[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127718-en":3,"doc-seo-127718-105":30,"detail-sidebar-cat-0-en-105":84},{"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},127718,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling - Research proposal","Effective cross-selling practices strengthen customer relationships and streamline insurance business processes. This study proposes a novel three-stage Machine Learning-Based System (MLBS) to identify potential insurance customers and improve customer relationship management through better targeting. It combines under-sampling with an ensemble strategy, selecting the best training samples via artificial neural networks and using stacking for prediction. The approach achieves superior recall, precision, and AUC versus baseline models, enhancing support for cross-selling campaigns.","A Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling  \nXiaoguang Tian  \nPurdue University Fort Wayne  \nJun Todorovic  \nPurdue University Fort Wayne  \nZelimir Todorovic  \nPurdue University Fort Wayne  \nEffective cross-selling practices are integral to maintaining strong customer relationships and optimizing business processes within the insurance industry. This study presents a novel three-stage Machine Learning-Based System (MLBS) designed to enhance the identification of potential insurance customers and improve customer relationship management. This study proposes combining under sampling strategies and an ensemble approach to improve prediction performance. The proposed MLBS method involves selecting the best training sample using artificial neural networks and employing the stacking ensemble approach. It yields superior prediction results, exhibiting the highest recall, precision, and Area Under the Curve (AUC). These advancements substantially bolster the efficiency of cross-selling strategies. This research pioneers the application of stacking ensemble learning within the cross-selling domain, representing a novel contribution to the business field. The outcomes underscore the superiority of the MLBS system over baseline models across multiple performance metrics, thereby significantly enhancing support for cross-selling campaigns in various businesses.  \nKeywords: digital transformation, insurance cross-selling, customer relationship management, machine learning, artificial neural networks  \nINTRODUCTION  \nThe innovative approach to marketing management has been associated with increased organizational performance and profitability. Marketing management is also a critical value-adding process in the business value chain of many organizations (Osarenkhoe & Bennani, 2007) . As a subdomain of marketing management, cross-selling is commonly used by the financial service industry to increase sales and profitability. Although cross-selling strategy focuses on selling peripheral and more expensive services to existing customers (Vyas & Math 2006), it must be noted that not all cross-selling efforts are worth the time and monetary investments made in them (Rosen, 2004) . For this reason, increasing the effectiveness of cross-selling activities while utilizing fewer resources is vital. As a result, many organizations today are  \nattempting to be more proactive and innovative to gain competitive advantages through information and technology. The information available in current corporate databases enhances business opportunities and gains further insights. These insights will help “provide better customer service, make call centers more efficient, cross-sell products more effectively, help sales staff close deals faster, simplify marketing and sales processes, discover new customers, increase customer revenues, etc.”(Osarenkhoe & Bennani, 2007, p. 156) .  \nSome studies explored various methods of identifying potential customers as the target market for cross-selling efforts. For example, Kamakura et al. (1991) developed a probability model to identify prospects based on their acquisition of financial products. Building on their original work, Kamakura et al.(2003) improved their model by adding a “factor analyzer” to enhance their model efficiency. Harrison and Ansell (2002) used the statistical method known as the “survival approach” to identify prospects and determine which product will be bought and when the purchase will be made. Knott et al. (2002) developed a next-product-to-buy (NPTB) model to predict which product a customer would most likely buy next using four different statistical techniques focusing on logistic regression, multinomial logit, discriminant analysis, and neural nets. Knott et al. (2002) found that, although the NPTB model generated incremental crossselling profits, its predictive power was not influenced by the statistical technique used dur","cbCaissLrfIqrw7W","https://ap.wps.com/l/cbCaissLrfIqrw7W","pdf",321390,1,17,"English","en",105,"# Introduction\n## Cross-selling as a marketing management process\n## Prior customer identification and prediction approaches\n## CRM, BPM, and the insurance cross-selling research gap\n## Study objective and proposed MLBS overview","[{\"question\":\"How does the MLBS perform compared with baseline models?\",\"answer\":\"The system delivers higher recall, precision, and AUC, demonstrating superiority across multiple performance metrics.\"}]","Machine-Learning-Based Business Analytical System for Insurance Customer Relationship Management and Cross-Selling - Research proposal | PDF",1785941160,43,{"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":79,"head_meta":81,"extra_data":83,"updated_unix":28},"machine-learning-based-business-analytical-system-for-insurance-customer-relationship-management-and-cross-selling-research-proposal","",{"@graph":36,"@context":78},[37,54,69],{"@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-based-business-analytical-system-for-insurance-customer-relationship-management-and-cross-selling-research-proposal/127718/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"How does the MLBS perform compared with baseline models?","Question",{"text":76,"@type":77},"The system delivers higher recall, precision, and AUC, demonstrating superiority across multiple performance metrics.","Answer","https://schema.org",{"og:url":52,"og:type":80,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":82,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":85},[86,90,94,98,103,108,113,116,121,124,128],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":87,"show_sort_weight":88,"slug":89},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Exam",70,"exam",{"id":99,"doc_module":4,"doc_module_name":46,"category_name":100,"show_sort_weight":101,"slug":102},5,"Comic",60,"comic",{"id":104,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},6,"Technology",50,"technology",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":114,"slug":115},30,"research-report",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},9,"Religion & Spirituality",20,"religion-spirituality",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":122,"show_sort_weight":119,"slug":123},"World Cup","world-cup",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":125,"slug":127},10,"Lifestyle","lifestyle",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":99,"slug":131},19,"General","general"]