Unified Marketing Measurement: How to fuse experimental data with marketing mix data?

Music City Center 

Digital marketers are struggling to measure campaign effectiveness due to the loss of customer-level tracking, rendering multi-touch attribution models obsolete. Moreover, constantly running experiments may be a costly alternative if effectiveness changes over time. As a consequence, firms have turned to using classic measurement tools like media mix models, which have always been built on potentially endogenous aggregate measures of campaign spend and performance.

We propose a Unified Marketing Measurement (UMM) framework that allows us to measure time-varying marketing effectiveness. Methodologically, we use a modern Bayesian nonparametrics framework that fuses the (available) experiments with aggregate media-mix data and leverages the exogenous variation in experiments to de-bias a media mix model. Using Gaussian Processes, our model regularizes ad effectiveness over time smoothly, allowing the experiments to separate marketing effectiveness from the correlation between sales and ad spending close to the experiment.

Our modeling framework also provides uncertainty quantification on ad effectiveness, which can be leveraged to determine if further experiments are needed. Using a series of simulations, we show the conditions for properly inferring ad effectiveness over time. We further show that endogeneity bias in observational data induces higher posterior uncertainty on the effectiveness and structural correlation estimates, which does not decrease with more observational data. This means we can use posterior uncertainty quantification to diagnose when additional experiments are needed.

Marketing Mix Models

Probabilistic Machine Learning

Bayesian nonparametrics

Experiments