Analysis on the in-app advertising contract of a mobile platform and two competitive app developers

Wei Hu; Xiao-chen Sun; Wan-feng Zhou

doi:10.3934/jimo.2022220

Article Contents

2023, Volume 19, Issue 9: 6398-6429. Doi: 10.3934/jimo.2022220

This issue Previous Article A new class of variational-like inclusion problems: Algorithmic and analytical approach Next Article Two-stage distributionally robust noncooperative games: Existence of Nash equilibrium and its application to Cournot–Nash competition

Analysis on the in-app advertising contract of a mobile platform and two competitive app developers

1.
School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
2.
College of Management and Economics, Tianjin University, Tianjin 300072, China
3.
School of Mathematical Sciences, Nankai University, Tianjin 300071, China

^*Corresponding author: Xiao-chen Sun
^*Corresponding author: Xiao-chen Sun

Received: July 2022

Revised: October 2022

Early access: November 2022

Published: September 2023

The second author is supported by NSFC grant 72132007

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Abstract

Moblie platform is an online store for distributing mobile apps and designed to connect mobile app developers and users. Mobile platform launches in-app advertisement for their advertisers to monetize the mobile app market. Unlike traditional advertising, the profitability of in-app advertising is not only affected by the decision of the platform, but also by the decision of the app developer, and they will share the advertising revenue. We develop an app advertising revenue sharing model with two competitive app developers and one mobile platform. The mobile platform is the leader, and the two app developers are the follower. Our analysis shows that the two app developers may choose to publish or not publish adds. If publishing, app developers may choose to offer a free app with in-app ads, or choose to offer a paid app with in-app ads. These results can be categorized into seven ranges for the platform's given revenue share of advertisement. In addition, we find the platform's optimal choice of ad revenue share is affected by the quality of advertisement. With the improvement of advertising quality, the revenue sharing ratio will first remain unchanged, then decrease, and finally increase. This is caused by the platform's subsidization strategy for app developers.

Keywords:

Mathematics Subject Classification: Primary: 91B41, 91B06; Secondary: 91A06.

Citation:

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Figure 1. Market Structure

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Figure 2. Seven decision intervals for the platform

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Figure 3. The trend of $ r_{AX} $ about $ V_A $

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Table 1. Summary of Notation

Decision variables
$ r_{AX} $	Platform P's ad revenue-sharing percentage with X
$ r_{AY} $	Platform P's ad revenue-sharing percentage with Y
$ P_X $	X's App price
$ P_Y $	Y's App price
Other variables
$ n_X $	App demand of X (number of participating app X users)
$ n_Y $	App demand of Y (number of participating app Y users)
$ n_{AX} $	Ad demand in app X
$ n_{AY} $	Ad demand in app Y
$ \pi_P $	Platform owner's profit
$ \pi_X $	App X developer's profit
$ \pi_Y $	App Y developer's profit
$ \pi_{AX} $	Advertiser's profit by placing advertisements in app X
$ \pi_{AY} $	Advertiser's profit by placing advertisements in app Y
Parameters
$ P_{AX} $	X's ad price
$ P_{AY} $	Y's ad price
$ r_{SX} $	Platform P's app sales revenue-sharing percentage with X
$ r_{SY} $	Platform P's app sales revenue-sharing percentage with Y
$ v_X $	App user's valuation for the app of X
$ v_Y $	App user's valuation for the app of Y
$ m $	Total number of potential advertisers
$ \beta $	Probability of each ad in the app appearing in the ad slot
$ \gamma $	Nuisance cost when the ad space in the app is filled with an ad
$ v_A $	An advertiser's valuation for advertising
$ V_A $	Maximum advertiser's valuation for advertising

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References

[1]	J. Aimonetti, Apple increases developer iAd revenue to 70 percent, CNET, 2012. Available from: https://www.cnet.com/tech/mobile/apple-increases-developer-iad-revenue-to-70-percent/.
[2]	S. P. Anderson and S. Coate, Market provision of broadcasting: A welfare analysis, Rev. Econ. Stud., 72 (2005), 947-972. doi: 10.1111/0034-6527.00357.
[3]	S. P. Anderson and J. Gabszewicz, The media and advertising: A tale of two-sided markets, in Handbook of the Economics of Art and Cluture, Elservier Press, 2006,567-614.
[4]	K. Asdemir, N. Kumar and V. S. Jacob, Pricing models for online advertising: CPM vs. CPC., Info. Syst. Res., 23 (2012), 804-822.
[5]	K. Bagwell, The economic analysis of advertising, in Handbook of Industrial Organization, Elservier Press, 2007, 1701-1844.
[6]	R. Casadesus-Masanell and F. Zhu, Strategies to fight ad-sponsored rivals, Manag. Sci., 56 (2010), 1484-1499.
[7]	R. Casadesus-Masanell and F. Zhu, Business model innovation and competitive imitation: The case of sponsor-based business models, Strateg. Manag. J., 34 (2013), 464-482. doi: 10.1002/smj.2022.
[8]	J. Chen and J. Stallaert, An economic analysis of online advertising using behavioral targeting, Manag. Inf. Syst. Q., 38 (2014), 429-449.
[9]	Y. W. Chen, J. Ni and D. G. Yu, Application developers product offering strategies in multi-platform markets, Eur. J. Oper. Res., 273 (2019), 320-333.
[10]	D. Godes, E. Ofek and M. Sarvaru, Content vs. advertising: The impact of competition on media firm strategy, Mark. Sci., 28 (2009), 20-35.
[11]	A. Ghose and S. Han, Estimating demand for mobile applications in the new economy, Manag. Sci., 60 (2014), 1470-1488.
[12]	L. Hao and M. Fan, An analysis of pricing models in the electronic book market, MIS Quarterly, 38 (2014), 1017-1032.
[13]	L. Hao, H. Guo and R. Easley, A mobile platform's in-app advertising contract under agency pricing for app sales, Prod. Oper. Manag., 26 (2017), 189-202.
[14]	Y. Hu, J. Shin and Z. Tang, Performance-based pricing models in online advertising: Cost per click versus cost per action, Manag. Sci., 62 (2015), 2022-2038.
[15]	A. Inc, Apple to debut iAds on July 1, Apple Press Info, 2010. Available from: https://www.apple.com/hk/en/newsroom/2010/06/07Apple-to-Debut-iAds-on-July-1/.
[16]	Y. Ji, R. Wang and Q. Gou, Monetization on mobile platform: Balancing in-app advertising and user base growth, Prod. Oper. Manag., 28 (2019), 2202-2220.
[17]	S. Kumar and S. P. Sethi, Dynamic pricing and advertising for web content providers, Eur. J. Oper. Res., 197 (2009), 924-944. doi: 10.1016/j.ejor.2007.12.038.
[18]	G. Lata and D. Darshna, Mobile app monetization: Issues and challenges, Inter. J. of Engin. Tech., Manag. and Sci., 3 (2015), 13-15.
[19]	X. Li, B. Gu and H. Liu, Price dispersion and loss leader pricing: Evidence from the online book industry, Manag. Sci., 59 (2013), 1290-1308.
[20]	X. Li, Y. Li and W. Gao, Cooperative advertising models in O2O supply chains, Int. J. Prod. Econ., 215 (2019), 144-152.
[21]	M. Lin, X. Ke and A. B. Whinston, Vertical differentitation and a comparison of online advertising models, J. Manag. Info. Syst., 29 (2012), 195-236.
[22]	D. Liu, S. Kumar and V. S. Moolerjee, Advertising strategies in electronic retailing: A differential games approach, Info. Syst. Res., 23 (2012), 903-917.
[23]	D. Liu and S. Viswanathan, Information asymmetry and hybrid advertising, J. Mak. Res., 51 (2012), 609-624.
[24]	J. Rochet and J. Tirole, Two-sided markets: A progress report, RAND J. Econ., 37 (2006), 645-667.
[25]	S. Ruutu, T. Casey and V. Kotovirta, Development and competition of digital service platforms: A system dynamics approach, Technol Forecast Soc Change., 117 (2017), 119-130.
[26]	M. Rsyman, The economics of two-sided markets, J. Econ. Perspect., 23 (2009), 125-143.
[27]	M. Sun and F. Zhu, Ad revenue and content commercialization: Evidence from blogs, Manag. Sci., 59 (2013), 2314-2331.
[28]	Y. Tan and V. S. Mookerjee, Allocating spending between advertising and information technology in electronic retailing, Manag. Sci., 51 (2005), 1236-1249.
[29]	R. Wang, Q. Guo, T. M. Choi and L. Liang, Advertising strategies for mobile platforms with "Apps", IEEE Trans. Syst., Man, Cybernet.: Syst., 48 (2018), 767-778.