Development and demonstration of a Customer Super-Profiling tool utilising data analytics for alternative targeting in marketing campaigns

[摘要] ENGLISH ABSTRACT:Being part of a competitive generation demands that a business has good marketing policies to attract new customers as well as to retain existingones. Marketing managers can develop long-term and healthy relationships with customers, if they can detect and predict changes in their customers' purchasing behaviour. With the growth of information systems and technology,businesses have an increasing capability to accumulate huge quantities of customer data in large databases. However, much of these potentially useful marketing insights into customer characteristics and their purchasing patternsoften remains hidden and untapped. Therefore, businesses can achieve competitive advantages by studying customer behaviour through data mining tools (i.e. supervised and unsupervised learning) and techniques (i.e.classification, regression and clustering).The goal of this research project was to develop a Customer Super-Profiling (CSP) tool that has the ability to analyse large (non-aggregate) customer datasets, considering both demographic and behavioural features. The dataanalytics was done by utilising more than one data mining tool, which generates customer super-profiles. These profiles are used to attract and classify new customers as well as to retain existing customers, providing the userwith the ability to predict each customer's specific needs.This research project outlines a general methodology for segmentation of customers by using the model of Recency, Frequency and Monetary (RFM),together with k-means clustering (unsupervised learning) to identify the various types of customers within the dataset. Customer profiles are then generated, in the form of decision rules (supervised learning) to identify each type of customer as well as classifying them into the various clusters created.These predictions are performed based on the customers' demographic and behavioural features. The CSP tool was applied and demonstrated on largecustomer datasets from four different domains and useful results were found.

[发布机构] Stellenbosch University