Pub Date : 2023-11-08DOI: 10.1007/s11129-023-09272-x
Yan Yan, Nicholas Resnick
Abstract Customer lifetime value (CLV) modeling underpins modern marketing analytics, enabling the development of tailored customer relationship management strategies based on the predicted future value of their customers. As part of Amperity’s enterprise customer data platform (CDP), we deploy and maintain a CLV prediction system that caters to a rapidly growing list of brands across various industries, purchase behaviors, and scales. Given the impracticality of developing bespoke models for each brand, our solution must be adaptive, generalizable, and high-performing ”out of the box”. Furthermore, our platform demands daily prediction updates to facilitate prompt marketing decisions. This paper introduces a turnkey CLV prediction system that achieves state-of-the-art performance across a diverse set of brands. This system has several contributions: 1) the use of encodings and embeddings to incorporate signals from high-cardinality data; 2) a multi-stage churn-CLV modeling framework that augments additional flexibility in adjusting churn probabilities, subsequently reducing CLV prediction errors while maintaining a synergistic learning process; 3) a feature-weighted ensemble of both generative and discriminative models to accommodate diverse underlying purchase patterns. Empirical results show that our enhanced model consistently surpasses benchmark performances for twelve retail brands across six evaluation intervals from June 2020 to September 2022.
{"title":"A high-performance turnkey system for customer lifetime value prediction in retail brands","authors":"Yan Yan, Nicholas Resnick","doi":"10.1007/s11129-023-09272-x","DOIUrl":"https://doi.org/10.1007/s11129-023-09272-x","url":null,"abstract":"Abstract Customer lifetime value (CLV) modeling underpins modern marketing analytics, enabling the development of tailored customer relationship management strategies based on the predicted future value of their customers. As part of Amperity’s enterprise customer data platform (CDP), we deploy and maintain a CLV prediction system that caters to a rapidly growing list of brands across various industries, purchase behaviors, and scales. Given the impracticality of developing bespoke models for each brand, our solution must be adaptive, generalizable, and high-performing ”out of the box”. Furthermore, our platform demands daily prediction updates to facilitate prompt marketing decisions. This paper introduces a turnkey CLV prediction system that achieves state-of-the-art performance across a diverse set of brands. This system has several contributions: 1) the use of encodings and embeddings to incorporate signals from high-cardinality data; 2) a multi-stage churn-CLV modeling framework that augments additional flexibility in adjusting churn probabilities, subsequently reducing CLV prediction errors while maintaining a synergistic learning process; 3) a feature-weighted ensemble of both generative and discriminative models to accommodate diverse underlying purchase patterns. Empirical results show that our enhanced model consistently surpasses benchmark performances for twelve retail brands across six evaluation intervals from June 2020 to September 2022.","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"103 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-24DOI: 10.1007/s11129-023-09268-7
Nico Neumann, Catherine E. Tucker, Kumar Subramanyam, John Marshall
Abstract Often marketers face the challenge of how to communicate best with the customers who have the right responsibilities, influence or purchasing power, especially in business-to-business (B2B) settings. For example, B2B marketers selling software and IT need to identify IT decision-makers (ITDMs) within organizations. The modern digital environment in theory allows marketers to target individuals in organizations through specifically designed third-party audience segments based on deterministic prospect lists or probabilistic inference. However, in this paper we show that in our context, such ‘off-the-shelf’ segments perform no better at reaching the right person than random prospecting. We present evidence that even deterministic attribute information is flawed for ITDM identification, and that the poor campaign results can be partly linked to incorrect assignment of established prospect profiles to online identifiers. We then use access to our publisher network data to investigate what would happen if the advertiser had used first-party data that are less susceptible to the identified issues. We demonstrate that first-party demographics or contextual information allows advertisers and publishers to outperform both third-party ITDM audience segments and random prospecting. Our findings have implications for understanding the shift in digital advertising away from third-party cookie tracking, and how to execute digital marketing in the context of broad privacy regulation.
{"title":"Is first- or third-party audience data more effective for reaching the ‘right’ customers? The case of IT decision-makers","authors":"Nico Neumann, Catherine E. Tucker, Kumar Subramanyam, John Marshall","doi":"10.1007/s11129-023-09268-7","DOIUrl":"https://doi.org/10.1007/s11129-023-09268-7","url":null,"abstract":"Abstract Often marketers face the challenge of how to communicate best with the customers who have the right responsibilities, influence or purchasing power, especially in business-to-business (B2B) settings. For example, B2B marketers selling software and IT need to identify IT decision-makers (ITDMs) within organizations. The modern digital environment in theory allows marketers to target individuals in organizations through specifically designed third-party audience segments based on deterministic prospect lists or probabilistic inference. However, in this paper we show that in our context, such ‘off-the-shelf’ segments perform no better at reaching the right person than random prospecting. We present evidence that even deterministic attribute information is flawed for ITDM identification, and that the poor campaign results can be partly linked to incorrect assignment of established prospect profiles to online identifiers. We then use access to our publisher network data to investigate what would happen if the advertiser had used first-party data that are less susceptible to the identified issues. We demonstrate that first-party demographics or contextual information allows advertisers and publishers to outperform both third-party ITDM audience segments and random prospecting. Our findings have implications for understanding the shift in digital advertising away from third-party cookie tracking, and how to execute digital marketing in the context of broad privacy regulation.","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"20 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-27DOI: 10.1007/s11129-023-09264-x
M. Nicolini, C. Piga, Andrea Pozzi
{"title":"From uniform to bespoke prices: Hotel pricing during EURO 2016","authors":"M. Nicolini, C. Piga, Andrea Pozzi","doi":"10.1007/s11129-023-09264-x","DOIUrl":"https://doi.org/10.1007/s11129-023-09264-x","url":null,"abstract":"","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"21 1","pages":"333 - 355"},"PeriodicalIF":1.9,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44255990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1007/s11129-023-09266-9
P. Jost
{"title":"Price commitment and the strategic launch of a fighter brand","authors":"P. Jost","doi":"10.1007/s11129-023-09266-9","DOIUrl":"https://doi.org/10.1007/s11129-023-09266-9","url":null,"abstract":"","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"21 1","pages":"381 - 435"},"PeriodicalIF":1.9,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49001448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-05DOI: 10.1007/s11129-023-09265-w
Bikram P. Ghosh, Michael R. Galbreth
{"title":"Effect of search cost in the presence of search deterring informative advertising","authors":"Bikram P. Ghosh, Michael R. Galbreth","doi":"10.1007/s11129-023-09265-w","DOIUrl":"https://doi.org/10.1007/s11129-023-09265-w","url":null,"abstract":"","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"21 1","pages":"357 - 379"},"PeriodicalIF":1.9,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42077781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1007/s11129-023-09262-z
Iman Ahmadi
{"title":"Face/Off: The adverse effects of increased competition","authors":"Iman Ahmadi","doi":"10.1007/s11129-023-09262-z","DOIUrl":"https://doi.org/10.1007/s11129-023-09262-z","url":null,"abstract":"","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"21 1","pages":"183 - 279"},"PeriodicalIF":1.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48755831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-10DOI: 10.1007/s11129-023-09261-0
Geoffrey Fisher, Matthew McGranaghan, Jūra Liaukonytė, Kenneth C. Wilbur
{"title":"Price promotions, beneficiary framing, and mental accounting","authors":"Geoffrey Fisher, Matthew McGranaghan, Jūra Liaukonytė, Kenneth C. Wilbur","doi":"10.1007/s11129-023-09261-0","DOIUrl":"https://doi.org/10.1007/s11129-023-09261-0","url":null,"abstract":"","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"1 1","pages":"1-35"},"PeriodicalIF":1.9,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41851338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-29DOI: 10.1007/s11129-022-09260-7
Adam N. Smith, J. Griffin
{"title":"Shrinkage priors for high-dimensional demand estimation","authors":"Adam N. Smith, J. Griffin","doi":"10.1007/s11129-022-09260-7","DOIUrl":"https://doi.org/10.1007/s11129-022-09260-7","url":null,"abstract":"","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"21 1","pages":"95-146"},"PeriodicalIF":1.9,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47115506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-13DOI: 10.1007/s11129-022-09256-3
Yichun Liu, Jeff D. Brazell, Greg M. Allenby
{"title":"Non-linear pricing effects in conjoint analysis","authors":"Yichun Liu, Jeff D. Brazell, Greg M. Allenby","doi":"10.1007/s11129-022-09256-3","DOIUrl":"https://doi.org/10.1007/s11129-022-09256-3","url":null,"abstract":"","PeriodicalId":46425,"journal":{"name":"Qme-Quantitative Marketing and Economics","volume":"20 1","pages":"397 - 430"},"PeriodicalIF":1.9,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48474300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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