[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-117971-105":59,"doc-detail-117971-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","exploring-the-use-of-machine-learning-and-explainability-in-marketing-mix-modeling","Exploring the Use of Machine Learning and Explainability in Marketing Mix Modeling","","This presentation, delivered at the 5th International Conference on Advanced Research Methods and Analytics (CARMA2023), explores the integration of machine learning and explainability techniques within Marketing Mix Modeling (MMM), particularly in the retail industry. Traditionally, MMM has relied on statistical methods like linear regressions to evaluate advertising impact on sales. However, its advancement has been hindered by its practical business focus, proprietary solutions, and the challenge of interpreting complex models, forcing an industry-wide reliance on simpler, less powerful methods. Recent developments in model interpretability, notably through tools like SHAP, now enable the application of sophisticated non-linear machine learning algorithms to MMM. This presentation outlines a methodology to overcome the limitations of traditional MMM, addressing issues such as variable interactions, non-linear relationships, and overall model interpretability, thereby offering a more robust and insightful approach to marketing analytics in the retail sector.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/exploring-the-use-of-machine-learning-and-explainability-in-marketing-mix-modeling/117971/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploring-the-use-of-machine-learning-and-explainability-in-marketing-mix-modeling/117971.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-20","2026-08-02",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What are the main reasons for the slow advancement in Marketing Mix Modeling (MMM)?","Question",{"text":112,"@type":113},"The slow advancement in MMM is primarily due to its focus on practical business applications, the proprietary nature of industry solutions, and the difficulty in interpreting complex models beyond linear regressions for business insights.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the use of SHAP address the challenges in traditional MMM?",{"text":117,"@type":113},"SHAP, a model explainer, enables the application of non-linear machine learning algorithms in MMM, addressing issues like variable interactions, non-linear relationships, and improving overall interpretability, which are often limitations of traditional MMM.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the primary focus of this presentation?",{"text":121,"@type":113},"This presentation outlines a method for incorporating machine learning algorithms with explainability techniques into Marketing Mix Modeling (MMM), specifically within the context of the retail industry.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},117971,1785680585,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":81},962084928432,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","5th International Conference on Advanced Research Methods and Analytics (CARMA2023) Universidad de Sevilla, Sevilla, 2023  \nExploring the use of machine learning and explainability in Marketing Mix Modeling  \nSlava Kisilevich1, Markus Hermann1  \n1Lidl Analytics, Lidl International, Germany  \nAbstract  \nMarketing Mix Modeling (MMM) employs statistical techniques, typically linear regressions, to assess the impact of advertising expenditure on sales. Despite advancements in statistics and machine learning, the field of MMM has remained relatively unchanging due to a few reasons: (1) its primary focus on practical business applications,(2) the proprietary nature ofMMM solutions by specialized companies, and (3) the difficulty in interpreting complex models beyond linear regressions for business purposes.  \nRecently, there has been increased emphasis on the interpretability of complex machine learning models. To address this, model explainers such as SHAP have been introduced, enabling the application of non-linear machine learning algorithms in the realm of MMM. This provides a solution to the various issues associated with traditional MMM methods, including variable interactions, non-linear relationships, and interpretability.  \nThis presentation outlines a method for incorporating machine learning algorithms with explainability techniques in the context of MMM in the retail industry  \nKeywords: MMM; Marketing Mix Modelling; Machine Learning; MMM Explainability; SHAP.  \nThis work is licensed under a Creative Commons License CC BY-NC-SA 4.0  \nEditorial Universitat Politcnica de Valncia 235","cbCaioksx0vkfeTX","https://ap.wps.com/l/cbCaioksx0vkfeTX","pdf",303949,"English","# Exploring the use of machine learning and explainability in Marketing Mix Modeling\n## Abstract\n## Keywords","[{\"question\":\"What are the main reasons for the slow advancement in Marketing Mix Modeling (MMM)?\",\"answer\":\"The slow advancement in MMM is primarily due to its focus on practical business applications, the proprietary nature of industry solutions, and the difficulty in interpreting complex models beyond linear regressions for business insights.\"},{\"question\":\"How does the use of SHAP address the challenges in traditional MMM?\",\"answer\":\"SHAP, a model explainer, enables the application of non-linear machine learning algorithms in MMM, addressing issues like variable interactions, non-linear relationships, and improving overall interpretability, which are often limitations of traditional MMM.\"},{\"question\":\"What is the primary focus of this presentation?\",\"answer\":\"This presentation outlines a method for incorporating machine learning algorithms with explainability techniques into Marketing Mix Modeling (MMM), specifically within the context of the retail industry.\"}]","Exploring the Use of Machine Learning and Explainability in Marketing Mix Modeling | PDF"]