I have written in my previous posts on why one can only validate (partially) MMMs and not calibrate it through experiments. Let’s quickly recap what
One of the fundamental question that you as a client should be asking an MMM vendor is – “Which technique do you employ to build
Technically the right answer is – It depends. But there are also a lot of heuristics and rule of thumb on how much data is
ICYMI (link in resources), in my previous posts I had highlighted why one can’t RCT MMM. I will just recap two points from that post
As a statistician, it pains me to see marketers do the following: ▪ Make million dollar marketing decision on just correlation ▪ Specify Marketing Mix
Experimentation has become a buzz word in MMM. Rightly so. Experimentation like Difference in Difference (DID) can help one to holistically prove the efficacy
The MMM model speaks but the problem is we often don’t listen. Much like a poorly tuned guitar produces noise rather than good notes, your
I keep stumbling upon articles and posts where people talk about using Randomized control trial tests (RCTs) to calibrate MMM (absolutely wrong way to go
We are currently in talks with a company to replace their existing MMM vendor. The company realized that the estimates given by this vendor was
At Aryma Labs we constantly try to push the frontier of what’s possible in Marketing Measurement and Attribution. In this regard, we have been working
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