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Dealing with Interdependencies and Uncertainty in Multi-Channel Advertising Campaigns Optimization

Published: 13 May 2019 Publication History

Abstract

In 2017, Internet ad spending reached 209 billion USD worldwide, while, e.g., TV ads brought in 178 billion USD. An Internet advertising campaign includes up to thousands of sub-campaigns on multiple channels, e.g., search, social, display, whose parameters (bid and daily budget) need to be optimized every day, subject to a (cumulative) budget constraint. Such a process is often unaffordable for humans and its automation is crucial. As also shown by marketing funnel models, the sub-campaigns are usually interdependent, e.g., display ads induce awareness, increasing the number of impressions-and, thus, also the number of conversions-of search ads. This interdependence is widely exploited by humans in the optimization process, whereas, to the best of our knowledge, no algorithm takes it into account. In this paper, we provide the first model capturing the sub-campaigns interdependence. We also provide the IDIL algorithm, which, employing Granger Causality and Gaussian Processes, learns from past data, and returns an optimal stationary bid/daily budget allocation. We prove theoretical guarantees on the loss of IDIL w.r.t. the clairvoyant solution, and we show empirical evidence of its superiority in both realistic and real-world settings when compared with existing approaches.

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  • (2024)Optimizing Real-Time Bidding Strategies: An Experimental Analysis of Reinforcement Learning and Machine Learning TechniquesProcedia Computer Science10.1016/j.procs.2024.04.191235(2017-2026)Online publication date: 2024
  • (2023)Improving Real-Time Bidding in Online Advertising using Markov Decision Processes and Machine Learning TechniquesInternational Journal of Advanced Engineering and Nano Technology10.35940/ijaent.F4231.071072310:7(1-8)Online publication date: 30-Jul-2023
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cover image ACM Other conferences
WWW '19: The World Wide Web Conference
May 2019
3620 pages
ISBN:9781450366748
DOI:10.1145/3308558
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 May 2019

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Author Tags

  1. Bid/Budget Optimization
  2. Granger Causality
  3. Internet Advertising

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WWW '19
WWW '19: The Web Conference
May 13 - 17, 2019
CA, San Francisco, USA

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Cited By

View all
  • (2024)Differential Evolution Framework for Budget Optimization in Marketing Models with Saturation and Adstock EffectsProcedia Computer Science10.1016/j.procs.2024.08.097242(520-527)Online publication date: 2024
  • (2024)Optimizing Real-Time Bidding Strategies: An Experimental Analysis of Reinforcement Learning and Machine Learning TechniquesProcedia Computer Science10.1016/j.procs.2024.04.191235(2017-2026)Online publication date: 2024
  • (2023)Improving Real-Time Bidding in Online Advertising using Markov Decision Processes and Machine Learning TechniquesInternational Journal of Advanced Engineering and Nano Technology10.35940/ijaent.F4231.071072310:7(1-8)Online publication date: 30-Jul-2023
  • (2023)Multichannel Advertising: Budget Allocation in the Presence of Spillover and Carryover EffectsSSRN Electronic Journal10.2139/ssrn.4461356Online publication date: 2023
  • (2020)Dynamic knapsack optimization towards efficient multi-channel sequential advertisingProceedings of the 37th International Conference on Machine Learning10.5555/3524938.3525318(4060-4070)Online publication date: 13-Jul-2020
  • (2020)Driving Exploration by Maximum Distribution in Gaussian Process BanditsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398872(948-956)Online publication date: 5-May-2020

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