Chebyshev’s Inequality for Marketing Mix Model Diagnostics
At Aryma Labs, we constantly endeavor to add as much science as possible to marketing. MMM model calibration historically has had parallels with multi-linear regression
At Aryma Labs, we constantly endeavor to add as much science as possible to marketing. MMM model calibration historically has had parallels with multi-linear regression
In my last post (ICYMI link in resources), I talked about the similarities and differences between Robyn’s Business metric Decomp RSSD and Bayesian Priors. Decomp
Open source Marketing Mix Modeling (MMM) tools are great for democratizing MMM. But they will never provide you with an accurate MMM model off the
Is there one true Marketing Mix Model (MMM) model ? Short answer: Yes “All models are wrong, some are useful” I have seen various vendors
After a lot of hard work you have built a great MMM model. The model is now powering saturation / reach frequency curves, scenario planning
At Aryma Labs, we strive to incorporate information theoretic approach in our MMM modeling processes because correlation based approaches have their limitations. Of late, in
At Aryma Labs, we increasingly leverage information theoretic methods over correlational ones. ICYMI, link to the article on the same is in resources. To set
In MMM, everybody talks about incrementality. How to discern the incremental sales (or any KPI). But what one should also question is – how much
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
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
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