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Internet Marketing & e-CommerceWard HansonKirthi Kalyanam

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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

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Benefits of Personalization

• Product distinction: a standard product becomes a specialized solution

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Benefits of Personalization

• Product distinction: a standard product becomes a specialized solution

• Low cost access to luxury and status of personalized services

© Copyright 2006, Thomson South-Western, a division of the Thomson Corporation

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

© Copyright 2006, Thomson South-Western, a division of the Thomson Corporation

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

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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

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The Personalization Balance

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

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The Personalization Balance

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

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The Personalization Balance

• Personalization efforts can produce backlash: First Class envy

• Excessive personalization wastes time

• Sometimes, standardization is just fine

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Personalization Approaches

• Mass Customization: blending standard products with individual information

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Personalization Approaches

• Mass Customization: blending standard products with individual information

• Choice Assistance: advice giving systems cut clutter of product choice

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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

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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

Many Opportunities for Personalization

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Mass Customization

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

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Mass Customization

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

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Mass Customization• Cosmetic Customization: Packaging

choices personalize standard products– Example: Choosing an iPod case

SOURCE: Getty Images

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Mass Customization• Transparent Customization: Product

features learn the user and adjust accordingly – Example: Google Scholar

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Mass Customization• Adaptive Customization: Product features

naturally customize themselves or are individually configured – Example: Weather reports by zip code

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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?

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Choice Assistance

A recommendation system flowchart

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Choice Assistance: Approaches

• Rules-based: Combines information about customers with understanding of how products are used

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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

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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

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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

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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

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Personalized Messaging

Increasing levels of knowledge about Web user

• Cookies

• Log Analysis

• Online Behavior

• Visitor Statistics

• Anonymous

• Valid Name and Database

• Valid Name

• User ID

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Personalized Messaging

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

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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