Assessing Marketing Mix Model (MMM) relevancy through Population Stability Index (PSI)
After a lot of hard work you have built a great MMM model. The model is now powering saturation /
Blogs
KL Divergence as MMM Calibration Metric
At Aryma Labs, we strive to incorporate information theoretic approach in our MMM modeling processes because correlation based approaches have
Marketing Mix Modeling’s Predict or Explain Dilemma
In MMM, there is often a dilemma on whether to make model better at explanation or prediction. Some MMM vendors
How we use AIC and KL Divergence in our MMM models
At Aryma Labs, we increasingly leverage information theoretic methods over correlational ones. ICYMI, link to the article on the same
Bayesian MMM’s Stating the obvious problem
In MMM, everybody talks about incrementality. How to discern the incremental sales (or any KPI). But what one should also
Why Aryma Labs does not rely on correlation alone
Many say MMMs are correlational in nature. Well statistically it is not completely true. When people say correlation they normally
MMMs are long memory models
In my previous posts, I had written about why one should not use Geo tests to fix priors in Bayesian
Calibration is fast, Validation is slow; Calibration does not require sacrifice, Validation requires sacrifice
I have written in my previous posts on why one can only validate (partially) MMMs and not calibrate it through
Bayesian MMM vs Frequentist MMM – Key Comparisons
One of the fundamental question that you as a client should be asking an MMM vendor is – “Which technique
How much data you need for MMM?
Technically the right answer is – It depends. But there are also a lot of heuristics and rule of thumb
