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 • 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

5. A Two-Stage Approachin Business Markets Macro-Segments: • First stage/rough cut • Industry/application • Firm size Micro-Segments: • Second-stage/fine cut • Different customer needs, wants, values within macro-segment

6. Relevant Segmentation Descriptor Variable A: Climatic Region 1. Snow Belt 2. Moderate Belt 3. Sun Belt Fraction of Customers Segment 1 Segment 2 Segment 3 0 100% Likelihood of Purchasing Solar Water Heater (a)

7. Irrelevant Segmentation Descriptor Variable B: Education 1. Low Education 2. Moderate Education 3. High Education Fraction of Customers Segment 1 Segment 2 Segment 3 0 100% Likelihood of Purchasing Solar Water Heater (b)

8. Consumer Industrial Segmentation Needs, wants benefits, Needs, wants benefits, solutions to Bases solutions to problems, problems, usage situation, usage rate, usage situation, usage rate. size*, industrial*. 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. Variables to Segmentand Describe Markets

9. Segmentation in Action We segment our customers by letter volume, by postage volume, by the type of equipment they use. Then we segment on whether they buy or lease equipment. Based on this knowledge, we target our marketing messages, fine tune our sales tactics, learn which benefits appeal to which customers and zero in on key decision makers at a company. —Kathleen Synnot, VP, Worldwide Marketing Mailing Systems Division, Pitney Bowes, Inc. [quoted in Marketing Masters (Walden and Lawler)]

10. Segmentation If you’re not thinking segments, you’re not thinking. To think segments means you have to think about what drives customers, customer groups, and the choices that are or might be available to them. —Levitt, Marketing Imagination

11. STP as Business Strategy Segmentation • Identify segmentation bases and segment the market. • Develop profiles of resulting segments. Targeting • Evaluate attractiveness of each segment. • Select target segments. Positioning • Identify possible positioning concepts for each target segment. • Select, develop, and communicate the chosen concept. … to create and claim value

12. Overview of Methods for STP • Clustering and discriminantanalysis • Choice-based segmentation • Perceptual mapping- later

13. Segmentation (for Carpet Fibers) Perceptions/Ratings for one respondent: Customer Values .. .. . . . . . . . . . . . . . D . . . . A . . . . . . Strength (Importance) .. .. Distance between segments C and D . . . . . . . . B . . . C . . . . . . . . . A,B,C,D: Location of segment centers. Typical members: A: schools B: light commercial C: indoor/outdoorcarpeting D: health clubs . . . . Water Resistance (Importance)

14. . . . . . . . . . . . . Targeting Segment(s) to serve . . . . . . . . . . . . . . . . . . . . . . Strength(Importance) . . . . . . . . . . . . Water Resistance (Importance)

15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Positioning Product Positioning . . Us . Comp 1 Comp 2 Strength(Importance) Water Resistance (Importance)

16. A Note on Positioning Positioning involves designing an offering so that the target segment members perceive it in a distinct and valued way relative to competitors. Three ways to position an offering: 1. Unique (“Only product/service with XXX”) 2. Difference (“More than twice the [feature] vs. [competitor]”) 3. Similarities (“Same functionality as [competitor]; lower price”) What are you telling your targeted segments?

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

18. Steps in a Segmentation Study • Articulate a strategic rationale for segmentation (ie, why are we segmenting this market?). • Select a set of needs-based segmentation variables most useful for achieving the strategic goals. • Select a cluster analysis procedure for aggregating (or disaggregating customers) into segments. • Group customers into a defined number of different segments. • Choose the segments that will best serve the firm’s strategy, given its capabilities and the likely reactions of competitors.

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

20. Cluster Analysis forSegmenting Markets • Define a measure to assess the similarity of customers on the basis of their needs. • Group customers with similar needs. Recommend: the “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).

21. Cluster Analysis Issues • Defining a measure of similarity (or distance) between segments. • Identifying “outliers.” • Selecting a clustering procedure • Hierarchical clustering (e.g., Single linkage, average linkage, and minimum variance methods) • Partitioning methods (e.g., K-Means) • Cluster profiling • Univariate analysis • Multiple discriminant analysis

22. • • • • • Perceptions or ratings datafrom one respondent III Dimension 2 b • • • • • • • a II I Dimension 1 Doing Cluster Analysis a = distance from member to cluster center b = distance from I to III

23. Ward’s Minimum Variance Agglomerative Clustering Procedure First Stage: A = 2 B = 5 C = 9 D = 10 E = 15 Second Stage: AB = 4.5 BD = 12.5 AC = 24.5 BE = 50.0 AD = 32.0 CD = 0.5 AE = 84.5 CE = 18.0 BC = 8.0 DE = 12.5 Third Stage: CDA = 38.0 CDB = 14.0 CDE = 20.66 AB = 5.0 AE = 85.0 BE = 50.5 Fourth Stage: ABCD = 41.0 ABE= 93.17 CDE = 25.18 Fifth Stage: ABCDE = 98.8

24. Ward’s Minimum Variance Agglomerative Clustering Procedure 98.80 25.18 5.00 0.50 A B C D E

25. Discriminant Analysis forDescribing Market Segments • Identify a set of “observable” variables that helps you to understand how to reach and serve the needs of selected clusters. • Use discriminant analysis to identify underlying dimensions (axes) that maximally differentiate between the selected clusters.

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

27. Interpreting Discriminant Analysis Results • What proportion of the total variance in the descriptor data is explained by the statistically significant discriminant axes? • Does the model have good predictability (“hit rate”) in each cluster? • Can you identify good descriptors to find differences between clusters? (Examine correlations between discriminant axes and each descriptor variable).

28. PDA Example

29. PDA – Segmentation • Performs Wards method - Code: proccluster data=hold.pda method=wards standard outtree=treedat pseudo; var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_Active Remote_Acc Share_Inf Monitor Email Web M_Media Ergonomic Monthly Price; run; proctree data=treedat; run;

30. PDA – Segmentation (alternative) • Performs K-means method - Code: procfastclus data=hold.pda maxc=4 maxiter=10 random=41 maxiter=50 out=clus; var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_ActiveRemote_Acc Share_Inf Monitor Email Web M_Media Ergonomic ; run; procmeans data =clus; var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_Active Remote_Acc Share_Inf Monitor Email Web M_Media Ergonomic Monthly Price; by cluster; run;

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

32. Four Cluster Solution – profile code; proc tree data = treedata nclusters=4 out=outclus no print; run; ** create new data set; data temp; merge hold.pda outclus; run; ** profile these segments; procmeans data =temp; var Innovator Use_Message Use_Cell Use_PIM Inf_Passive Inf_Active Remote_Acc Share_Inf Monitor Email Web M_MedErgonomic Monthly Price; by cluster; run;

33. PDA profiles

34. PDA Visual profile

35. PDA Visual profile…

36. PDA profiles • Cluster 1. Phone users who use Personal Information Management software, to whom Email and Web access, as well as Multimedia capabilities are important. • Cluster 2. People who use messaging services and cell phones, need remote access to information, appreciate better monitors, but not for multi-media usage.

37. PDA profiles.. Cluster 3. Pager users who have a high need for fast information sharing (receiving as well as sending) and also remote access. They use neither email extensively, nor the Web, nor Multi-media, but do require a handy, non-bulky device. Cluster 4. Innovators who use cell phones a lot, have a high need for Email, Web, and Multi-media use. They also require a sleek device.

38. Profile based on Demos/behaviour

39. Name the segments Cluster 1 - Sales Pros: Cluster 1 consists mainly of sales professionals: 54% of the cluster members indicated Sales as their occupation. They use the cell phone heavily, and many (45%) own a PDA already; practically all have access to a PC. Their work often takes them away from the office. They mostly read two of the selected magazines: 30% read BW. From the needs data, we see that they are quite price sensitive. Cluster 2 – Service Pros: Cluster 2 is made up primarily of service personnel (39%) and secondarily of sales personnel (23%). They use cell phones heavily, but only about one fifth currently use a PDA. They spend much time on the road and in remote locations. They read PC Magazine, 29%. From the needs data, we see that they are quite price sensitive.

40. Name the segments… Cluster 3 – Hard Hats: Cluster 3 is made up predominantly of construction (31%) and emergency (19%) workers. They use cell phones, but usually do not own a PDA. By the nature of their work, they have high information relay needs and generally work in remote locations. They exchange information with colleagues in the field (e.g. construction workers on the site). Many read Field & Stream (31%) and also PC Magazine. Note also from the needs data, that they are the least price sensitive (willing to pay highest price plus monthly fee) and also have the lowest income. This apparent anomaly occurs because these folks are less likely to have to pay for the device themselves, raising the question of whose preferences—their own or their employers’—will drive the adoption decision

41. Name the segments… Cluster 4 – Innovators: Cluster 4 represents early adopters (see needs data), predominantly professionals (lawyers, consultants, etc.). Every cluster member has access to a PC, 89 percent already own PDAs. They read many magazines, especially BW 49%, PCMag 32%. Most are highly paid and highly educated.

42. Who to target… • Discuss.

43. Interpreting Cluster Analysis Results • Select the appropriate number of clusters: • Are the bases variables highly correlated? (Should we reduce the data through factor analysis before clustering?) • Are the clusters separated well from each other? • Should we combine or separate the clusters? • Can you come up with descriptive names for each cluster (eg, professionals, techno-savvy, etc.)? • Segment the market independently of your ability to reach the segments (i.e., separately evaluate segmentation and discriminant analysis results).

44. Discrimination based on demographics/behaviour proc discrim data=temp outstat=outdisc method=normal pool=yes list crossvalidate; class cluster; priors prop; vars age education etc… ; run; ** all relevant vars. not used to create segment solutions;

45. Discrimination based on demographics/behaviour This allows us a way to target and profile future customers:

46. Discrimination based on demographics/behaviour

47. Discrimination based on demographics/behaviour • The first discriminant function above explains 51% the variation. According to its coefficients, i.e., the four groups are particularly different with respect to the amount away from the office. • In addition, the function shares high correlation with the level of education, possession of a PDA, and income. • The second function explains 32% of the variance and primarily distinguishes the occupation types construction/emergency from sales/service, and the third function separates Sales and Service types.

48. Visualising relationships

49. Correspondence Analysis • Provides a graphical summary of the interactions in a table • Also known as a perceptual map • But so are many other charts • Can be very useful • E.g. to provide overview of cluster results • However the correct interpretation is less than intuitive, and this leads many researchers astray