Geo-level Bayesian Hierarchical Media Mix Modeling
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
(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
Research Areas
