IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v31y2020i2p589-606.html
   My bibliography  Save this article

When to Play Your Advertisement? Optimal Insertion Policy of Behavioral Advertisement

Author

Listed:
  • Subodha Kumar

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122;)

  • Yinliang (Ricky) Tan

    (A. B. Freeman School of Business, Tulane University, New Orleans, Louisiana 70118;)

  • Lai Wei

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China)

Abstract

Digital advertisements offer a full spectrum of behavioral customization for timing and content capabilities. The existing research in display advertising has predominantly concentrated on the content of advertising; however, our focus is on optimizing the timing of display advertising. In practice, users are constantly adjusting their engagement with content as they process new information continuously. The recent development of emotional tracking and wearable technologies allows platforms to monitor the user’s engagement in real time. We model the user’s continuous engagement process through a Brownian motion. The proposed optimal policy regarding the timing of behavioral advertising is based on a threshold policy with a trigger threshold and target level. Specifically, the platform should insert the advertisement when the user’s engagement level reaches the trigger threshold, and the length of the advertisement should let the user’s engagement level drop to the target level. Analogous to the familiar idea of “price discrimination,” the methods we propose in this study allow the platforms to maximize their revenue by “discriminatory” customization of the timing and length of the advertisement based on the behavior of individual users. Finally, we quantify the benefits of the proposed policy by comparing it with the practically prevalent policies (i.e., preroll, midroll, and a mix of the two) through a simulation study. Our results reveal that, for a wide range of settings, the proposed policy not only significantly increases the platform’s profitability but also improves the completion rate at which consumers finish viewing the advertisement.

Suggested Citation

  • Subodha Kumar & Yinliang (Ricky) Tan & Lai Wei, 2020. "When to Play Your Advertisement? Optimal Insertion Policy of Behavioral Advertisement," Information Systems Research, INFORMS, vol. 31(2), pages 589-606, June.
    • Handle: RePEc:inm:orisre:v:31:y:2020:i:2:p:589-606
    DOI: 10.1287/isre.2019.0904
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/isre.2019.0904
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2019.0904?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    2. T. Tony Ke & Zuo-Jun Max Shen & J. Miguel Villas-Boas, 2016. "Search for Information on Multiple Products," Management Science, INFORMS, vol. 62(12), pages 3576-3603, December.
    3. Lin Hao & Hong Guo & Robert F. Easley, 2017. "A Mobile Platform's In‐App Advertising Contract Under Agency Pricing for App Sales," Production and Operations Management, Production and Operations Management Society, vol. 26(2), pages 189-202, February.
    4. Qiaowei Shen & J. Miguel Villas-Boas, 2018. "Behavior-Based Advertising," Management Science, INFORMS, vol. 64(5), pages 2047-2064, May.
    5. Kumar, Subodha & Sethi, Suresh P., 2009. "Dynamic pricing and advertising for web content providers," European Journal of Operational Research, Elsevier, vol. 197(3), pages 924-944, September.
    6. Michel Benaroch, 2018. "Real Options Models for Proactive Uncertainty-Reducing Mitigations and Applications in Cybersecurity Investment Decision Making," Information Systems Research, INFORMS, vol. 29(2), pages 315-340, June.
    7. J. Miguel Villas-Boas, 2018. "A Dynamic Model of Repositioning," Marketing Science, INFORMS, vol. 37(2), pages 279-293, March.
    8. Dengpan Liu & Subodha Kumar & Vijay S. Mookerjee, 2012. "Advertising Strategies in Electronic Retailing: A Differential Games Approach," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 903-917, September.
    9. Yong Tan & Vijay S. Mookerjee, 2005. "Allocating Spending Between Advertising and Information Technology in Electronic Retailing," Management Science, INFORMS, vol. 51(8), pages 1236-1249, August.
    10. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2017. "Optimizing Performance-Based Internet Advertisement Campaigns," Operations Research, INFORMS, vol. 65(1), pages 38-54, February.
    11. Yuxin Chen & Xinxin Li & Monic Sun, 2017. "Competitive Mobile Geo Targeting," Marketing Science, INFORMS, vol. 36(5), pages 666-682, September.
    12. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2016. "Too Much Information? Information Provision and Search Costs," Marketing Science, INFORMS, vol. 35(4), pages 605-618, July.
    13. Suvankar Ghosh & Xiaolin Li, 2013. "A Real Options Model for Generalized Meta-Staged Projects---Valuing the Migration to SOA," Information Systems Research, INFORMS, vol. 24(4), pages 1011-1027, December.
    14. Radha Mookerjee & Subodha Kumar & Vijay S. Mookerjee, 2017. "Optimizing Performance-Based Internet Advertisement Campaigns," Operations Research, INFORMS, vol. 65(1), pages 38-54, February.
    15. Zhen Sun & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2017. "Not Just a Fad: Optimal Sequencing in Mobile In-App Advertising," Information Systems Research, INFORMS, vol. 28(3), pages 511-528, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dominick Werner & Martin Adam & Alexander Benlian, 2022. "Empowering users to control ads and its effects on website stickiness," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1373-1397, September.
    2. 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.
    3. Hemant K. Bhargava, 2022. "The Creator Economy: Managing Ecosystem Supply, Revenue Sharing, and Platform Design," Management Science, INFORMS, vol. 68(7), pages 5233-5251, July.
    4. Li, Jingyan & Wu, Jie & Zhang, Juzhi & Ji, Xiang, 2024. "Freemium design: Optimal tier differentiation models for content platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    5. Manmohan Aseri & Amit Mehra & Vijay Mookerjee & Hong Xu, 2024. "Should an Ad Agency Offer Geoconquesting or Protection from It?," Information Systems Research, INFORMS, vol. 35(2), pages 850-870, June.
    6. Lijun Bo & Meng Li & Tingting Zhang, 2023. "Evaluation Timing with Dynamic Information: Optimization and Heuristic," Production and Operations Management, Production and Operations Management Society, vol. 32(12), pages 3931-3950, December.
    7. Haibing Gao & Subodha Kumar & Yinliang (Ricky) Tan & Huazhong Zhao, 2022. "Socialize More, Pay Less: Randomized Field Experiments on Social Pricing," Information Systems Research, INFORMS, vol. 33(3), pages 935-953, September.
    8. Zhou, Xiaoyang & Chen, Kexin & Wen, Haoyu & Lin, Jun & Zhang, Kai & Tian, Xin & Wang, Shouyang & Lev, Benjamin, 2021. "Integration of third-party platforms: Does it really hurt them?," International Journal of Production Economics, Elsevier, vol. 234(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. J. Miguel Villas-Boas & Yunfei (Jesse) Yao, 2021. "A Dynamic Model of Optimal Retargeting," Marketing Science, INFORMS, vol. 40(3), pages 428-458, May.
    3. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    4. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    5. Michael R. Galbreth & Bikram Ghosh, 2020. "The effect of exogenous product familiarity on endogenous consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 18(2), pages 195-235, June.
    6. Yi Zhu & Anthony Dukes, 2017. "Prominent Attributes Under Limited Attention," Marketing Science, INFORMS, vol. 36(5), pages 683-698, September.
    7. Manmohan Aseri & Amit Mehra & Vijay Mookerjee & Hong Xu, 2024. "Should an Ad Agency Offer Geoconquesting or Protection from It?," Information Systems Research, INFORMS, vol. 35(2), pages 850-870, June.
    8. Raluca M. Ursu & Daria Dzyabura, 2020. "Retailers’ product location problem with consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 18(2), pages 125-154, June.
    9. Haoyu Liu & Shulin Liu, 2019. "Considering In-App Advertising Mode, Platform-App Channel Coordination by a Sustainable Cooperative Advertising Mechanism," Sustainability, MDPI, vol. 12(1), pages 1-28, December.
    10. Zheng, Hong & Wu, Huamin & Tian, Lin, 2022. "Healthcare service enhancement with patient search," Journal of Business Research, Elsevier, vol. 152(C), pages 398-409.
    11. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    12. Pedro M. Gardete & Liang Guo, 2021. "Prepurchase Information Acquisition and Credible Advertising," Management Science, INFORMS, vol. 67(3), pages 1696-1717, March.
    13. Anthony Dukes & Lin Liu, 2016. "Online Shopping Intermediaries: The Strategic Design of Search Environments," Management Science, INFORMS, vol. 62(4), pages 1064-1077, April.
    14. Yuetao Gao & Yue Wu, 2023. "Regulating Probabilistic Selling of Counterfeits," Management Science, INFORMS, vol. 69(8), pages 4498-4517, August.
    15. Liang Guo, 2022. "Strategic Communication Before Price Haggling: A Tale of Two Orientations," Marketing Science, INFORMS, vol. 41(5), pages 922-940, September.
    16. Sameer Mehta & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2020. "Sustaining a Good Impression: Mechanisms for Selling Partitioned Impressions at Ad Exchanges," Information Systems Research, INFORMS, vol. 31(1), pages 126-147, March.
    17. Rafael P. Greminger, 2022. "Optimal Search and Discovery," Management Science, INFORMS, vol. 68(5), pages 3904-3924, May.
    18. Yuan Fang & Bin Shen & Yifan Cao, 2022. "To Share or Not to Share? The Optimal Technology Investment in a Virtual Product Supply Chain," Sustainability, MDPI, vol. 14(19), pages 1-30, October.
    19. Liu, Lin & Wang, X. Henry, 2023. "Partial sequential search and product differentiation," Economics Letters, Elsevier, vol. 225(C).
    20. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:31:y:2020:i:2:p:589-606. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.