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At Aryma Labs we constantly try to push the frontier of what’s possible in Marketing Measurement and Attribution. In this regard, we have been working on a very interesting problem for a long time. What is this problem? Well, basically your MMM model should not be just good at prediction but should also have good […]
Innovating with Robyn 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.
Hands down Bayesian. Ask how? Through priors and posterior distribution. It is well known fact that one of the Achilles heel of Bayesian technique is the subjective priors. In a small data (relatively speaking) problem like MMM, one never has enough data where the evidence in data could overwhelm the priors. This is hence a
Which technique provides for great manipulation in MMM – Bayesian or Frequentist? Read More »
I come across a lot of literature and talks on the internet that one should or can calibrate their MMM models through Experimentation. I disagree. Why? Because Calibration and Validation are entirely different things in statistics. ICYMI we wrote a detailed article on this subject (link in resources). But a TL;DR version is: Calibration is
Use Experimentation to validate your MMM models, not calibrate it. Read More »
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 »
One of the hallmarks of Frequentist philosophy is the adoption of Type 1 error rate control. Type 1 error is about false positives. In MMM, one has to be more wary about type 1 error than Type 2 errors. Why? 📌 Because the implication of falsely attributing a media/marketing channel for the change in KPI
Want performance guarantees ? choose Frequentist MMM Read More »
The word uncertainty means different things in Bayesian MMM vs Frequentist MMM. From a Frequentist perspective, the uncertainty is due sampling variability. That is, there is a true fixed parameter of the population. But given the sample at hand, you may or may not capture this true parameter. In MMM parlance, one could think of
Bayesian uncertainty ≠ Frequentist uncertainty Read More »
So Google just deprecated third party cookies for 1% of users worldwide. In nearly 270 days, third party cookies will be totally deprecated. The domino effect of this would be that Marketing Attribution will get more tougher. But Marketing Mix Modeling (MMM) is a good solution to fill the void. We are now seeing a
Adopting MMM for the first time ? Use Frequentist MMM Read More »
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