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 have the bivariate Pearson correlation
Marketing Attribution
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
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
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: ▪ Make million dollar marketing decision on just correlation ▪ Specify Marketing Mix
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 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 (absolutely wrong way to go
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 given by this vendor was
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 not use experiments to calibrate
