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Optimal bidding strategies for digital advertising

Author

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  • Médéric Motte

    (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Huyên Pham

    (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

Abstract

With the emergence of new online channels and information technology, digital advertising tends to substitute more and more to traditional advertising by offering the opportunity to companies to target the consumers/users that are really interested by their products or services. We introduce a novel framework for the study of optimal bidding strategies associated to different types of advertising, namely, commercial advertising for triggering purchases or subscriptions, and social marketing for alerting population about unhealthy behaviours (anti-drug, vaccination, road-safety campaigns). Our continuoustime models are based on a common framework encoding users online behaviours via their web-browsing at random times, and the targeted advertising auction mechanism widely used on Internet, the objective being to efficiently diffuse advertising information by means of digital channels. Our main results are to provide semi-explicit formulas for the optimal value and bidding policy for each of these problems. We show some sensitivity properties of the solution with respect to model parameters, and analyse how the different sources of digital information accessible to users including the social interactions affect the optimal bid for advertising auctions. We also study how to efficiently combine targeted advertising and non-targeted advertising mechanisms. Finally, some classes of examples with fully explicit formulas are derived.

Suggested Citation

  • Médéric Motte & Huyên Pham, 2021. "Optimal bidding strategies for digital advertising," Working Papers hal-03429785, HAL.
  • Handle: RePEc:hal:wpaper:hal-03429785
    Note: View the original document on HAL open archive server: https://hal.science/hal-03429785
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    References listed on IDEAS

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    More about this item

    Keywords

    Bid optimisation; auction; targeted advertising; digital information; Point processes; martingale techniques JEL Classification: C70; C61 MSC Classification: 91B26; 90B60; 60G55;
    All these keywords.

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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