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 calibration methods. But MMM is not just linear regression (see link in resources). It has more bells and whistles. As a result, it needs better calibration techniques. In our MMM […]
Chebyshev’s Inequality for Marketing Mix Model Diagnostics Read More ยป
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 RSSD is a good metric but at the same time it also runs the risk of confining the user to historical spends as yardstick. Given this limitation, the question arises
How to use Robyn’s Decomp RSSD Metric Effectively Read More ยป
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 shelf. Why? Because of Flintstones curse (see Ridhima’s post – link in resources). But that does not mean we can’t appreciate some innovative methods in these open source MMM tools.
Similarities between Decomp RSSD and Bayesian Priors in Marketing Mix Modeling (MMM) Read More ยป
Is there one true Marketing Mix Model (MMM) model ? Short answer: Yes “All models are wrong, some are useful” I have seen various vendors use the above statement to tell client that “there could be many MMM models and no one model is absolutely right”. They thus push the decision to pick a model
Is there one true Marketing Mix Model (MMM) model ? Read More ยป
After a lot of hard work you have built a great MMM model. The model is now powering saturation / reach frequency curves, scenario planning and Budget optimization. The client is happy using all of these. But the client asks you the following question – “How long can I continue to use the MMM models,
Assessing Marketing Mix Model (MMM) relevancy through Population Stability Index (PSI) Read More ยป
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 our recent client projects, we have been using KL Divergence as calibration metric over and above the other calibration metrics like R Squared value, Standard Error, P value, within sample
KL Divergence as MMM Calibration Metric Read More ยป
In MMM, there is often a dilemma on whether to make model better at explanation or prediction. Some MMM vendors focus on prediction while compromising on explanation. But is it the correct approach? No. At Aryma Labs, we err on the side of caution and focus more on getting the explanation right first. ๐๐ก๐ ๐๐ข๐๐ฌ
Marketing Mix Modeling’s Predict or Explain Dilemma Read More ยป
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 the background, let me explain what is AIC and KL Divergence ๐๐ค๐๐ข๐ค๐ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐ซ๐ข๐ญ๐๐ซ๐ข๐จ๐ง (๐๐๐) AIC = 2k -2ln(L) where k is the number of parameters L is the likelihood
How we use AIC and KL Divergence in our MMM models Read More ยป
In MMM, everybody talks about incrementality. How to discern the incremental sales (or any KPI). But what one should also question is – how much incremental insights one is getting out of MMM? Has the MMM exercise informed you something extra that you did not know already? We recently had a conversation with one client
Bayesian MMM’s Stating the obvious problem Read More ยป
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 mind. What happens in MMM is much more than just bivariate correlation. Multi linear regression like MMM are technically conditional correlations. Correlations can be useful in limited scope but
Why Aryma Labs does not rely on correlation alone Read More ยป
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