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Personalization in E-commerce Applications

Personalization in E-commerce Applications. Presented by Ingrid Liao. Topics. E-commerce (EC) Adaptation Frameworks for EC website development Trends in e-commerce applications Reminder. E-commerce (EC). E-commerce (EC): Introduction.

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Personalization in E-commerce Applications

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  1. Personalization in E-commerce Applications Presented by Ingrid Liao Personalization in E-commerce Applications

  2. Topics • E-commerce (EC) • Adaptation • Frameworks for EC website development • Trends in e-commerce applications • Reminder Personalization in E-commerce Applications

  3. E-commerce (EC) Personalization in E-commerce Applications

  4. E-commerce (EC): Introduction • Definition: the conducting of business communication and transactions over networks and through computers • Buying and selling of goods and services • All aspects of business interaction, two levels: • Business to Business e-commerce (B2B) • Business to Consumer e-commerce (B2C) • ( Source: Glossary of IT & Internet Terms) Personalization in E-commerce Applications

  5. E-commerce (EC): Advantages • Geographical and time zone distance are no longer important • Presentation of products and services in a web-based catalog is an effective way to publish information at low costs Personalization in E-commerce Applications

  6. Lack of face to face dialog Good EC product candidates: software, music, book, high-tech products Good EC service candidates: information, booking, shipping services Problematic candidates: dress, insurance One size fits all catalog Personalization Allowing individuals to customize website appearance and functionality E-commerce (EC): Problems & Solutions Personalization in E-commerce Applications

  7. Adaptation Personalization in E-commerce Applications

  8. Adaptable Adaptation decided by user Lower-level feature Adaptive Adaptation performed by system in an automated way Adaptable versus Adaptive Personalization in E-commerce Applications

  9. Factors for Adaptivity • User • Device • Context of use Personalization in E-commerce Applications

  10. User characteristics Knowledge & skills Interests & preferences Needs about disability Goals B2C e-commerce Complex products/services Category or properties Accessible services Application domain User Characteristics Personalization in E-commerce Applications

  11. Type of Devices • Environment data • PC, laptop, mobile phone, PDA, on-board device, … • Different characters • Screen size • Computation and memory capabilities • I/O mechanism • Connection speed, bandwidth • … Personalization in E-commerce Applications

  12. Context of Use • Broad • Physical context • User location (most popular context feature) • Environment conditions • Social Context • Social community or group • Task being performed Personalization in E-commerce Applications

  13. What is Adapted? • Suggestion of product/service (content recommendation) • Recommender • Tailored to user/device/context characteristics • Configuration guide • Presentation of product/service • Media, presentation styles • User interface (structure) • Layout e.g. information & navigation structure Personalization in E-commerce Applications

  14. Accessibility 3D, virtual reality UI Usability Guidelines e.g. Serco Users w/ special needs Emotional buying style Being usable is the 1st step for being successful More HCI, Less Adaptation Personalization in E-commerce Applications

  15. Frameworks for EC website development Personalization in E-commerce Applications

  16. Merchant Systems • Facilitate creation and management of electronic catalogs • Support transactional, secure services and integration with legacy software • Only basic personalization features, e.g. product recommendation • Personalization strategies, e.g. BroadVision • Push: recommend information and access • Pull: handle user request in a personalized way • Quantifier matching Personalization in E-commerce Applications

  17. Personalized Product Recommendation • Enhance recommendation capabilities • Interactive: user search according to own selection criteria, e.g. dynamic taxonomies • Inference: based on user behavior • Recommendation techniques • Collaborative filtering: analyzing similarities in different people’s purchase history, e.g. Amazon • Content-based filtering: analyzing product properties similar to individual’s past purchase • Taking indirect users into account Personalization in E-commerce Applications

  18. Collaborative Pros Items as elementary entities Cons “Bootstrapping” problems: minimum number of ranking Sparse user-rank matrix Content-based Pros Successfully recommend new items Cons Information must be available User behavior monitor Similar items Collaborative versus Content-based filtering Personalization in E-commerce Applications

  19. How to Enhance Customer’s Trust in Recommender • Transparency and explanation • Right amount of information • Negotiation between customer and system • Explanation of recommendation Personalization in E-commerce Applications

  20. Customer Information Sharing • Increase knowledge about common customers • Points for attention • Respect customer’s privacy preferences • Mutual trust between service providers • Misuse • Competitors Personalization in E-commerce Applications

  21. Personalized Product Info Presentation • Individual customer’s interests & preferences • Dynamically generated product descriptions in electronic catalogs • How? • Individual user model • Different levels of detail • Information on demand • Customized compare table • Example: SeTA system Personalization in E-commerce Applications

  22. Personalized Product Presentation Example Personalization in E-commerce Applications

  23. Personalized Product Presentation Example Personalization in E-commerce Applications

  24. Personalized Product Presentation Example • Customized compare table • Enable user to check product similarities and differences important to him/her • Unobtrusively identify user priorities Personalization in E-commerce Applications

  25. Customer Relationship Management (CRM) • One-to-one interaction • Ultimate goal: profit increase • Individual and personalized interaction • Customer satisfaction • Long-term relationship with customers • Increase customer loyalty • Accurate user model • Supplement the lack of direct and personal contact with a human being Personalization in E-commerce Applications

  26. Mass Customization • Production of product/services tailored to specific customer needs, maintaining mass production efficiency and costs • Past: off-the-shelf goods • Good • Enhance relationship between customer & vendor • Limitation • Costly and require expertise knowledge in configuration from scratch Personalization in E-commerce Applications

  27. Mass Customization Example: Footwear • http://www.adidas.com/products/miadidas04/content/uk/container.asp Personalization in E-commerce Applications

  28. Trends in e-commerce applications Personalization in E-commerce Applications

  29. Ubiquitous Computing • Possibility of accessing a serve anytime, anywhere and exploiting different types of (mobile) devices • Adaptation in particular to context of use and device specific requirements • Context-aware Applications • Example: mobile guides • Ability to integrate different adaptation strategies Personalization in E-commerce Applications

  30. M-commerce • Commercial transactions performed by exploiting wireless devices • Support e-commerce transactions by providing information access and promotion • Information about user’s local context • Timely, relevant, focused services • Physical context • Type of activity Personalization in E-commerce Applications

  31. M-commerce Services and Applications (Source: Grami and Schell) Personalization in E-commerce Applications

  32. Low Acceptance of Mobile Devices • Technical limitation of mobile devices • High cost yet poor quality services • Lack of standards and protocols • Individual’s attitudes • User’s goal • … Personalization in E-commerce Applications

  33. Design Elements of M-commerce Interface Personalization in E-commerce Applications (Source: Lee and Benbasat)

  34. M-commerce: Adaptation • Adapting product/service presentation to screen size • Adapting layout of user interface to characteristics of device Personalization in E-commerce Applications

  35. Reminder Personalization in E-commerce Applications

  36. Personalization • Not a goal, but • Add values to • CRM by supporting long-term relationship • Quality of the offer if tailored to customer needs • Usability if make navigation easier • Back-office integration Personalization in E-commerce Applications

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