Dynamic Bayesian Marketing Mix Modeling

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An advanced and adaptive approach to modeling 

The latest in a sequence of time series modeling advances, NextGen Marketing Mix Modeling integrates several advanced modeling techniques into one unified model estimation process. Specifically, Dynamic Linear Models combine with Hierarchical Bayesian and Vector Error Correction techniques to provide a complete model of short and long-term consumer demand.

Unifying these techniques into one estimation approach is exclusive to Marketscience.

This approach allows us to better estimate the increasingly complex consumer demand equation and therefore better measure the underlying economic theory that underpins all modeling approaches.

This approach overcomes several shortcomings of traditional Marketing Mix Modeling:

  • Provides a more realistic decomposition of consumer demand into short-term marketing elements and long-term baseline evolution - overcoming the fixed baseline approach of the standard mix model. 

  • A flexible dynamic lag structure approach allows the data to specify the advertising response and captures potential wear-in, decay and diminishing return effects.

  • Advanced VECM techniques combine long-term baseline evolution with consumer attitudinal and experience data to accurately quantify long-term brand-building media effects , 

  • More granular, high frequency data gives greater clarity on short-term effects, allowing you to better optimize media investments.

  • Higher-order pooling allows us to measure marketing effects at the appropriate level, thus overcoming aggregation bias. It also allows us to model at zip code and customer micro-segment level and therefore better align with test and control experimentation and programmatic buying approaches. 

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