[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118394-en":3,"doc-seo-118394-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118394,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Customer data prediction and analysis in e-commerce using machine learning","Customer churn is a major challenge for e-commerce companies, causing revenue loss and reduced loyalty. This research investigates machine learning approaches to predict customer churn, annual spending, and product on-time delivery within e-commerce. After a comprehensive literature review, the study performs empirical analysis using machine learning algorithms, including data pre-processing, prediction modeling, and performance evaluation. Results show machine learning is effective for churn and on-time delivery prediction, with SVM delivering the strongest performance, reaching 83.45% accuracy for churn and 68.42% for delivery.","Customer data prediction and analysis in e-commerce using  \nmachine learning  \nMd Abdullah Al Rahib, Nirjhor Saha, Raju Mia, Abdus Sattar  \nDepartment of Computer Science and Engineering, Faculty of Science and Information Technology, Daffodil International University,  \nDhaka, Bangladesh  \nArticle history:  \nReceived Apr 7, 2023 Revised Oct 9, 2023 Accepted Feb 12, 2024  \nKeywords:  \nCustomer annual spending  \nCustomer churn Data analysis E-commerce Machine learning  \nPerformance metrics Product on-time delivery  \nCorresponding Author:  \nCustomer churn is a major challenge faced by e-commerce companies, as it leads to loss of revenue and decreased customer loyalty. In recent years, for predicting and reducing client churn machine learning techniques are powerful tools. This research aims to explore the use of machine learning algorithms for predicting customer churn, annual spending, and product ontime delivery in e-commerce. The study first conducted a comprehensive review of the literature on customer churn in machine learning. The literature showed that customer churn has been predicted successfully using a variety of machine learning algorithms, including support vector machine (SVM), random forest, and decision tree in various industries. To address this gap in the literature, the study conducted an empirical analysis of customer churn in e-commerce using machine learning algorithms. The data were then pre-processed and analyzed utilizing machine learning techniques for prediction. According to the study ’s findings, machine learning algorithms are effective in predicting customer churn, and product on-time delivery in e-commerce. The best-performing algorithm SVM achieved an accuracy of 83.45% in predicting customer churn and 68.42% for product on-time delivery prediction.  \nThis is an open access article under the CC BY-SA license.  \nMd Abdullah Al Rahib  \nDepartment of Computer Science and Engineering, Faculty of Science and Information Technology Daffodil International University  \nDhaka, Bangladesh  \nEmail: [abdullah15-12247@diu.edu.bd](abdullah15-12247@diu.edu.bd)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nNow e-commerce industry has seen tremendous growth in recent years, with millions of consumers flocking to the internet to make purchases. However, retaining customers and preventing them from“churning” or switching to competitors is a major challenge for e-commerce businesses. Acquiring new customer is frequently more costly than keeping current ones, customer churn can significantly affect a company ’s income. Several research papers have been instrumental in advancing our understanding of customer churn prediction in e-commerce.  \nFor instance, Matuszelański and Kopczewska [1] uses extreme gradient boosting and logistic regression models to predict churn by leveraging socio-geo-demographic data from census records. Xiahou and Harada [2] employs k-means clustering, logistic regression, and support vector machines (SVM) to address churn prediction, emphasizing the significance of data balancing and ensemble learning techniques, Pondel et al. [3] delves into the realm of big data, employing decision support and neural networks to tackle churn prediction for one-off customers. Also, several studies used datasets from Alibaba Cloud Tianchi platform [4], IBM Telco Customer Churn dataset, whereas others used data from e-commerce marketplaces  \nsuch as Kaggle. One study focused on decision support for predicting customer churn in the big data domain, while others focused on using ensemble learning techniques to deal with non-contractual customer data [5], and high-dimensional, unbalanced data. Additionally, a study was dedicated to predictive models using k-nearest neighbor (KNN), decision tree [6], [7], Naive Bayes, and logistic regression for predicting customer churn one-commerce mobile customers. Also, some studies implemented deep learning techniques such as multilayer perceptron (MPL), recurrent layer recurrent","cbCaidQLNFf6d6dU","https://ap.wps.com/l/cbCaidQLNFf6d6dU","pdf",840192,1,10,"English","en",105,"# Abstract\n# Introduction\n## Customer churn challenge in e-commerce\n## Related work and prior prediction approaches\n# Methodology and analysis\n# Results and performance metrics\n# Conclusion","[{\"question\":\"What customer problems does the study aim to predict in e-commerce?\",\"answer\":\"The study focuses on predicting customer churn, customer annual spending, and product on-time delivery to support better customer retention and operations.\"},{\"question\":\"How does the research develop its approach?\",\"answer\":\"It begins with a comprehensive literature review, then performs empirical analysis using machine learning algorithms, including data pre-processing and prediction modeling.\"},{\"question\":\"Which algorithm performs best and what are the reported results?\",\"answer\":\"Support vector machine (SVM) achieves the best performance, with 83.45% accuracy for predicting customer churn and 68.42% accuracy for predicting on-time delivery.\"}]","Customer data prediction and analysis in e-commerce using machine learning | 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customer problems does the study aim to predict in e-commerce?","Question",{"text":76,"@type":77},"The study focuses on predicting customer churn, customer annual spending, and product on-time delivery to support better customer retention and operations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the research develop its approach?",{"text":81,"@type":77},"It begins with a comprehensive literature review, then performs empirical analysis using machine learning algorithms, including data pre-processing and prediction modeling.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithm performs best and what are the reported results?",{"text":85,"@type":77},"Support vector machine (SVM) achieves the best performance, with 83.45% accuracy for predicting customer churn and 68.42% accuracy for predicting on-time 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