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