research-article

Beyond Customer Lifetime Valuation: Measuring the Value of Acquisition and Retention for Subscription Services

Authors:

Hamidreza Badri,

Allen TranAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 132 - 140

https://doi.org/10.1145/3485447.3512058

Published: 25 April 2022 Publication History

Abstract

Understanding the value of acquiring or retaining subscribers is crucial for subscription-based businesses. While customer lifetime value (LTV) is commonly used to do so, we demonstrate that LTV likely over-states the true value of acquisition or retention. We establish a methodology to estimate the monetary value of acquired or retained subscribers based on estimating both on and off-service LTV. To overcome the lack of data on off-service households, we use an approach based on Markov chains that recovers off-service LTV from minimal data on non-subscriber transitions. Furthermore, we demonstrate how the methodology can be used to (i) forecast aggregate subscriber numbers that respect both aggregate market constraints and account-level dynamics, (ii) estimate the impact of price changes on revenue and subscription growth and (iii) provide optimal policies, such as price discounting, that maximize expected lifetime revenue.

References

[1]

Josef Bauer and Dietmar Jannach. 2021. Improved Customer Lifetime Value Prediction With Sequence-To-Sequence Learning and Feature-Based Models. ACM Transactions on Knowledge Discovery from Data (TKDD) 15, 5(2021), 1–37.

[2]

Tarek Ben Rhouma and Georges Zaccour. 2018. Optimal marketing strategies for the acquisition and retention of service subscribers. Management Science 64, 6 (2018), 2609–2627.

Digital Library

[3]

Paul D Berger and Nada I Nasr. 1998. Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing 12, 1 (1998), 17–30.

[4]

Benjamin Paul Chamberlain, Angelo Cardoso, CH Bryan Liu, Roberto Pagliari, and Marc Peter Deisenroth. 2017. Customer lifetime value prediction using embeddings. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1753–1762.

Digital Library

[5]

Ching-Hsue Cheng, You-Shyang Chen, and Ya-Ling Wu. 2009. Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model. Expert Systems with Applications 36, 2 (2009), 1826–1832.

Digital Library

[6]

Peter J Danaher. 2002. Optimal pricing of new subscription services: Analysis of a market experiment. Marketing Science 21, 2 (2002), 119–138.

Digital Library

[7]

Ömer Fahrettin Demirel, Selim Zaim, Ahmet Çalişkan, and Pinar Özuyar. 2012. Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods. Turkish Journal of Electrical Engineering & Computer Sciences 20, 5(2012), 695–711.

[8]

Sunil Gupta, Dominique Hanssens, Bruce Hardie, Wiliam Kahn, V Kumar, Nathaniel Lin, Nalini Ravishanker, and S Sriram. 2006. Modeling customer lifetime value. Journal of Service Research 9, 2 (2006), 139–155.

[9]

Keisuke Hirano and Guido W. Imbens. 2004. The Propensity Score with Continuous Treatments. John Wiley & Sons, Ltd, Chapter 7, 73–84. https://doi.org/10.1002/0470090456.ch7 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/0470090456.ch7

[10]

David Hubbard, Benoit Rostykus, Yves Raimond, and Tony Jebara. 2021. Beta Survival Models. In Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021(Proceedings of Machine Learning Research), Russell Greiner, Neeraj Kumar, Thomas Alexander Gerds, and Mihaela van der Schaar (Eds.). Vol. 146. PMLR, 22–39. https://proceedings.mlr.press/v146/hubbard21a.html

[11]

Woonghee Tim Huh, Soulaymane Kachani, and Ali Sadighian. 2010. Optimal pricing and production planning for subscription-based products. Production and Operations Management 19, 1 (2010), 19–39.

[12]

Robin John Hyndman and George Athanasopoulos. 2018. Forecasting: Principles and Practice(2nd ed.). OTexts, Australia.

[13]

Guido W. Imbens and Donald B. Rubin. 2015. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. https://doi.org/10.1017/CBO9781139025751

[14]

William Ogilvy Kermack and Anderson G McKendrick. 1927. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115, 772 (1927), 700–721.

[15]

Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, and Max Welling. 2017. Causal Effect Inference with Deep Latent-Variable Models. In Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 6449–6459.

Digital Library

[16]

Sungjoon Nam, Puneet Manchanda, and Pradeep K Chintagunta. 2007. The effects of service quality and word of mouth on customer acquisition, retention and usage. Retention and Usage(2007).

[17]

Paul R. Rosenbaum and Donald B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 1 (04 1983), 41–55. https://doi.org/10.1093/biomet/70.1.41

[18]

Matthew Rowe. 2011. Forecasting audience increase on Youtube. In Workshop on User Profile Data on the Social Semantic Web (2011).

[19]

Roland Rust, Katherine Lemon, and Valarie Zeithaml. 2004. Return on Marketing: Using Customer Equity To Focus Marketing Strategy. Journal of Marketing 68 (01 2004), 109–127. https://doi.org/10.1509/jmkg.68.1.109.24030

[20]

John Thøgersen. 2009. Promoting public transport as a subscription service: Effects of a free month travel card. Transport Policy 16, 6 (2009), 335–343.

[21]

Ali Vanderveld, Addhyan Pandey, Angela Han, and Rajesh Parekh. 2016. An engagement-based customer lifetime value system for e-commerce. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 293–302.

Digital Library

[22]

Huizhi Xie and Juliette Aurisset. 2016. Improving the sensitivity of online controlled experiments: Case studies at Netflix. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 645–654.

Digital Library

[23]

Jie Xiong, Väinö Jääskinen, and Jukka Corander. 2016. Recursive learning for sparse Markov models. Bayesian analysis 11, 1 (2016), 247–263.

Cited By

Drozda PRopiak KMozalewski ŁMałaczyński MFrukacz M(2025)Different Approaches for Customer Lifetime Value in e-Book Subscription DomainArtificial Intelligence and Soft Computing10.1007/978-3-031-81596-6_6(65-76)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-81596-6_6
Zhou ZLin LWang HZhou XWei GWang SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Cross Domain Method for Customer Lifetime Value Prediction in Supply Chain PlatformProceedings of the ACM Web Conference 202410.1145/3589334.3645391(4037-4046)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645391
Massiah JYilmaz EJiao YKazai GFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)On the Reliability of User Feedback for Evaluating the Quality of Conversational AgentsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615286(4185-4189)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615286
Show More Cited By

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 25 April 2022

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WWW '22

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Drozda PRopiak KMozalewski ŁMałaczyński MFrukacz M(2025)Different Approaches for Customer Lifetime Value in e-Book Subscription DomainArtificial Intelligence and Soft Computing10.1007/978-3-031-81596-6_6(65-76)Online publication date: 17-Feb-2025
https://doi.org/10.1007/978-3-031-81596-6_6
Zhou ZLin LWang HZhou XWei GWang SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)A Cross Domain Method for Customer Lifetime Value Prediction in Supply Chain PlatformProceedings of the ACM Web Conference 202410.1145/3589334.3645391(4037-4046)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645391
Massiah JYilmaz EJiao YKazai GFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)On the Reliability of User Feedback for Evaluating the Quality of Conversational AgentsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615286(4185-4189)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615286
Lamela-Orcasitas CGarcía-Madariaga J(2023)How to really quantify the economic value of customer information in corporate databasesHumanities and Social Sciences Communications10.1057/s41599-023-01654-610:1Online publication date: 13-Apr-2023
https://doi.org/10.1057/s41599-023-01654-6

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