INTELLIGENTPERSONALIZATION APPROACHESFORCOMPLEX CUSTOMER BEHAVIOUR: AN OVERVIEW

Abstract

Intelligent techniques have been used in the marketing and sales sectors of business toimprove analysis, increase revenues and save time. In customer-centric institutions, one of the areas inwhich intelligent techniques and data mining algorithms have been used is the personalization forenhanced CRM (customer relationship management) performance. However, with a growing number ofcustomers, the diversity of products on offer, the complex behavior of customer groups and thecontinuous change of personalization parameters, the production of a tailored personalizedrecommendation and the prediction of future needs are a challenging task. Within these institutions,personalization that is more true to the customer needs leads to better targeted marketing campaignsand enhances customer satisfaction with the ultimate aim of increasing the rates of customer retention,and improving competitive advantage. Intelligent techniques and data mining algorithms have beenused to produce a more accurately tailored action or service to individual customers or segments ofcustomers. However, many limitations still exist in the CRM personalization lifecycle that underminethe scope of personalized actions that follow; especially in evaluating of effectiveness of targeting,ensuring the coverage of a large segment customers and the control on the decision making process.