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dc.identifier.citationSant Fournier, N. (2018). Evaluating deep learning and machine learning techniques to predict customer churn within a local retail industry (Master's dissertation).en_GB
dc.description.abstractA top priority in any business is a constant need to increase revenue and profitability. Within the retail industry, the main source of revenue is based on the purchases of customers. For this reason, companies need to focus on customer retention. When a customer leaves or churns from a business, the opportunity for potential sales or cross selling is lost. When a customer leaves the business without any form of explanation or notice, the company may find it hard to respond and take corrective action. Ideally companies should be proactive and identify potential churners prior to them leaving. Customer retention has been noted to be less costly than attracting new customers. Therefore, identifying individuals that are likely to churn is of great benefit to the company. Through data available within the Point of Sales (POS), customer transactions may be extracted and buying patterns may be identified. This project demonstrates how through transactional data, features are created and may be defined as significant in predicting churn. By predicting churn, companies may adopt a proactive approach to retaining customers. The data provided within this project pertains to a local supermarket. Therefore the results attained through the various models are based on true data. The novelty of this dissertation is the concept of implementing and comparing Deep Learning algorithms to Machine Learning techniques. Convolution Neural Networks, Deep Neural Networks and Restricted Boltzmann Machine are the selected Deep Learning techniques, whilst Random Forest and Logistic Regression are implemented as Machine Learning algorithms. Furthermore, various datasets are designed to evaluate how the mentioned algorithms perform based on the features designed. The overall accuracy results obtained for the mentioned algorithms are: Random Forest attained an 94%, Restricted Boltzmann Machine obtained 83%, Logistic Regression acquired 77% and Convolution Neural Network attained 74%. The results are satisfactory and may contribute in assisting the supermarket in retaining customers.en_GB
dc.subjectCustomer relationsen_GB
dc.subjectConsumer satisfactionen_GB
dc.subjectMachine learningen_GB
dc.subjectRetail tradeen_GB
dc.titleEvaluating deep learning and machine learning techniques to predict customer churn within a local retail industryen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Information and Communication Technology. Department of Artificial Intelligenceen_GB
dc.contributor.creatorSant Fournier, Nicole-
Appears in Collections:Dissertations - FacICT - 2018
Dissertations - FacICTAI - 2018

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