Multi-Homing Alliances and Price Competition

By Abhinav Uppal, Nanda Kumar, Manish Gangwar
Journal of Marketing Research

DOI

doi.org/10.1177/00222437251381805

Citation

Uppal, Abhinav., Kumar, Nanda., Gangwar, Manish. (2025). Multi-Homing Alliances and Price Competition Journal of Marketing Research doi.org/10.1177/00222437251381805.

Journal of Marketing Research, 2025

Abstract

The OTT subscription video streaming industry has witnessed significant growth and heightened competition in recent years, marked by the influx of new players. At the same time, we are observing an interesting phenomenon where competing services are forming new alliances that facilitate consumer multi-homing. For instance, Amazon Prime Video has partnered with services, such as (HBO) MAX and Paramount+, to enhance the combined viewing experience for consumers through seamless integration. We build a game-theoretic model with horizontally differentiated services to examine how an alliance facilitating multi-homing between two competing services affects price competition in the market. We find that the alliance's impact on price competition depends on the level of content differentiation in the market -- competition intensifies when differentiation is high but relaxes when differentiation is low. The alliance benefits the partnering services as long as the differentiation is not too high, and interestingly, may increase the profitability of a third non-partnering service when differentiation is sufficiently low. We show that consumer surplus increases under the alliance, even if price competition is relaxed. We also investigate a focal service's decision to partner with one of two competing services and show that it prefers partnering with a service that has high quality content but a smaller loyal base. Our research offers insights into the current landscape of the OTT video streaming market and provides implications for both managers and policymakers.

Abhinav Uppal is an Assistant Professor of Marketing at the Indian School of Business. His research interests lie broadly in using microeconomic theory and game-theory based models to study topical problems in marketing. Currently, his work focuses on two main streams of research: the first is retailing, specifically how traditional retailers can counter modern threats; the second is competitive strategy and pricing, particularly how various market settings, structures, and strategic partnerships influence firms’ competitive behavior and marketing decisions. His research has been published in top marketing journals, including Marketing Science and the Journal of Marketing Research.

Professor Uppal received his PhD and MS in Marketing from the Wharton School, University of Pennsylvania. He has previously worked on algorithmic trading strategies in equity markets at Tower Research Capital and conducted research on technology for emerging markets at Microsoft Research India. He holds a BTech in Computer Science from the Indian Institute of Technology (IIT) Delhi and is a KVPY and NTSE scholar.

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Manish Gangwar is an Associate Professor of Marketing at the Indian School of Business (ISB). He is a distinguished faculty member and the Executive Director of the Institute of Data Science and Business Analytics at ISB. A leading academic in pricing and business analytics, he has previously served as Associate Dean of Research and RCI Management at ISB. Professor Gangwar holds a PhD in Management Science from the University of Texas at Dallas, an MS from the University of Kentucky, and a BE from the Indian Institute of Technology, Roorkee, complemented by years of industry experience.

Widely recognised as one of India’s foremost academicians in data science, Professor Gangwar has received several awards for his contributions to research and teaching. He serves on the editorial review boards of several leading academic journals. His expertise lies in advanced analytical methodologies, including machine learning, econometrics, data science, and game theory.

His research spans a wide range of topics, such as competitive promotions, omnichannel retail strategies, dynamic pricing, SaaS revenue models, and the application of AI in marketing. He has published extensively in top-tier academic journals such as Marketing Science, Journal of Marketing, Operations Research, Product and Operations Management, and the Journal of Retailing, as well as in book chapters and industry publications. For more information, please visit his Google Scholar Profile.

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