Skip to main content
Calibrating Marketing Mix Models
Lauri Potka avatar
Written by Lauri Potka
Updated over a week ago

πŸ”₯ Model calibration is a hot topic in Marketing Mix Modeling right now
​
Model calibration is a key step in the modeling workflow for ensuring the accuracy of ROI estimates.
​
​How does it work?
πŸ‘‰ In the Bayesian modeling approach, one can use "priors" to set assumptions for the model on the likely range where the ROI for a marketing activity lies.
πŸ‘‰ Instead of using non-informative priors, where the aim is not to guide the model, the calibration approach uses informative priors, i.e. it gives the model narrower range where to search the ROI from.
​
The main assumption in model calibration is, that you have strong evidence, which justifies creating priors with a narrow distribution and thus limiting the range of possible ROI estimates. This makes model calibration tricky - you need to be sure that you understand the biases and shortcomings in the calibration data that you use.
​
​We typically leverage four types of data in our calibration framework, when forming prior distributions for marketing activities:
​
1️⃣ Randomized control trials, e.g., Facebook Conversion Lift tests.
​
2️⃣ Geo tests: Experiments where control geos and test geos are compared against each other in an attempt to understand the effect of a change in marketing activity.
​
3️⃣ Shutdown tests: Experiments where an activity is stopped for a certain period of time, after which the ”on” and ”off” periods are compared.
​
4️⃣ Triangulation based on incrementality factor benchmarks for the same marketing activity in similar companies.

Did this answer your question?