[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122319-en":3,"doc-seo-122319-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},122319,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Modeling for Forecasting Repeat Purchases in Online Shopping","Online shopping platforms invest in marketing to increase customer engagement, yet many newly acquired users fail to repurchase, harming long-term revenue and ROI. The paper mines and explores an online-shopping dataset, applies feature engineering, and builds machine-learning models using LightGBM, Logistic Regression, and XGBoost. Parameter optimization and model evaluation verify performance, and comparative results identify LightGBM as the best prediction model to support efficient marketing decisions for online stores.","Machine Learning Modeling for Forecasting Repeat Purchases in Online Shopping  \nDuan Lianzhai1*, Dian Tri Hariyanto2  \n1Department of Master Science and Information Technology, Faculty of Computer Science,  \nPresident University, Indonesia  \n2PT Lontar Papyrus Pulp & Paper, Jambi, Indonesia  \n[E-Mail:](E-Mail:1Lark95@163.com)[1](E-Mail:1Lark95@163.com)[Lark95@163.com](E-Mail:1Lark95@163.com), [2](2diantrihariyanto3@gmail.com)[diantrihariyanto3@gmail.com](2diantrihariyanto3@gmail.com)  \nReceived Mar 28th 2024; Revised Apr 5th 2024; Accepted May 15th 2024  \nCorresponding Author: Kurniawan Danil  \nAbstract  \nOnline shopping merchants will conduct a series of marketing activities to increase customers, but in many cases, most of the new customers will not make repeat purchases, which is not conducive to the long-term interests of the merchants. Therefore, it is important for merchants to target users who are more likely to repurchase, as this can reduce marketing costs and increase ROI. Based on the dataset provided by the online shopping website, this paper conducts mining and exploratory analysis of the data, utilizes feature engineering methodology, and modeling analysis using LightGBM, Logistic, Xgboost for machine learning modeling. Meanwhile, parameter optimization and model evaluation verification are performed, Finally, the comparative analysis resulted in Light GBMas the best prediction model, will provide efficient marketing decisions for the operation of online shopping stores.  \nKeyword: Data Analysis, Data Modeling, Machine Learning, Online Shopping, Repeat Purchase Forecast  \n1. INTRODUCTION  \nMerchants sometimes launch large-scale promotions or issue coupons on specific dates to attract consumers. However, many of the buyers attracted are one-time consumers. These promotions may not be helpful to the growth of sales performance in the long term, so for to solve this problem, merchants need to identify which type of consumers can be converted into repeat buyers. By analyzing and positioning these potential loyal customers and conducting precise marketing, merchants can greatly reduce promotional costs and increase return on investment (ROI) . As we all know, it is difficult to accurately target customers when advertising online, especially targeting new consumers. With the development of big data technology and the continuous growth of e-commerce platforms, personal information such as users’ interests and hobbies, as well as behavioral information such as daily shopping, have been accumulated in the databases ofmajor e-commerce platforms, gradually forming a massive amount of data. It has been found that by mining big data on online shopping behavior, users' repeat purchase behavior can be predicted in advance, and it can even be specifically predicted which merchants' products each user has repeat purchase intentions.  \nIn order to predict customers’ decision to purchase, analytical methods such as regression and ML have been used by researchers over the years. The most widely used methods include Stepwise Logistic Regression (SLR) [1], Decision Tree (DT) [2], Random Forest (RF) [3], Support Vector Machines (SVM) [4], and Artificial Neural Networks (ANN) [5] . DT and RF have widespread applications for pre- diction related problems because of their ease of use and the high interpretability of their generated results. Moreover, unlike ANN, DT, and RF are both capable of directly handling categorical variables [2,3]. However, DT is less robust than RF and has been found to be highly sensitive to even small variations in data [6] . Additionally, RF is simpler to tune because it has a smaller number of hyperparameters as compared to neural network-based models [3] . However, ANN has been found to outperform DT and RF in terms of resource utilization and handling of multidimensional complex datasets [6,7] . SLR has been used in extant literature for predictions involving binary dependent variables. However, it suffers f","cbCaitQjujWb468g","https://ap.wps.com/l/cbCaitQjujWb468g","pdf",747033,1,12,"English","en",105,"# Introduction\n## Motivation and Problem Context\n## Related Predictive Modeling Methods\n## Machine Learning and Deep Learning Background","[{\"question\":\"Why is repeat purchase forecasting important for online merchants?\",\"answer\":\"Most new customers may not repurchase after marketing campaigns, which limits long-term sales growth. Predicting likely repurchasers helps reduce marketing costs and improve ROI.\"},{\"question\":\"What modeling approaches are used to predict repeat purchases?\",\"answer\":\"The paper uses feature engineering and machine-learning models including LightGBM, Logistic Regression, and XGBoost. It also performs parameter optimization and model evaluation.\"},{\"question\":\"Which model performs best according to the comparative analysis?\",\"answer\":\"The comparative results indicate that LightGBM achieves the best prediction performance and can support more effective marketing decisions for online shopping operations.\"}]","Machine Learning Modeling for Forecasting Repeat Purchases in Online Shopping | PDF",1785809987,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-modeling-for-forecasting-repeat-purchases-in-online-shopping","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-modeling-for-forecasting-repeat-purchases-in-online-shopping/122319/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is repeat purchase forecasting important for online merchants?","Question",{"text":75,"@type":76},"Most new customers may not repurchase after marketing campaigns, which limits long-term sales growth. Predicting likely repurchasers helps reduce marketing costs and improve ROI.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What modeling approaches are used to predict repeat purchases?",{"text":80,"@type":76},"The paper uses feature engineering and machine-learning models including LightGBM, Logistic Regression, and XGBoost. It also performs parameter optimization and model evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best according to the comparative analysis?",{"text":84,"@type":76},"The comparative results indicate that LightGBM achieves the best prediction performance and can support more effective marketing decisions for online shopping operations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]