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Dissertation/Thesis Abstract

Personalized Versioning: Product Strategies Constructed from Experiments on Pandora
by Goli, Ali, Ph.D., The University of Chicago, 2020, 62; 28025298
Abstract (Summary)

The role of advertising as an "implicit price" has long been recognized by economists and marketers. However, the impact of personalizing implicit prices on firm profits and consumer welfare has not been studied. We first conduct a set of large-scale field experiments on Pandora by exogenously shifting the ad load, that is the implicit price, for ad-supported users. We then use a state-of-the-art machine-learning model to examine the heterogeneous treatment effects of firm's interventions on listeners behavior, both in terms of listening hours and in terms of the propensity to subscribe to the ad-free version of the product. We next show that by reallocating ads across individuals, the firm can improve subscription profits by 10% without reducing total profits generated from advertising. To achieve the same subscription rate using a uniform ad-allocation policy, the firm would need to increase the number of ads served on the platform by more than 30%. Furthermore, the gains from personalization emerge quickly after implementation, as subscription behavior adapts to changing ad load relatively quickly. We also evaluate the welfare implications of personalizing implicit prices. 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 that have a higher willingness to pay.

Indexing (document details)
Advisor: Chintagunta, Pradeep K., Dubé, Jean-Pierre
Commitee: Hitsch, ‪Günter, Rao, Anita
School: The University of Chicago
Department: Business
School Location: United States -- Illinois
Source: DAI-A 82/3(E), Dissertation Abstracts International
Subjects: Marketing, Artificial intelligence, Finance
Keywords: Advertising, Field experiment, Heterogenous treatment effect, Machine learning, Pricing, Versioning
Publication Number: 28025298
ISBN: 9798672162621
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