Yueqing Wang
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Bias Correction For Paid Search In Media Mix Modeling
Jim Koehler
Mike Perry
research.google.com, Google Inc. (2018)
Preview abstract
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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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)
Preview abstract
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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Preview abstract
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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Preview abstract
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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