based Segmentation: Choice Models Expected (gross) customer profitability = Probability of purchase x Likely purchase volume if a purchase is made x Profit margin (for this customer)
  • 52. Principles Chapter 3: Segmentation and Targeting - 52 © DecisionPro 2007 Behavior-Based Segmentation: Choice Models 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 $1.87
  • 53. Principles Chapter 3: Segmentation and Targeting - 53 © DecisionPro 2007 A Key Result from this Specification: The Multinomial Logit (MNL) Model eVij pij = ––––––  eVik k where: pij = probability that customer i chooses supplier j. Vij = estimated value of utility (ie, based on estimates of bijk) obtained from maximum likelihood estimation. ^ ^ ^
  • 54. Principles Chapter 3: Segmentation and Targeting - 54 © DecisionPro 2007 Key idea: Segment on the basis of probability of choice— 1. Loyal to us 2. Loyal to competitor 3. Switchables: loseable/winnable customers Applying the MNL Model in Segmentation Studies
  • 55. Principles Chapter 3: Segmentation and Targeting - 55 © DecisionPro 2007 Switchability Segmentation Current Product-Market by Switchability (ABB Procedure) Questions: Where should your marketing efforts be focused? How can you segment the market this way? Loyal to Us Losable Winnable Customers (business to gain) Loyal to Competitor
  • 56. Principles Chapter 3: Segmentation and Targeting - 56 © DecisionPro 2007 Switchability Segmentation  Loyal, if the firm is a current customer of and its probability of choosing us is greater than 0.80.  Competitive, if the firm is a current customer of us and its choice probability is less than 0.80.  Lost, if the firm is: (1) not a current customer, and (2) its probability of buying from one of the competitors is greater than 0.80, or its probability of buying from ABB is less than 0.15.  Switchable, if none of the above applies.
  • 57. Principles Chapter 3: Segmentation and Targeting - 57 © DecisionPro 2007 Cus. ID Purchase Volume ($) District Choice A (US) B C D Type 1 761 1 B 15.30% 82.27% 2.42% 0.01% Loyal to B 2 627 1 D 0.00% 0.00% 2.61% 97.39% Loyal to D 3 643 2 A 74.70% 25.29% 0.01% 0.00% Comp 4 562 3 D 48.79% 39.73% 0.00% 11.48% Losable 5 469 3 C 1.97% 0.01% 98.02% 0.00% Loyal to C 6 233 1 B 0.01% 96.85% 3.09% 0.04% Loyal to B 7 664 3 D 40.47% 7.69% 0.08% 51.76% Winnable
  • 58. Principles Chapter 3: Segmentation and Targeting - 58 © DecisionPro 2007 Cus. ID Purchase Volume ($) District Choice A (US) B C D Type 1 761 1 B 15.30% 82.27% 2.42% 0.01% 2 627 1 D 0.00% 0.00% 2.61% 97.39% 3 643 2 A 74.70% 25.29% 0.01% 0.00% 4 562 3 D 48.79% 39.73% 0.00% 11.48% 5 469 3 C 1.97% 0.01% 98.02% 0.00% 6 233 1 B 0.01% 96.85% 3.09% 0.04% 7 664 3 D 40.47% 7.69% 0.08% 51.76%
  • 59. Principles Chapter 3: Segmentation and Targeting - 59 © DecisionPro 2007 Profiling Clusters Two Cluster Solution for PC Data: Need-Based Variables size power office use LAN storage needs color periph. wide connect. budget –1 1 0 Business Design Means of Variables
  • 60. Principles Chapter 3: Segmentation and Targeting - 60 © DecisionPro 2007 PDA Visual profile 0 0.5 1 1.5 2 2.5 INNO VATO R USE_M ESSAG E USE_CELL USE_PIM INF_PASSIVE INF_ACTIVE REM O TE_ACC SHARE_INF M O NITO R EM AIL W EB M _M EDIA ERG O N O M IC M O NTH LY PRICE Series1 Series2 Series3 Series4
  • 61. Principles Chapter 3: Segmentation and Targeting - 61 © DecisionPro 2007 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.
  • 62. Principles Chapter 3: Segmentation and Targeting - 62 © DecisionPro 2007 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.
  • 63. Principles Chapter 3: Segmentation and Targeting - 63 © DecisionPro 2007 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.
  • 64. Principles Chapter 3: Segmentation and Targeting - 64 © DecisionPro 2007 Two-Group Discriminant Analysis Firm Profitability Number of Employees XXOXOOO XXXOXXOOOO XXXXOOOXOOO XXOXXOXOOOO XXOXOOOOOOO X-segment O-segment x = High need for wireless internet o = Low need for wireless internet Discriminant function
  • 65. Principles Chapter 3: Segmentation and Targeting - 65 © DecisionPro 2007 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).
  • 66. Principles Chapter 3: Segmentation and Targeting - 66 © DecisionPro 2007 Successful Segmentation Results in a Simple-to-Use Targeting Tool Classify a potential customer/target into segments. Discriminant Function Approach Compute discriminant function scores for each customer/ prospect on each discriminant function, and assign the customer to the segment that has its centroid closest to that customer. Other Approaches (e.g., Tree-based) for Assignment Classification and Regression Trees (CART)
  • 67. Principles Chapter 3: Segmentation and Targeting - 67 © DecisionPro 2007 Obtaining Data for Needs-Based Segmentation Studies  Conduct traditional marketing research surveys for gathering needs data from a random sample of respondents. Often, a qualitative study (e.g., focus group) is conducted to determine the appropriate needs variables.  Gather data through a “Conjoint” study.  Obtain descriptor data through a combination of secondary and primary data sources.
  • 68. Principles Chapter 3: Segmentation and Targeting - 68 © DecisionPro 2007 A Good Segmentation Study …  Identifies segments of customers with differentiated needs.  How many different segments?  How do their needs differ?  Enables segments to be separately targeted/reached (this can be problematic even if segments have distinctly different needs).  Finds one or more attractive segments (i.e. a profitable and separate marketing program can be designed for selected segments).  Facilitates the implementation of the segmentation scheme as an ongoing process, not a discrete project.
  • 69. Principles Chapter 3: Segmentation and Targeting - 69 © DecisionPro 2007 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).