Personalization in Internet Marketing & e-Commerce

1. Internet Marketing & e-CommerceWard HansonKirthi KalyanamRequests for permission to copy any part of the material should be addressed to: PERMISSIONS DEPARTMENTTHOMSON BUSINESS and ECONOMICS5109 Natorp BoulevardMason, OH 45040Phone: (800) 423-0563

2. Part Two: Chapter NinePersonalization “The rediscovery of the customer is a byproduct – perhaps the most important one – of the onset of the information revolution.” Blattberg and Glazer, The Marketing Information Revolution

3. Benefits of Personalization • Product distinction: a standard product becomes a specialized solution

4. Benefits of Personalization • Product distinction: a standard product becomes a specialized solution • Low cost access to luxury and status of personalized services

5. Benefits of Personalization • Product distinction: a standard product becomes a specialized solution • Low cost access to luxury and status of personalized services • Hard to evaluate experience goods become predictable search goods

6. Benefits of Personalization • Product distinction: a standard product becomes a specialized solution • Low cost access to luxury and status of personalized services • Hard to evaluate experience goods become predictable search goods • Survival through differentiation

7. Benefits of Personalization • Product distinction: a standard product becomes a specialized solution • Low cost access to luxury and status of personalized services • Hard to evaluate experience goods become predictable search goods • Survival through differentiation • Increased customer loyalty

8. The Personalization Balance • Competitive advantage comes when personalization is supported by focus on customer-relating capabilities (CRC)

9. The Personalization Balance SOURCE: George Day and Christophe Van den Bulte, “Superiority in Customer Rlationship Management,” working paper, Wharton School, September 2002

10. The Personalization Balance • Personalization efforts can produce backlash: First Class envy • Excessive personalization wastes time • Sometimes, standardization is just fine

11. Personalization Approaches • Mass Customization: blending standard products with individual information

12. Personalization Approaches • Mass Customization: blending standard products with individual information • Choice Assistance: advice giving systems cut clutter of product choice

13. Personalization Approaches • Mass Customization: blending standard products with individual information • Choice Assistance: advice giving systems cut clutter of product choice • Personalized Messaging: consumer needs and choices tracked across repeated interaction

14. Many Opportunities for Personalization SOURCE: Adapted from Kai Riemer and Carsten Totz, “The Many Faces of Personalization,” in Tseng and Piller, The Customer Centric Enterprise, (New York/Berlin: Springer Verlag, 2003

15. Mass Customization • Customization of how a product is represented or of a product’s specific attributes through four approaches: • Collaborative • Cosmetic • Transparent • Adaptive

16. Mass Customization • Collaborative Customization: Online reconfiguration of a product to match consumer preferences • Example: Choosing features for a new car

17. Mass Customization • Cosmetic Customization: Packaging choices personalize standard products • Example: Choosing an iPod case SOURCE: Getty Images

18. Mass Customization • Transparent Customization: Product features learn the user and adjust accordingly • Example: Google Scholar

19. Mass Customization • Adaptive Customization: Product features naturally customize themselves or are individually configured • Example: Weather reports by zip code

20. Choice Assistance • Determining if a recommendation system could enhance online marketing: • Do customer lifetime values vary greatly? • Do customer needs vary greatly? • Are key product attributes qualitative or complex?

21. Choice Assistance A recommendation system flowchart

22. Choice Assistance: Approaches • Rules-based: Combines information about customers with understanding of how products are used

23. Choice Assistance: Approaches • Rules-based: Combines information about customers with understanding of how products are used • Computer-assisted self-explication (CASE): Advice generated from databases of consumer input

24. Choice Assistance: Approaches • Rules-based: Combines information about customers with understanding of how products are used • Computer-assisted self-explication (CASE): Advice generated from databases of consumer input • Collaborative Filtering: For complex product choices, linking like-minded individuals for recommendations

25. Choice Assistance: Approaches • Rules-based: Combines information about customers with understanding of how products are used • Computer-assisted self-explication (CASE): Advice generated from databases of consumer input • Collaborative Filtering: For complex product choices, linking like-minded individuals for recommendations • Endorsement: Virtual word-of-mouth referrals link users with preferred providers

26. Personalized Messaging • Users must be authenticated through a fixed, valid address • Online through Web servers, browser cookies or registration systems • Email and Instant Messaging accounts • Cell phones and wireless PDAs

27. Personalized Messaging Increasing levels of knowledge about Web user • Valid Name and Database • Valid Name • User ID • Cookies • Log Analysis • Online Behavior • Visitor Statistics • Anonymous

28. Personalized Messaging • With an established address, online behavior patterns suggest best approach for personalized messaging

29. Personalized Messaging • With an established address, online behavior patterns suggest best approach for personalized messaging • Distinct online events or inquiries can provide trigger moments for messaging • Example: A user searching for home value calculator could need real estate services