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INTRODUCTION
For the past 10 years, we have witnessed a steady and strong increase of online retail sales. According to the Interactive Media in Retail Group (IMRG), online shoppers in the United Kingdom spent an estimated £50 billion in year 2011, a more than 5000 per cent increase compared with year 2000. 1 This remarkable increase of online sales indicates that the way consumers shop for and use financial services has fundamentally changed.
Compared with traditional shopping in retail stores, online shopping has some unique characteristics: each customer's shopping process and activities can be tracked instantaneously and accurately, each customer's order is usually associated with a delivery address and a billing address, and each customer has an online store account with essential contact and payment information. These desirable, special online shopping characteristics have enabled online retailers to treat each customer as an individual with personalized understanding of each customer and to build upon customer-centric business intelligence.
In relation to customer-centric business intelligence, online retailers are usually concerned with the following common business concerns:
Which items/products' web pages has a customer visited? How long has a customer stayed with each web page, and in which sequence has a customer visited a set of products' web pages?
Who are the most/least valuable customers to the business? What are the distinct characteristics of them?
Who are the most/least loyal customers, and how are they characterized?
What are customers' purchase behaviour patterns? Which products/items have customers purchased together often? In which sequence the products have been purchased?
Which types of customers are more likely to respond to a certain promotion mailing? and
What are the sales patterns in terms of various perspectives such as products/items, regions and time (weekly, monthly, quarterly, yearly and seasonally), and so on?
In order to address these business concerns, data mining techniques have been widely adopted across the online retail sector, coupled with a set of well-known business metrics about customers' profitability and values, for instance, the recency, frequency and monetary (RFM) model, 2 and the customer life value model.3 For many online retailers in the United Kingdom and internationally alike, especially the leading companies including Amazon, Walmart, Tesco, Sainsbury's, Argos, Marks and Spencer, John Lewis, and EasyJet, data mining has now...