Customer churn prediction tool using deep learning: a case of an ecommerce business operating in Kenya
Date
2023
Authors
Narina, P.
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Journal ISSN
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Publisher
Strathmore University
Abstract
The problem of customer churn poses significant challenges for businesses, particularly in the e-commerce sector. With a high level of competition and low customer retention rates, businesses face the risk of losing customers and experiencing a decline in revenue. Previous research in customer churn prediction exhibited limitations in terms of accuracy and the lack of consumer-facing tools for predictions. Moreover, existing studies were often conducted before the COVID-19 era, failing to capture the impact of recent changes in consumer behavior and market dynamics. This research aimed to develop an effective customer churn prediction model for e-commerce businesses in Kenya. The study aimed to address the limitations of existing research by leveraging advanced machine learning techniques and considering the specific challenges faced by businesses in the post-COVID-19 era. To achieve the objective, an agile software development methodology was adopted. This approach allowed for continuous iterations and refinements during the model development process. Customer dataset was obtained from Kaggle an online platform for sharing datasets. The dataset included customer demographic information, transaction history, and customer engagement metrics. The data was carefully pre-processed to handle missing values, outliers, and ensure data quality. The multilayer perceptron model (MLP), a powerful deep learning algorithm, was employed to train the customer churn prediction model. The dataset was split into training and testing sets, with an 80-20 ratio, to assess the model's performance. The results of the study indicated that the developed customer churn prediction model achieved high accuracy, with precision, recall, and F1 scores of 94%. This demonstrated the model's effectiveness in identifying potential churners and enabling businesses to take proactive measures for customer retention. The findings of this study have significant implications for e-commerce businesses, providing them with a valuable tool to predict customer churn and implement targeted retention strategies. By leveraging the power of advanced machine learning techniques, businesses could enhance customer satisfaction, optimize resource allocation, and drive sustainable growth in a highly competitive market.
Keywords: Customer Churn, Deep Learning, Multi-Layer perceptron, E-commerce, Big Data.
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Full- text thesis
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Citation
Narina, P. (2023). Customer churn prediction tool using deep learning: A case of an ecommerce business operating in Kenya [Strathmore University]. http://hdl.handle.net/11071/13533