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 MMM. ICYMI the link is
Marketing Mix Modeling
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 experiments. Let’s quickly recap what
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 do you employ to build
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 on how much data is
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 two points from that post
What Marketing Mix Modeling domain can learn from Biostatistics
As a statistician, it pains me to see marketers do the following: โช Make million dollar marketing decision on just correlation โช Specify Marketing Mix
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 to holistically prove the efficacy
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 rather than good notes, your
You canโt 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 (absolutely wrong way to go
Why you shouldnโt 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 given by this vendor was
