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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1674
Title: Applying boosting algorithms to predict consumer churn rate on an e commerce platform
Authors: Pham Cong An 
Keywords: Churn analysis;Boosting algorithms;E-commerce churn
Issue Date: 2023
Abstract: 
The research aimed to determine the most effective machine learning model for churn rate prediction. After comprehensive analysis and comparison of the models, the XG boost algorithm emerged as the best performing one, exhibiting superior accuracy and predictive capabilities. Additionally, this study sought to identify the key factors impacting customer churn. The results indicated that tenure and complaint were the most significant variables contributing to churn rate. Understanding these influential factors can aid e-commerce platforms in devising targeted strategies to mitigate churn and enhance customer retention. Based on the findings, this thesis proposes several managerial suggestions for e- commerce platforms to improve customer retention. First, implementing robust customer service processes is crucial to address complaints promptly and effectively. Offering personalized assistance and timely resolutions can foster positive customer experiences, consequently reducing churn. Second, strategies to enhance customer retention, such as loyalty programs, targeted discounts, and tailored marketing campaigns, should be devised to incentivize customer loyalty and encourage repeat purchases. Overall, this study highlights the potential of machine learning models in analyzing churn rate and offers actionable insights for e-commerce platforms to retain customers effectively and sustain long-term growth. By leveraging the power of XG boost and focusing on tenure and complaint management, e- commerce businesses can build stronger customer relationships and foster loyalty, ultimately leading to improved customer retention and increased profitability.
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1674
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Finance & Accounting (FA)
Finance & Accounting (FA)

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