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Personalization in e-Commerce

Personalization in e-Commerce. Dr. Alexandra Cristea a.i.cristea@warwick.ac.uk http://www.dcs.warwick.ac.uk/~acristea/. 1. Contents. Introduction Benefits Perspectives Ubiquitous Computing. Introduction. E-commerce:

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Personalization in e-Commerce

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  1. Personalization in e-Commerce Dr. Alexandra Cristea a.i.cristea@warwick.ac.uk http://www.dcs.warwick.ac.uk/~acristea/

  2. 1. Contents • Introduction • Benefits • Perspectives • Ubiquitous Computing

  3. Introduction • E-commerce: • The conducting of business communication and transactions over networks and through computers. • the buying and selling of goods and services, and the transfer of funds, through digital communications • Others: all inter-company and intra-company functions (such as marketing, finance, manufacturing, selling, and negotiation) • B2B: business interactions between enterprises • B2C: interactions between enterprise and customers

  4. Benefits • First: “Hello Johnny!” syndrome • Cost as issue • 2005 onwards: Customer-Centric services for CRM (customer-relationship-management), • which can flexibly react to dynamically changing market requirements • Customer Data Integration (CDI) services

  5. Amazon

  6. Amazon

  7. Perspectives: Use of adaptation • Often simple business rules, allowing e.g., administrators to offer discounts on the basis of products selected by customers

  8. Perspectives: Personalized Features • (e.g., BroadView: www.broadvision.com) • Push: system is pro-active • Pull: system relies on the user who requests information • Also: • qualifier matching, • simple rule-based matching : business rules • E.g., generation of electronic coupons (based on previous purchases) that are sent by e-mail to each customer who has not purchased goods for a while

  9. Perspectives:Personalized Product Recommendations • Generalized • Interactive, dynamic taxonomies • Customer behaviour (customers who bought) • Item similarity (or correlation) • Personalized • Content-based (e.g. content-based filtering: past and present of user) versus social recommendations(collaborative filtering) – pros & cons; • hybrid recommender systems • Item-to-item collaborative filtering(similarity to content based; item similarity, but lightweight, without user – for stable products)

  10. Perspectives: Customer info sharing • As a solution to latency (cold start): central UM • Issues?

  11. Perspectives: Personalized Product Info • … leading to a sale • E.g., evaluation-oriented (as a car-sales person)

  12. Case Study: SeTA • sorting items on a suitability basis, to the preferences of their beneficiary. • Individual UM (direct: questionnaires + monitoring) & indirect (stereotype) • demographic data (e.g., age, job), & preferences for products (e.g., products). • Prologue and summary tailored to user • User + vendor interests represented • Comparison table is allowed

  13. Beginner (non expert)

  14. Advanced (expert user)

  15. Conclusions Case Study SeTA • Positive: advanced UM, dynamic content generation techniques, personalized recommendation: generation of electronic catalogs meeting individual user needs with high accuracy. • Negative: knowledge intensive approach supporting the system adaptation which may discourage web designer.

  16. Perspectives: CRM • customer-centered instead of product-centered • share of customer, replacing traditional share of market. • accurate UM can then support the proposal of personalized offers to improve the customer’s loyalty and thus the company’s profit, in the medium-long term • mass customization • Cross-selling, up-selling

  17. Perspectives: Mass Customization • Custom-design (for real!) • Issues: costly (for firm) ; difficult (for customer) • Adaptation can help with the latter via intelligent interaction with the buyer

  18. Context-aware and Ubiquitous Computing in e-Commerce • accessing a service anytime, anywhere and via different types of (mobile) devices. • M-Commerce: commercial transactions performed by using wireless devices • E.g., digital wallets, push information services, and location-based services (e.g., visiting a museum, or attending a concert, or driving on a motorway) • Issues: power, bandwidth, efficiency, screen size limitations

  19. Ubiquitous m-Commerce Perspectives • generation of product and service presentations whose length is tailored to the screen size. • layout of the user interface to the characteristics of the device used to access the service. (via HTML or XML processing, e.g.)

  20. Conclusions & Discussion • Here: B2C • Potential personalization also in B2B • Quality of Service (QoS) levels • (web) Service discovery, composition, execution • Web Services description languages, e.g. WSDL enable the specification of service public interfaces. • Web Service orchestration languages, e.g., WS-BPEL, support the definition of composite services based on the orchestration of multiple providers within possibly complex workflows • Semantic Web techniques have been used to add personalization to Web Services

  21. Conclusions • Personalization in e-Business: yes, if: • Supporting CRM (cust-rel-mng) • Enhancing usability • Enhancing interoperability

  22. Any questions?

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