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dc.contributor.authorOmonge, Jevans
dc.date.accessioned2022-06-24T09:38:55Z
dc.date.available2022-06-24T09:38:55Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/11071/12887
dc.descriptionA Thesis submitted to the Faculty of Information Technology in partial fulfillment of the requirements for the award of Master of Science in Information Technology, at Strathmore Universityen_US
dc.description.abstractMarket segmentation is a marketing strategy that has been widely used by many companies globally. With the ever increasing volume of client data, many companies are now unable to clearly cluster their clients into their respective segments, subsequently providing them with products and services that are best suited for them. Telkom Kenya is currently the third largest telecommunications company in Kenya. Currently, telecommunications companies do not have well defined marketing plans for their customers based on their daily expenditure. Some companies, for instance, may provide their customers with additional voice airtime even when such customers spend significant amounts of credit on data bundles rather than the actual voice airtime. One way of overcoming this challenge is by enhancing the current state of market segmentation in telecommunication companies in general. In this study, we present an approach that incorporates business intelligence, big data and machine learning in order to achieve customer segmentation. The study is based on data collected from the spending patterns of Telkom Kenya customers. When designing the customer segmentation model, the fundamental steps in the designing of any machine learning model were followed. To begin with, data was collected from the CRM department of the company. Key trends and inferences from the data were obtained from extensive data visualization that was performed on the data. The data was then formatted to ensure that it was consistent before performing feature engineering with the primary purpose of improving the quality of the features. Thereafter, the data was split into training and testing sets. Finally, the processed data was fed into the actual machine learning models. The main classification algorithms evaluated in this study are Logistic Regression Classifier, Linear Discriminant Analysis, K-Nearest Neighbor, Decision Tree Classifier and the Guassian Naive Bayes Classifier. Of the five, Logistic Regression Classifier was found to have the cross-validation accuracy and was thus embraced for the customer segmentation process. The results of this study therefore show yet another potential application of machine learning in marketing in general through customer segmentation. As seen from the results, the machine learning has been able to categorize customers into their respective categories with 71% accuracy. Through the classification, Telkom Kenya is in a position of marketing their products and services to the right group of customers, thereby ensuring that their marketing strategies are effective.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectCustomeren_US
dc.subjectSegmentation modelen_US
dc.subjectLogistic regressionen_US
dc.subjectTelkom_Kenyaen_US
dc.titleA Customer segmentation model using logistic regression: a case of Telkom Kenyaen_US
dc.typeThesisen_US


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