Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
Pay-Per-Action Model for On-line Advertising
International Journal of Electronic Commerce
Evaluating online ad campaigns in a pipeline: causal models at scale
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring the effectiveness of display advertising: a time series approach
Proceedings of the 20th international conference companion on World wide web
Proceedings of the 20th international conference on World wide web
Time Series: Modeling, Computation, and Inference
Time Series: Modeling, Computation, and Inference
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In this paper, we develop a time series approach, based on Dynamic Linear Models (DLM), to estimate the impact of ad impressions on the daily number of commercial actions when no user tracking is possible. The proposed method uses aggregate data, and hence it is simple to implement without expensive infrastructure. Specifically, we model the impact of daily number of ad impressions on daily number of commercial actions. We incorporate persistence of campaign effects on actions assuming a decay factor. We relax the assumption of a linear impact of ads on actions using the logtransformation. We also account for outliers with long-tailed distributions fitted and estimated automatically without a pre-defined threshold. This is applied to observational data post-campaign and does not require an experimental set-up. We apply the method to data from the Advertising.com ad network on 2,885 campaigns for 1,251 products during six months, to calibrate and perform model selection. We set up a randomized experiment for two campaigns where user tracking is feasible. We find that the output of the proposed method is consistent with the results of A/B testing with similar confidence intervals. Finally, we validate our model with a synthetic public data set, PROMO, and identify 84% of effective campaigns correctly.