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 »
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 is calibration and validation. Calibration is a process where you try to improve the model fit by tweaking various knobs and levers. There are metrics that tell you how well
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 that will provide a perfect segue for the topic that we are going to cover today. ◾ Marketing Reality is complex and RCT can’t control for all variables When we
RCT in MMM violates Principle of Marginality Read More »
As a statistician, it pains me to see marketers do the following: ▪ Make million dollar marketing decision on just correlation ▪ Specify Marketing Mix Models (MMM) without any statistical rigor ▪ Think that MMM can be specified without controlling for all factors ▪ Think confidence interval from disparate models and experiments can be compared
What Marketing Mix Modeling domain can learn from Biostatistics Read More »
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 about things – see post link in resources) or to validate the MMM models. In this post we will focus on why one can’t use RCTs to validate the MMM
You can’t RCT Marketing Mix Models (MMMs) Read More »
In my last few posts, I touched upon the following points (link to all in comments): – Why you should not use experiments to calibrate your MMM model. – Why you can’t use Geo experiments to fix priors of your MMM model. – Why you should instead use experiments to only validate your MMM model.
So a lot of myths have been propagated by digital marketers of late, such as MMMs should be adopted only if : – Companies have revenue of $50M+ yearly. – they spend a lot on non click media (tv, radio etc.) – you have 5 or more channels. I have been studying and practicing statistics
Why Experimentation is not a substitute for Marketing Mix Modeling (MMM) Read More »
I know some of you must be wondering what is β-hat and Y-hat. So lets start this post with a few explainers. 📌 β-hat : β – hat problems are inference focused. We care about what variables go into the model. What are the parameter values and How much each of the Independent variables affect
Why a β-hat outlook is more beneficial than Y-hat in Marketing Mix Modeling (MMM) Read More »
Are you using Geo tests to fix priors in Bayesian MMM? You might want to rethink that. Priors are subjective in nature, making it difficult to get a consensus on what to set, especially in a domain like MMM where you have multiple stakeholders. While some MMM vendors try to add objectivity by using Geo
Why you shouldn’t use Geo tests to fix priors in Bayesian MMM Read More »
Did you know MMM has inbuilt Incrementality testing? Let me elaborate. Incrementality is defined as the additional impact of a marketing on the KPI (sales, TOMA, CAC etc..) over and above what would have been generated organically. Recently, we had an interesting conversation with a brand that ventured into TV ads since past one year.
What? MMM has inbuilt Incrementality testing? Read More »
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