RCT in MMM violates Principle of Marginality
ICYMI (link in resources), in my previous posts I had highlighted why one can’t RCT MMM. I will just recap
Blogs
What Marketing Mix Modeling domain can learn from Biostatistics
As a statistician, it pains me to see marketers do the following: ▪ Make million dollar marketing decision on just
Marketing Mix Modeling (MMM) Calibration Experiments
Experimentation has become a buzz word in MMM. Rightly so. Experimentation like Difference in Difference (DID) can help one
Maximum Likelihood Estimation and Bootstrapping – The Truth Whisperers in Marketing Mix Modeling (MMM)
The MMM model speaks but the problem is we often don’t listen. Much like a poorly tuned guitar produces noise
You can’t RCT Marketing Mix Models (MMMs)
I keep stumbling upon articles and posts where people talk about using Randomized control trial tests (RCTs) to calibrate MMM
Why you shouldn’t use media ROI benchmarks to set the priors
We are currently in talks with a company to replace their existing MMM vendor. The company realized that the estimates
Innovating with Robyn
At Aryma Labs we constantly try to push the frontier of what’s possible in Marketing Measurement and Attribution. In this
Why Difference in Difference (DID) Experimentation is the ideal way to validate your MMM model.
In my last few posts, I touched upon the following points (link to all in comments): – Why you should
Which technique provides for great manipulation in MMM – Bayesian or Frequentist?
Hands down Bayesian. Ask how? Through priors and posterior distribution. It is well known fact that one of the Achilles
Use Experimentation to validate your MMM models, not calibrate it.
I come across a lot of literature and talks on the internet that one should or can calibrate their MMM
