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Part Two: Chapter Nine Personalization “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

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

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

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

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

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

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

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

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

Personalization Approaches Mass Customization: blending standard products with individual information

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

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

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

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

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

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

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

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

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?

Choice Assistance A recommendation system flowchart

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

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

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

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

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

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

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

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