In addition to gaining customer information, marketing the company, developing relationship with its customers, the ultimate objective of loyalty cards is to retain customer loyalty to drive repeat purchases. Hence, through development of loyalty schemes, the marketers / retail companies etc. aim to ensure that shoppers make repeat purchases. One of the most common reasons behind consumers’ repeat purchases is brand loyalty. A research study conducted by Sullivan’s identifies three major drivers of customer loyalty, which are a) economic driver, b) dialogue driver and c) affinity driver. Application of these theoretical implications in the studied case of Tesco, economic driver refers to factors that restrict the purchase power of consumers as a result of which, Tesco extends credit to consumers, holding their share-of-spend and confining them to its brand. The retailer understands the needs of the consumers through processing the customer information and hence offering them credit rewards. The second driver: dialogue driver facilitates the continued streaming of dialogue (obtainment, processing / analysis and management of data) occurring through the loyalty cards and thus establishing the relationship between the retailer and consumer. The third driver; affinity driver expands when mostly up- market customers sign up for the loyalty card since they love the brand, intends to experience relationship with the brand, as well as receiving recognition and reward offered by the brand. The reason behind this driver is not the motivation of the credit offered by the loyalty card just as by Tesco, but an appreciation to the brand. Data mining is the process of searching through data, seeking formerly anonymous relationships among the data that are interesting to the user of the data . The most common examples of data mining are: a) process of scrutinizing and recording the contents of shopping basket at the checkouts as carried out by Tesco, b) reading and assessing past consumer behaviour so that customer ranking is generated based upon which credits are approved or disapproved, c) analyzing valuable customers through tracking their purchase habits, frequency of visits, length of loyalty etc., d) restoring, organizing and retrieving the personal information of tax payers as in the case of IRS and so forth. The benefits of data mining that have been extracted from two case studies are: a) extending automation benefits to legacy system along with identification of facts about customers, tax payers etc.
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