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SEGMENTATION

SEGMENTATION. INTERNET MARKETING 15.823 PROF. GLEN L. URBAN SPRING 2001. OUTLINE. REVIEW OF SEGMENTATION BASES METHODS LEVELS OF SEGMENTATION GLOBAL LOCAL ONE TO ONE --PERSONALIZATION CONSUMER RELATIONSHIP MANAGEMENT. WHY SEGMENT MARKETS. WHY SEGMENT. Preference Heterogeneity

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SEGMENTATION

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  1. SEGMENTATION INTERNET MARKETING 15.823 PROF. GLEN L. URBAN SPRING 2001

  2. OUTLINE • REVIEW OF SEGMENTATION • BASES • METHODS • LEVELS OF SEGMENTATION • GLOBAL • LOCAL • ONE TO ONE --PERSONALIZATION • CONSUMER RELATIONSHIP MANAGEMENT

  3. WHY SEGMENT MARKETS

  4. WHY SEGMENT • Preference Heterogeneity • Balance Versus Costs • Variety versus Production • INTERNET -- Some Costs Down • communicate individually • Interface customization • Some Costs not Changed • Physical Production/Inventory • Software -- Service Design

  5. DECISION • Segment or Not? Low Cost or Custom • Basis of Segmentation -- Many Choices • Level of Segmentation -- Big Enough • Common versus Segment Attributes -- Product/Service or Communication

  6. On Line Trading • eTrade? • MLPFS • Bridge Trader

  7. TRADITIONAL BASES OF SEGMENTATION • Demographics • Attitudes -- Psychographics • Preferences • products - like similar things • attribute importances -- benefit segmentation • Uses -- Intensity (Heavy/Light)

  8. TOP FRONTVIEW LEFT SIDE RIGHT SIDE

  9. EXAMPLES • PRIZM • VALS

  10. PRIZM USA census 1000 measures -- 34 factors -- 5 domains education and affluence family life cycle mobility ethnicity housing stock & urban cluster ZIP areas 12 and 40 cluster link to other zip data consumption

  11. PRIZMTHE 1980 ZIP CLUSTER MODELTWELVE ZIP-CLUSTER GROUPS, IN DESCENDING ZQ RANK Group CodesGroup Titles S1 Educated, Affluent Executives & Professionals in Elite Metro Suburbs S2 Pre & Post-Child Families & Singles In Upscale, White-Collar Suburbs S3 Upper-Middle, Child-Raising Families In Outlying, Owner-Occupied Suburbs U1 Educated, White-Collar Singles & Ethnics in Upscale, Urban Areas T1 Educated, Young, Mobile Families In Exurban Satellites & Boom Towns S4 Middle-Class, Post-Child Families In Aging Suburbs & Retirement Areas T2 Mid-Class, Child-Raising, Blue-Collar Families In Remote Suburbs & Towns U2 Mid-Class Immigrants & Minorities In Dense, Urban Row & Hi-Rise Areas R1 Rural Towns & Villages Amidst Farms & Ranches Across Agrarian Mid-America T3 Mixed Gentry & Blue-Collar Labor In Lo-Mid Rustic, Mill & Factory Towns R2 Mixed Whites, Blacks, Spanish & Indians In Poor Rural Towns & Farms U3 Mixed Blacks, Spanish & Immigrants In Aging, Urban Row & Hi-Rise Areas

  12. 43 LIFE STYLE QUESTIONS • My idea of fun at a national park would be to stay at an expensive lodge and dress up for dinner • I could stand to skin a dead animal • 1 to 7 agree --disagree scales • cluster • describe average person in cluster

  13. VALS2 GROUPS • Actualizers • Fulfilleds • Believers • Achievers • Strivers • Experiencers • Makers • Strugglers

  14. Levels of segmentation • Global • Country • Local • Individual

  15. EXAMPLES • Surgua -- SE Bank • Wine.com -- California Wines • Lycos/Terra Case • Yahoo.Boston -- Local Audience • Personalization

  16. LOCAL AUDIENCE • Like Minded Individuals • Affinity • Targeted commerce • Segments • local residents • college student • new comers • Tourists • Business Travelers • Displaced/relocated natives

  17. Personalization • Screen Layout • Targeted Marketing • Permissive Marketing • ads • brochures/info • Recommendations • Advisors • Intelligent Agents • Mass Customization

  18. CUSTOMER RELATIONSHIP MANAGEMENT • Organize Data • Profiling -- algorithms • Rules Engine • Delivery -- Communication/promotion • Measurement • Learning

  19. TAKEAWAY • Segmentation Art • Basis • Level • Segmentation Science • Profiling • Rules Engines • CRM -- Case Prime Response

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