A Novel Model for Partial and Total Churn Prediction in E-Commerce

A Novel Model for Partial and Total Churn Prediction in E-Commerce | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Novel Model for Partial and Total Churn Prediction in E-Commerce Hossam H. Ahmed, Mohamed H. Khafagy, Mostafa R. Kaseb This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3972583/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract The e-commerce market is a rapidly growing industry, with many companies entering the market to provide customers with easy access to a variety of products and services. However, with the increasing number of e-commerce sites, customers are now able to move their purchases from one site to another or split their purchases among multiple sites. This trend creates a challenge for companies, as acquiring new customers is more costly than retaining existing ones. The proposed model is used to predict customer churn in the e-commerce market. Customer churn refers to customers who stop using a particular product or service. The model uses a dataset from a B2C multi-category e-commerce application that describes customer behavior and interactions. The model defines and predicts the types of customer churn, which can be either total (when a customer stops using the e-commerce site altogether) or partial (when a customer reduces their purchases or becomes less profitable), The dynamic churn definition step enables the model to detect the two types of churn. The model uses the Length, Regency, Frequency, and Monitory (LRFM) model combined with the k-means algorithm to define churn status in the first phase. In the second phase of the study, the model uses XGBoost on behavioral and interaction data to predict customer churn status. The results of this study showed that the proposed model achieves an accuracy rate of 98% for the algorithm that detects both partial and total churn, while the accuracy for the partial churn algorithm is 98% and the accuracy for the total churn algorithm is 99%. E-commerce Churn Prediction Customer Churn LRFM K-means Behavior Analysis XGBoost Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 20 Feb, 2024 Submission checks completed at journal 20 Feb, 2024 First submitted to journal 20 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3972583","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273938791,"identity":"c5734701-3558-43bd-a098-9074648317fe","order_by":0,"name":"Hossam H. 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However, with the increasing number of e-commerce sites, customers are now able to move their purchases from one site to another or split their purchases among multiple sites. This trend creates a challenge for companies, as acquiring new customers is more costly than retaining existing ones. The proposed model is used to predict customer churn in the e-commerce market. Customer churn refers to customers who stop using a particular product or service. The model uses a dataset from a B2C multi-category e-commerce application that describes customer behavior and interactions. The model defines and predicts the types of customer churn, which can be either total (when a customer stops using the e-commerce site altogether) or partial (when a customer reduces their purchases or becomes less profitable), The dynamic churn definition step enables the model to detect the two types of churn. 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