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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

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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.

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Notes

  1. There is no consensus around what a conversion is; it is up to the advertiser to specify it.

  2. https://gemini.yahoo.com/.

  3. We refer to Normal and Gaussian distribution, interchangeably.

  4. In practice, we often seek for \(\hat{{\varvec{\Theta }}} _{\text {MLE}}\) so as to maximise the log-likelihood function\(\text {ln}(L(\varvec{\Theta };{\mathcal {D}}_j))\), since this is equivalent (the natural logarithm is monotonically increasing) but simpler because products change into summations.

  5. This is also often referred to as the bias-variance trade-off.

  6. A threshold already used in previous work [16].

  7. The actual revenue loss is not shown due to business confidentiality.

  8. In this setting, \(p=\text {NACR}\) and \({{\hat{p}}} = {\widehat{\text {NACR}}}_{\text {MLE}}\).

  9. This may happen if the same pivoting app has been running for long and a new, better performing app slightly overtakes it.

  10. CPM stands for cost per mille (impressions) and indicates the earnings gained every thousand ad impressions sold.

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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.

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Authors and Affiliations

  1. University of Padua, Padua, Italy

    Gabriele Tolomei

  2. Spotify, London, UK

    Mounia Lalmas

  3. Amazon, Palo Alto, CA, USA

    Ayman Farahat

  4. Yahoo Research @ Oath, London, UK

    Andrew Haines

Authors
  1. Gabriele Tolomei
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  2. Mounia Lalmas
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  3. Ayman Farahat
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  4. Andrew Haines
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Correspondence to Gabriele Tolomei.

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The authors declared that they have no conflict of interest.

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All the authors contributed to this work while employed at Yahoo Research.

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Tolomei, G., Lalmas, M., Farahat, A. et al. 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. Int J Data Sci Anal 7, 53–66 (2019). https://doi.org/10.1007/s41060-018-0122-1

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