David X. Chan
Authored Publications
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We propose a number of potential approaches to enable Sales Lift measurement in a privacy-safe and secure manner. We discuss these approaches in the context of digital publisher media channels, but in theory these approaches can be extended to most types of media channels that has the ability to link media exposure with outcomes. We discuss Sales Lift measurement both in the context of single publisher and multi-publisher scenarios and weigh the trade-off of the different solutions in terms of utility, privacy, security, and computation costs.
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In this paper, we compare a variety of methods for causal inference through simulation, examining their
sensitivity to and asymptotic behavior in the presence of correlation between (heterogeneous) treatment
effect size and propensity to be treated, as well as their robustness to model mis-specification. We limit
our focus to well-established methods relevant to the estimation of sales lift, which initially motivated
this paper and serves as an illustrative example throughout. We demonstrate that popular matching
methods often fail to adequately debias lift estimates, and that even doubly robust estimators, when
naively implemented, fail to deliver statistically valid confidence intervals. The culprit is inadequate
standard error estimators, which often yield insufficient confidence interval coverage because they fail to
take into account uncertainty at early stages of the causal model. As an alternative, we discuss a more
reliable approach: the use of a doubly robust point estimator with a sandwich standard error estimator.
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Bias Correction For Paid Search In Media Mix Modeling
Jim Koehler
Mike Perry
research.google.com, Google Inc. (2018)
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Evaluating the return on ad spend (ROAS), the causal effect of advertising
on sales, is critical to advertisers for understanding the performance of their
existing marketing strategy as well as how to improve and optimize it. Media
Mix Modeling (MMM) has been used as a convenient analytical tool to address
the problem using observational data. However it is well recognized that MMM
suffers from various fundamental challenges: data collection, model specification
and selection bias due to ad targeting, among others (Chan & Perry 2017; Wolfe
2016).
In this paper, we study the challenge associated with measuring the impact
of search ads in MMM, namely the selection bias due to ad targeting. Using
causal diagrams of the search ad environment, we derive a statistically principled
method for bias correction based on the back-door criterion (Pearl 2013).
We use case studies to show that the method provides promising results by
comparison with results from randomized experiments. We also report a more
complex case study where the advertiser had spent on more than a dozen media
channels but results from a randomized experiment are not available. Both our
theory and empirical studies suggest that in some common, practical scenarios,
one may be able to obtain an approximately unbiased estimate of search ad
ROAS.
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Advertisers have a need to understand the effectiveness of their media spend in driving sales in order to optimize budget allocations. Media mix models are a common and widely used approach for doing so. The paper outlines the various challenges such models encounter in consistently providing valid answers to the advertiser’s questions on media effectiveness. The paper also discusses opportunities for improvements in media mix models that can produce better inference.
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Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects
Jim Koehler
research.google.com, Google Inc., 76 Ninth Avenue
Google New York
NY 10011 (2017)
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Media mix models are used by advertisers to measure the effectiveness of their advertising and provide insight in making future budget allocation decisions. Advertising usually has lag effects and diminishing returns, which are hard to capture using linear regression. In this paper, we propose a media mix model with flexible functional forms to model the carryover and shape effects of advertising. The model is estimated using a Bayesian approach in order to make use of prior knowledge accumulated in previous or related media mix models. We illustrate how to calculate attribution metrics such as ROAS and mROAS from posterior samples on simulated data sets. Simulation studies show that the model can be estimated very well for large size data sets, but prior distributions have a big impact on the posteriors when the sample size is small and may lead to biased estimates. We apply the model to data from a shampoo advertiser, and use Bayesian Information Criterion (BIC) to choose the appropriate specification of the functional forms for the carryover and shape effects. We further illustrate that the optimal media mix based on the model has a large variance due to the variance of the parameter estimates.
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One of the major problems in developing media mix models is that the data that is generally available to the modeler lacks sufficient quantity and information content to reliably estimate the parameters in a model of even moderate complexity. Pooling data from different brands within the same product category provides more observations and greater variability in media spend patterns. We either directly use the results from a hierarchical Bayesian model built on the category dataset, or pass the information learned from the category model to a brand-specific media mix model via informative priors within a Bayesian framework, depending on the data sharing restriction across brands. We demonstrate using both simulation and real case studies that our category analysis can improve parameter estimation and reduce uncertainty of model prediction and extrapolation.
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Media mix modeling is a statistical analysis on historical data to measure the return on investment
(ROI) on advertising and other marketing activities. Current practice usually utilizes data aggregated
at a national level, which often suffers from small sample size and insufficient variation in
the media spend. When sub-national data is available, we propose a geo-level Bayesian hierarchical
media mix model (GBHMMM), and demonstrate that the method generally provides estimates
with tighter credible intervals compared to a model with national level data alone. This reduction
in error is due to having more observations and useful variability in media spend, which can protect
advertisers from unsound reallocation decisions. Under some weak conditions, the geo-level model
can reduce the ad targeting bias. When geo-level data is not available for all the media channels,
the geo-level model estimates generally deteriorate as more media variables are imputed using the
national level data
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In an earlier study, we reported that on average 89% of the visits to the advertiser’s site from search ad clicks were incremental. In this research, we examine how the ranking of an advertiser’s organic listings on the search results page affects the incrementality of ad clicks expressed through Incremental Ad Clicks (IAC) and as estimated by Search Ads Pause models. A meta-analysis of 390 Search Ads Pause studies highlights the limited opportunity for clicks from organic search results to substitute for ad clicks when the ads are turned off. On average, 81% of ad impressions and 66% of ad clicks occur in the absence of an associated organic search result. We find that having an associated organic search result in rank one does not necessarily mean a low IAC. On average, 50% of the ad clicks that occur with a top rank organic result are incremental, compared to 100% of the ad clicks being incremental in the absence of an associated organic result.
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Incremental Clicks: The Impact of Search Advertising
Jim Koehler
Deepak Kumar
Journal of Advertising Research, 51, no. 4 (2011), pp. 643-647
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Advertisers often wonder whether search ads cannibalize their organic traffic. In other words, if search ads were paused, would clicks on organic results increase, and make up for the loss in paid traffic? Google statisticians recently ran over 400 studies on paused accounts to answer this question.
In what we call “Search Ads Pause Studies”, our group of researchers observed organic click volume in the absence of search ads. Then they built a statistical model to predict the click volume for given levels of ad spend using spend and organic impression volume as predictors. These models generated estimates for the incremental clicks attributable to search ads (IAC), or in other words, the percentage of paid clicks that are not made up for by organic clicks when search ads are paused.
The results were surprising. On average, the incremental ad clicks percentage across verticals is 89%. This means that a full 89% of the traffic generated by search ads is not replaced by organic clicks when ads are paused. This number was consistently high across verticals.
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