[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124304-en":3,"doc-seo-124304-105":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":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},124304,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Application of Machine Learning Algorithms in Predicting Customer Loyalty - Towards Grocery Retailers","Retailers pursue customer loyalty not only through repeat purchases, but also via a higher share of purchases compared with competitors and a willingness to recommend. This study treats loyalty in grocery retail as a three-dimensional construct and evaluates supervised machine learning for prediction. Retailer price characteristics and non-price characteristics are incorporated as predictors. Using Croatia-collected data (433 samples), models are trained with multiple predictors to classify customer loyalty.","APPLICATION OF MACHINE LEARNING ALGORITHMS IN PREDICTING CUSTOMER LOYALTY TOWARDS GROCERY RETAILERS  \nJelena Franjkovic 1 ,  \nIvana Fosic2 ,  \nAna Zivkovic3  \nAbstract: Retailers strive for customer loyalty in the sense of repeat purchases, but also as a high proportion of purchases (compared to competitors) and willingness to recommend to other customers. This paper examines customer loyalty in the grocery sector as a three-dimensional construct and shows how machine learning techniques can be useful in its study. Price characteristics of the retailer (price level, value for money, price dynamics, price communication and price dispersion) and non-price characteristics of the retailer (general product range, retailer's private label product range, store design and atmosphere, service level and location) are included in the model as predictor variables. Using the data collected through the primary research conducted in Croatia, 433 samples were divided into 10 independent predictor variables and one dependent variable (customer loyalty), a prediction was created using supervised machine learning classification algorithms. The Random Forest classifier proves to be the best choice overall, with ROC_AUC value of 0.790, a high accuracy of 0.915 and an F1 score of 0.954, reflecting both precision and responsiveness. The application of the SHapley Additive exPlanations analysis additionally enables the interpretation of the results, highlighting the influence of features on the accuracy of the prediction. The results indicate that price dynamics and service level are the most important features for the model predictions, followed by value for money and price communication.  \n1 Department of Marketing , Faculty of Economics and Business in Osijek, Osijek, Croatia, [e-mail: ](e-mail: jelena.franjkovic@efos.hr)[jelena.franjkovic@efos.hr](e-mail: jelena.franjkovic@efos.hr), ORCID: 0000-0001-7725-3098  \n2 Department of Management, Organization and Entrepreneurship, Faculty of Economics and Business in Osijek, Osijek, Croatia, e-mail: [ivana.fosic@efos.hr](ivana.fosic@efos.hr) , ORCID: 0000-0003-4893-3654  \n3 Department of Management, Organization and Entrepreneurship, Faculty of Economics and Business in Osijek, Osijek, Croatia, e-mail: [ana.zivkovic@efos.hr](ana.zivkovic@efos.hr) , ORCID: 0000-0002-6469-4377  \nKey words: customer loyalty, grocery retail, supervised machine learning, prediction, price dynamics  \nJEL: M30, C53 .  \nDOI: [https://doi.org/10.58861/tae.bm.2025.2.05](https://doi.org/10.58861/tae.bm.2025.2.05)  \nIntroduction  \nEvery company strives for customer loyalty, for a long-term relationship with the consumer. The specificity of grocery and food retailing lies in a wide range of factors, including the price and non-price characteristics of the retailer, which can influence the loyalty of customers towards the retailer and its brand as a company. The approach to customer loyalty in retail is more sophisticated and comprehensive than loyalty to a single manufacturer brand. Retailers advertise on price and product availability while providing a certain level of service that consumers continually evaluate. Retailers collect data on consumers through POS systems and loyalty programs, which encourages the use of machine learning (ML) approaches to data analysis. It is also even easier for online retailers to use AI and ML tools (Gauri et al. , 2021) . The topic is also gaining attention among academics who are exploring the role and possibilities of machine learning, AI and IoT (Popova, & Petrova, 2024; Popova et al. , 2024; Petrova et al. , 2022) and its predictive capabilities with the aim of increasing customer satisfaction and loyalty (e.g. Andresini et al. , 2023; Wang et al. , 2021; Rane et al. , 2023; Islamgaleyev et al. , 2020; Arefinet al. , 2024) . The aim of this paper is to evaluate the application of ML algorithms to predict loyalty towards retailers in traditional grocery retailing, focusing on the high accuracy a","cbCaii4DqRgrst0P","https://ap.wps.com/l/cbCaii4DqRgrst0P","pdf",2463614,1,20,"English","en",105,"# Introduction\n## Customer loyalty and influencing factors\n# Abstract and Method Overview\n## Predictors and Data\n## Supervised Learning Models\n## Model Interpretation with SHAP","[{\"question\":\"How is customer loyalty defined in this grocery retail study?\",\"answer\":\"It is modeled as a three-dimensional construct including repeat purchase intention, purchase share, and willingness to recommend to other customers.\"},{\"question\":\"Which types of features are used to predict loyalty?\",\"answer\":\"The model includes price-related features (price level, value for money, price dynamics, price communication, and price dispersion) and non-price features (product range, private label range, store design/atmosphere, service level, and location).\"},{\"question\":\"Why is Random Forest highlighted as the best performing model?\",\"answer\":\"The Random Forest classifier achieves strong overall metrics, including ROC_AUC of 0.790, accuracy of 0.915, and an F1 score of 0.954, reflecting both precision and responsiveness.\"}]","Application of Machine Learning Algorithms in Predicting Customer Loyalty - Towards Grocery Retailers | PDF",1785821491,50,{"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},"application-of-machine-learning-algorithms-in-predicting-customer-loyalty-towards-grocery-retailers","",{"@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/application-of-machine-learning-algorithms-in-predicting-customer-loyalty-towards-grocery-retailers/124304/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is customer loyalty defined in this grocery retail study?","Question",{"text":75,"@type":76},"It is modeled as a three-dimensional construct including repeat purchase intention, purchase share, and willingness to recommend to other customers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which types of features are used to predict loyalty?",{"text":80,"@type":76},"The model includes price-related features (price level, value for money, price dynamics, price communication, and price dispersion) and non-price features (product range, private label range, store design/atmosphere, service level, and location).",{"name":82,"@type":73,"acceptedAnswer":83},"Why is Random Forest highlighted as the best performing model?",{"text":84,"@type":76},"The Random Forest classifier achieves strong overall metrics, including ROC_AUC of 0.790, accuracy of 0.915, and an F1 score of 0.954, reflecting both precision and responsiveness.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]