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Estimating Demand for Subscription Products: Identification of Willingness to Pay Without Price Variation

Author

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  • Cheng Chou

    (School of Business, University of Leicester, Leicester LE1 7RH, United Kingdom)

  • Vineet Kumar

    (School of Management, Yale University, New Haven, Connecticut 06511)

Abstract

We demonstrate how to obtain the distribution of consumer willingness to pay (WTP) for digital subscription products, where consumers pay a fixed price each period for potentially unlimited usage, for example, music streaming like Spotify. Typically, in such applications, usage data are observed and is critically valuable for the method here. We demonstrate how variation in usage and subscription choice together can identify the WTP distribution in the absence of price variation. Our framework accommodates and builds upon a range of utility specifications for usage, which is related to subscription decisions. We provide the conditions required on exogenous variation impacting usage, and prove how these lead to identification of the WTP distribution. We also investigate the conditions under which usage variation is not equivalent to price variation. We apply our method to an empirical application using the data from a music streaming service. Using the estimated WTP distribution, we obtain the revenue maximizing prices for different consumer segments.

Suggested Citation

  • Cheng Chou & Vineet Kumar, 2024. "Estimating Demand for Subscription Products: Identification of Willingness to Pay Without Price Variation," Marketing Science, INFORMS, vol. 43(4), pages 797-816, July.
  • Handle: RePEc:inm:ormksc:v:43:y:2024:i:4:p:797-816
    DOI: 10.1287/mksc.2021.0426
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    Cited by:

    1. Pedro M. Gardete & Daniela Schmitt & Florian Stahl, 2025. "Pricing and consumption in subscription settings," Nova SBE Working Paper Series wp674, Universidade Nova de Lisboa, Nova School of Business and Economics.
    2. Soheil Ghili & K. Sudhir & Nitish Jain & Ankur Garg, 2025. "Second-degree Price Discrimination: Theoretical Analysis, Experiment Design, and Empirical Estimation," Papers 2507.13426, arXiv.org, revised Oct 2025.

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