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Economic Analysis of Reward Advertising

Author

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  • Hong Guo
  • Xuying Zhao
  • Lin Hao
  • De Liu

Abstract

Reward advertising is an emerging monetization mechanism for app developers in which consumers choose to view ads in exchange for apps’ premium content. We provide the first economic analysis of reward advertising by studying the implications of offering reward ads, either by itself, or in conjunction with direct selling of premium content. We find that the condition for offering reward ads is surprisingly simple, and it is often optimal to offer reward ads jointly with direct selling of premium content. Interestingly, a high reward rate could decrease the number of reward ads viewed because of accelerated satiation for premium content; thus, developers need to balance the need to incentivize ad viewing and to prevent excessive accelerated satiation. The need for limiting the number of reward ads per consumer only arises when the marginal revenue of reward ads diminishes quickly. Such limit is only effective when the base ad revenue rate is not too high and when ad viewers have relatively homogenous nuisance costs. Finally, reward ads may increase or decrease consumer surplus.

Suggested Citation

  • Hong Guo & Xuying Zhao & Lin Hao & De Liu, 2019. "Economic Analysis of Reward Advertising," Production and Operations Management, Production and Operations Management Society, vol. 28(10), pages 2413-2430, October.
    • Handle: RePEc:bla:popmgt:v:28:y:2019:i:10:p:2413-2430
    DOI: 10.1111/poms.13015
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    Cited by:

    1. Chutian Wang & Bo Zhou & Yogesh V. Joshi, 2024. "Endogenous Consumption and Metered Paywalls," Marketing Science, INFORMS, vol. 43(1), pages 158-177, January.
    2. Abhijeet Ghoshal & Radha Mookerjee & Zhen Sun, 2023. "Serving two masters? Optimizing mobile ad contracts with heterogeneous advertisers," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 618-636, February.
    3. Chernonog, Tatyana & Levy, Priel, 2023. "Co-creation of mobile app quality in a two-platform supply chain when platforms are asymmetric," European Journal of Operational Research, Elsevier, vol. 308(1), pages 183-200.
    4. Subodha Kumar & Xiaowei Mei & Liangfei Qiu & Lai Wei, 2020. "Watching Ads for Free Mobile Data: A Game-Theoretic Analysis of Sponsored Data with Reward Task," Working Papers 20-08, NET Institute.
    5. Xiaowei Mei & Hsing Kenneth Cheng & Subhajyoti Bandyopadhyay & Liangfei Qiu & Lai Wei, 2022. "Sponsored Data: Smarter Data Pricing with Incomplete Information," Information Systems Research, INFORMS, vol. 33(1), pages 362-382, March.
    6. Haoyu Liu & Shulin Liu, 2020. "Research on Advertising and Quality of Paid Apps, Considering the Effects of Reference Price and Goodwill," Mathematics, MDPI, vol. 8(5), pages 1-23, May.
    7. Xingyue Zhang & Yuliang Yao, 2020. "How Much is Too Much? The Effect of Offline Call Intensity on Online Purchase of Digital Services," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 509-525, March.
    8. Gene Moo Lee & Shu He & Joowon Lee & Andrew B. Whinston, 2020. "Matching Mobile Applications for Cross-Promotion," Information Systems Research, INFORMS, vol. 31(3), pages 865-891, September.

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