Personalizing Ad Load to Optimize Subscription and Ad Revenues: Product Strategies Constructed from Experiments on Pandora

Ali Goli (), David H. Reiley () and Hongkai Zhang ()
Additional contact information
Ali Goli: University of Washington, Seattle, Washington 98195
David H. Reiley: SiriusXM Pandora, Oakland, California 94612
Hongkai Zhang: SiriusXM Pandora, Oakland, California 94612

Marketing Science, 2025, vol. 44, issue 2, 327-352

Abstract: The role of advertising as an implicit price has long been recognized in economics and marketing, yet the effects of personalizing these implicit prices on firm profits and consumer welfare have not been explored. Using a large-scale field experiment on Pandora, we exogenously changed the ad load—the number of ads played per hour—for more than seven million users over 18 months. We find that the impact of ad load on consumption takes more than a year to stabilize, whereas subscription responses stabilize in less than six months. Using a machine-learning model, we analyze the heterogeneous effects of changing ad load on ad and subscription revenues. We then show that reallocating ads across users can enhance subscription profits by 7% without decreasing overall advertising profits. To achieve the same subscription rate with a uniform ad allocation policy, the firm would need to increase ad load by more than 22%. Our results show that, on average, consumer welfare drops by 2% with the proposed personalization strategy, and the effect seems to be more pronounced for users who have a higher willingness to pay.

Keywords: advertising; personalization; field experiments; heterogeneous treatment effects; machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:44:y:2025:i:2:p:327-352

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