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You must have clicked on this ad by mistake! Data-driven identification of accidental clicks on mobile ads with applications to advertiser cost discounting and click-through rate prediction

  • Gabriele TolomeiEmail author
  • Mounia Lalmas
  • Ayman Farahat
  • Andrew Haines
Regular Paper

Abstract

In the cost per click pricing model, an advertiser pays an ad network only when a user clicks on an ad; in turn, the ad network gives a share of that revenue to the publisher where the ad was impressed. Still, advertisers may be unsatisfied with ad networks charging them for “valueless” clicks, or so-called accidental clicks. These happen when users click on an ad, are redirected to the advertiser website and bounce back without spending any time on the ad landing page. Charging advertisers for such clicks is detrimental in the long term as the advertiser may decide to run their campaigns on other ad networks. In addition, machine-learned click models trained to predict which ad will bring the highest revenue may overestimate an ad click-through rate, and as a consequence negatively impacting revenue for both the ad network and the publisher. In this work, we propose a data-driven method to detect accidental clicks from the perspective of the ad network. We collect observations of time spent by users on a large set of ad landing pages—i.e., dwell time. We notice that the majority of per-ad distributions of dwell time fit to a mixture of distributions, where each component may correspond to a particular type of clicks, the first one being accidental. We then estimate dwell time thresholds of accidental clicks from that component. Using our method to identify accidental clicks, we then propose a technique that smoothly discounts the advertiser’s cost of accidental clicks at billing time. Experiments conducted on a large dataset of ads served on Yahoo mobile apps confirm that our thresholds are stable over time, and revenue loss in the short term is marginal. We also compare the performance of an existing machine-learned click model trained on all ad clicks with that of the same model trained only on non-accidental clicks. There, we observe an increase in both ad click-through rate (+ 3.9%) and revenue (+ 0.2%) on ads served by the Yahoo Gemini network when using the latter. These two applications validate the need to consider accidental clicks for both billing advertisers and training ad click models.

Keywords

Accidental ad clicks Online mobile advertising Dwell time Mixture of distributions Ad cost discounting Click-through rate prediction 

Notes

Acknowledgements

The authors would like to thank Michal Aharon and Marc Bron for their support in setting up the online A/B test, which allowed them to deploy and assess their approach on a second use case, i.e., the ad click model.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of PaduaPaduaItaly
  2. 2.SpotifyLondonUK
  3. 3.AmazonPalo AltoUSA
  4. 4.Yahoo Research @ OathLondonUK

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