Juliana Sanchez Ramirez , Kristof Coussement , Arno De Caigny , Dries F. Benoit , Emil Guliyev
{"title":"结合使用数据建立 B2B 客户流失预测模型","authors":"Juliana Sanchez Ramirez , Kristof Coussement , Arno De Caigny , Dries F. Benoit , Emil Guliyev","doi":"10.1016/j.indmarman.2024.05.008","DOIUrl":null,"url":null,"abstract":"<div><p>The potential for predicting churn in B2B contexts remains untapped despite the growing availability of digital customer traces, particularly usage data. Nevertheless, this source provides valuable insights into customer behavior and product interaction, serving as a powerful tool for understanding customer needs and improving retention efforts. This paper aims to explore the value of usage data for B2B customer churn prediction using a real-world dataset with 3959 subscriptions from a European software provider. The contribution to literature is two-fold. First, we define a framework to structure usage data into cross-sectional features that could be included in predictive models. We study the impact of usage information on churn prediction performance along three primary components– timing, granularity, and expertise. The findings confirm the value of this data source and two of the three examined components, particularly in terms of AUC and TDL. Second, we provide insights into the carbon footprint of the entire modeling process required to integrate usage data into different machine-learning algorithms. Furthermore, to strengthen the robustness of our findings, we compare five common machine-learning algorithms in CCP. Consequently, this study uncovered unexplored aspects of usage data and practical implications for enhancing churn prediction in a B2B setting.</p></div>","PeriodicalId":51345,"journal":{"name":"Industrial Marketing Management","volume":null,"pages":null},"PeriodicalIF":7.8000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Incorporating usage data for B2B churn prediction modeling\",\"authors\":\"Juliana Sanchez Ramirez , Kristof Coussement , Arno De Caigny , Dries F. Benoit , Emil Guliyev\",\"doi\":\"10.1016/j.indmarman.2024.05.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The potential for predicting churn in B2B contexts remains untapped despite the growing availability of digital customer traces, particularly usage data. Nevertheless, this source provides valuable insights into customer behavior and product interaction, serving as a powerful tool for understanding customer needs and improving retention efforts. This paper aims to explore the value of usage data for B2B customer churn prediction using a real-world dataset with 3959 subscriptions from a European software provider. The contribution to literature is two-fold. First, we define a framework to structure usage data into cross-sectional features that could be included in predictive models. We study the impact of usage information on churn prediction performance along three primary components– timing, granularity, and expertise. The findings confirm the value of this data source and two of the three examined components, particularly in terms of AUC and TDL. Second, we provide insights into the carbon footprint of the entire modeling process required to integrate usage data into different machine-learning algorithms. Furthermore, to strengthen the robustness of our findings, we compare five common machine-learning algorithms in CCP. Consequently, this study uncovered unexplored aspects of usage data and practical implications for enhancing churn prediction in a B2B setting.</p></div>\",\"PeriodicalId\":51345,\"journal\":{\"name\":\"Industrial Marketing Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.8000,\"publicationDate\":\"2024-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Industrial Marketing Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0019850124000865\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Industrial Marketing Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019850124000865","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
Incorporating usage data for B2B churn prediction modeling
The potential for predicting churn in B2B contexts remains untapped despite the growing availability of digital customer traces, particularly usage data. Nevertheless, this source provides valuable insights into customer behavior and product interaction, serving as a powerful tool for understanding customer needs and improving retention efforts. This paper aims to explore the value of usage data for B2B customer churn prediction using a real-world dataset with 3959 subscriptions from a European software provider. The contribution to literature is two-fold. First, we define a framework to structure usage data into cross-sectional features that could be included in predictive models. We study the impact of usage information on churn prediction performance along three primary components– timing, granularity, and expertise. The findings confirm the value of this data source and two of the three examined components, particularly in terms of AUC and TDL. Second, we provide insights into the carbon footprint of the entire modeling process required to integrate usage data into different machine-learning algorithms. Furthermore, to strengthen the robustness of our findings, we compare five common machine-learning algorithms in CCP. Consequently, this study uncovered unexplored aspects of usage data and practical implications for enhancing churn prediction in a B2B setting.
期刊介绍:
Industrial Marketing Management delivers theoretical, empirical, and case-based research tailored to the requirements of marketing scholars and practitioners engaged in industrial and business-to-business markets. With an editorial review board comprising prominent international scholars and practitioners, the journal ensures a harmonious blend of theory and practical applications in all articles. Scholars from North America, Europe, Australia/New Zealand, Asia, and various global regions contribute the latest findings to enhance the effectiveness and efficiency of industrial markets. This holistic approach keeps readers informed with the most timely data and contemporary insights essential for informed marketing decisions and strategies in global industrial and business-to-business markets.