[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126151-en":3,"doc-seo-126151-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126151,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The relevance of lead prioritization - a B2B lead scoring model based on machine learning","B2B marketing and sales teams struggle to identify, qualify, and prioritize large lead volumes efficiently, limiting their ability to focus on the most valuable opportunities and to optimize qualification time and digital marketing performance. This article presents a case study of a B2B software firm developing a lead scoring model using CRM lead data from January 2020 to April 2024. Fifteen classification algorithms were tested, and Gradient Boosting achieved the strongest accuracy and ROC AUC. Feature importance highlighted predictors such as source and lead status, improving conversion prediction. The research links consumer theory to machine learning for lead scoring and helps align marketers and data scientists.","TYPE Original Research PUBLISHED 07 March 2025  \nDOI 10. 3389/frai.2025.1554325  \nOPEN ACCESS  \nEDITED BY  \nErfan Babaee Tirkolaee, University of Istinye, Türkiye  \nREVIEWED BY  \nEvren ¸Sadi ¸SEKER, Istanbul University, Türkiye Tereza Semerádová,  \nTechnical University of Liberec, Czechia  \n*CORRESPONDENCE  \nGuillermo Sosa-Gómez  \n [gsosag@up.edu.mx](gsosag@up.edu.mx)  \nRECEIVED 01 January 2025  \nACCEPTED 17 February 2025  \nPUBLISHED 07 March 2025  \nCITATION  \nGonzález-Flores L, Rubiano-Moreno J and Sosa-Gómez G (2025) The relevance of lead prioritization: a B2B lead scoring model based on machine learning.  \nFront. Artif. Intell. 8:1554325 .  \ndoi: 10.3389/frai.2025.1554325  \nCOPYRIGHT  \n© 2025 González-Flores, Rubiano-Moreno and Sosa-Gómez. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nThe relevance of lead prioritization: a B2B lead scoring model based on machine learning  \nLaura González-Flores1 , Jessica Rubiano-Moreno2 and Guillermo Sosa-Gómez1*  \n1 Universidad Panamericana, Facultad de Ciencias Económicas y Empresariales, Zapopan, Jalisco, Mexico, 2 Facultad de Ciencias Administrativas y Comerciales, Universidad de Ciencias Aplicadas y Ambientales, Bogotá, Colombia  \nIn business-to-business (B2B) companies, marketing and sales teams face significant challenges in identifying, qualifying, and prioritizing a large number of leads. Lead prioritization is a critical task for B2B organizations because it allows them to allocate resources more e􀀀ectively, focus their sales force on the most viable and valuable opportunities, optimize their time spent qualifying leads, and maximize their B2B digital marketing strategies. This article addresses the topic by presenting a case study of a B2B software company’s development of a lead scoring model based on data analytics and machine learning under the consumer theory approach. The model was developed using real lead data generated between January 2020 and April 2024, extracted from the company’s CRM, which were analyzed and evaluated by fifteen classification algorithms, where the results in terms of accuracy and ROC AUC showed a superior performance of the Gradient Boosting Classifier over the other classifiers. Atthe same time, the feature importance analysis allowed the identification of features such as “source” and “lead status,” which increased the accuracy of the conversion prediction. The developed model significantly improved the company’s ability to identify high quality leads compared to the traditional methods used. This research confirms and complements existing theories related to understanding the application of consumer behavior theory and the application of machine learning in the development of B2B lead scoring models. This study also contributes to bridging the gap between marketers and data scientists in jointly understanding lead scoring as a critical activity because of its impact on overall marketing strategy performance and sales revenue performance in B2B organizations.  \nKEYWORDS  \nlead scoring, digital marketing, B2B sales, business-to-business, marketing automation, lead qualification, lead conversion, CRM  \n1 Introduction  \nIn business-to-business (B2B) sales processes, marketing and sales teams face the challenge of identifying, qualifying, and prioritizing leads. Lead quali􀀂cation is a critical task because it impacts conversion rates and maximizes the e􀀓ectiveness of marketing and sales e􀀓orts and strategies (Priya, 2020) . Digital marketing strongly emphasizes the need to address potential customers in a personalized manner in the B2B segment (EspadinhaCruz ","cbCaibnt0Ar3e2WR","https://ap.wps.com/l/cbCaibnt0Ar3e2WR","pdf",3634798,6,1,23,"English","en",105,"# Introduction\n## Lead qualification and prioritization in B2B\n## Digital marketing personalization challenges\n## Industry 4.0 and data-driven market digitization","[{\"question\":\"Why is lead prioritization important for B2B organizations?\",\"answer\":\"It helps allocate resources more effectively, focuses sales on the most viable opportunities, optimizes qualification time, and strengthens B2B digital marketing performance.\"},{\"question\":\"How was the lead scoring model developed in the study?\",\"answer\":\"The model was built using real lead data extracted from the company’s CRM, covering January 2020 to April 2024, and evaluated with multiple classification algorithms.\"},{\"question\":\"Which factors most improved the model’s conversion prediction?\",\"answer\":\"Feature importance analysis identified variables such as “source” and “lead status,” which increased the accuracy of conversion prediction.\"}]","The relevance of lead prioritization - 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