Browsing by Author "Walters, Marisa"
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- ItemCustomer super-profiling demonstrator to enable efficient targeting in marketing campaigns(Southern African Institute for Industrial Engineering, 2017-11-22) Walters, Marisa; Bekker, JamesENGLISH ABSTRACT: Difficulties lie with identifying the right customers to engage in successful marketing campaigns. Thus far, segmentation has been a popular marketing method for selecting customer groups for targeted campaigns. However, each segment can be further exploited by performing customer profiling. In this paper, we explain the on-going development of a proposed simulator and demonstration tool that incorporates big data analytics to uncover hidden patterns within the customer dataset, thereby generating a customer super-profile. A developed toy problem and a large, realistic problem demonstrate segmentation, via clustering, and create customer profiles to enable marketers to identify appropriate marketing strategies. The proposed framework serves as the basis for enhancing customer relationship management by providing improved customer profiles for marketing campaigns.
- ItemDevelopment and demonstration of a customer super-profiling tool to enable efficient targeting in marketing campaigns(South African Institute for Industrial Engineering, 2018) Walters, Marisa; Bekker, JamesENGLISH ABSTRACT: Being part of a competitive generation demands having good marketing policies to attract new customers as well as to retain existing customers. This research outlines a general methodology for segmentation of customers by using the model of Recency, Frequency and Monetary (RFM) to identify types of customers, and then predict their customer profiles, based on demographic and behavioural features. A few previous studies dealt with the question using non-aggregate customer data. We, however, also address the problem by using decision trees, something which has rarely been done before. We applied and demonstrated this tool on a large customer dataset and found useful results.
- ItemDevelopment and demonstration of a Customer Super-Profiling tool utilising data analytics for alternative targeting in marketing campaigns(Stellenbosch : Stellenbosch University, 2018-12) Walters, Marisa; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.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 existing ones. 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 patterns often 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 data analytics 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 user with 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 large customer datasets from four different domains and useful results were found.