Personalization in e-Commerce

Dr. Alexandra Cristeaa.i.cristea@warwick.ac.uk

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?