A data mining approach for turning potential customers into real ones in basket purchase analysis

Abstract

Today, in this highly competitive, quality-based world attracting customers is very important. In line with "customer is always right" slogan, customer relationship management is at the core of an organisation's strategy. It plays an important role in four aspects, including: customer identification, customer attraction, customer retention, and customer satisfaction. By customer's life cycle's analysis, commercial organisations have obtained increased customer value. Data mining and data warehousing tools and other customer relation management methods have provided new opportunities for businesses. This paper discusses the practical use of data mining in identification of potential customers and potential customers' criteria in the competitive business environment. It also presents the mechanism for identification of potential customers who have the potential to become real customers. After analysing customers' basket purchase, based on the identified parameters, a pattern emerges. By using the decision tree tool, we identify the main criteria and its several sub-criteria and determine their importance level. We determine their importance in order to analyse customers' basket purchase and turn potential customers into real ones. Based on the proposed model, we suggest that organisations increase their investment on the customers that have potential to turn into a loyal customer.