A clustering approach for knowledge discovery in database marketing

Abstract

Due to the advances in information and communication technologies, corporations can effectively obtain and store transactional and demographic data on individual customers at reasonable costs [1]. The challenge now is how to extract important knowledge from these vast databases in order to gain a competitive advantage [2]. Firms are increasingly realizing the importance of understanding and leveraging customer level data, and critical business decision models are being built upon analyzing such data. Emphasis on customer relationship management makes the marketing function an ideal application area to greatly benefit from the use of Data Mining (DM) tools for decision support. Through DM, organizations can identify valuable customers, predict future behaviors, and make proactive, knowledge-driven decisions. This includes understanding the customers’ preferences through facts and customers’ behavior through analyzing their transaction data. There has been much research done in this direction, and clustering transactions to learn segments has been one research stream that has generated a variety of useful approaches [3][4]. DM techniques are used in several areas, such as fraud detection [5], bankruptcy prediction [6], intensive care medicine [7], civil engineering [8], just to name a few. Their use for marketing decision support highlights unique and interesting issues such as customer relationship management, real-time interactive marketing, customer profiling and cross-organizational management of knowledge [9]. The Database Marketing (DBM) activity has changed significantly over the last several years. In the past, database marketers applied business rules to target customers directly. Examples include targeting customers solely on their product gap on on marketer’s intuition. The current approach, which has widespread use, relies on predictive response models to target customers for offers. These models accurately estimate the probability that a customer will respond to a specific offer and can significantly increase the response rate to a product offering. The old model of “design-build-sell” (a product-oriented view), is being replaced by “sell-build-redesign” (a customer-oriented view). The traditional process of mass marketing is being challenged by the new approach of one-to-one marketing. DBM departments face several types of business constraints. Typically there are: - restrictions on the minimum and maximum number of product offers that can be made in a campaign; - requirements on minimum expected profit from product offers; - limits on channel capacity; - limits on funding available for the campaign; - customer specific ‘do not solicit’ and credit risk limiting rules; and - campaign return-on-investment hurdle rates that must be met. Recently, some DM methodologies and applications have been developed to explore the practices and planning methods of sales and marketing management between customers and sellers in the market [10]. In this paper, the DBM process involved a development of models to correctly classify which clients use (or not) a voucher, using five answers as inputs, predicting the customer response, enabling the commercial organization to offer products suitable to the right customers. First, a description of the adopted data is given. Then, a brief presentation of KDD is performed. Next, experiments are presented and the results analysed. Finally closing conclusions are drawn.