personalization in e-commerce dr. alexandra cristea [email protected] acristea
Post on 22-Dec-2015
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TRANSCRIPT
Personalization in e-Commerce
Dr. Alexandra [email protected]
http://www.dcs.warwick.ac.uk/~acristea/
• Introduction
• Benefits
• Perspectives
• Ubiquitous Computing
1. Contents
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
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
Amazon
Amazon
Perspectives: Use of adaptation
• Often simple business rules, allowing e.g., administrators to offer discounts on the basis of products selected by customers
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
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)
Perspectives: Customer info sharing
• As a solution to latency (cold start): central UM
• Issues?
Perspectives: Personalized Product Info
• … leading to a sale– E.g., evaluation-oriented (as a car-sales
person)
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
Beginner (non expert)
Advanced (expert user)
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.
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
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
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
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.)
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
Conclusions
• Personalization in e-Business: yes, if:– Supporting CRM (cust-rel-mng)– Enhancing usability– Enhancing interoperability
Any questions?