{"id":16502,"date":"2025-06-04T10:12:21","date_gmt":"2025-06-04T14:12:21","guid":{"rendered":"https:\/\/www.rochester.edu\/coe\/?p=16502"},"modified":"2025-06-04T10:12:55","modified_gmt":"2025-06-04T14:12:55","slug":"wegmans-predicting-customer-reorder-intervals-for-wegmans-meals2go","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/coe\/wegmans-predicting-customer-reorder-intervals-for-wegmans-meals2go\/","title":{"rendered":"Wegmans: Predicting Customer Reorder Intervals for Wegmans Meals2GO"},"content":{"rendered":"

\"Predicting<\/p>\n

Team Members: Manasvi Patwa, Neel Agarwal, Pranav Yeola, Shyam Shah<\/em><\/p>\n

The goal of this project was to predict the time intervals between customer orders for Wegmans Meals2GO to enhance inventory management and personalize marketing strategies. To achieve this goal, students analyzed a dataset of over 1.5 million items ordered by 277,099 customers over two years. The team refined the dataset through comprehensive preprocessing and feature engineering to ensure consistency and reliability. They then utilized a bucketing strategy to segment customers based on their ordering patterns, and employed machine learning models, such as Extreme Gradient Boosting (XGBoost), to predict reorder intervals. Finally, students explored iterative modeling techniques to further improve their model\u2019s predictive accuracy. The results showed that XGBoost can effectively predict reorder intervals with time-based data splits to improve model performance. This analysis provides actionable insights for Wegmans Meals2GO that will optimize future inventory planning and align promotional strategies with customer behaviors.<\/p>\n","protected":false},"excerpt":{"rendered":"

Team Members: Manasvi Patwa, Neel Agarwal, Pranav Yeola, Shyam Shah The goal of this project was to predict the time intervals between customer orders for Wegmans Meals2GO to enhance inventory…<\/p>\n","protected":false},"author":162,"featured_media":16522,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[42],"tags":[192,202],"class_list":["post-16502","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fall-2024-projects","tag-retail","tag-wegmans"],"yoast_head":"\nWegmans: Predicting Customer Reorder Intervals for Wegmans Meals2GO - Center of Excellence in Data Science and Artificial Intelligence<\/title>\n<meta name=\"description\" content=\"Using over 1.5 million customer order records, this project applied XGBoost and feature engineering to predict reorder intervals. 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