Which technique provides for great manipulation in MMM – Bayesian or Frequentist?
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
Marketing Attribution
Use Experimentation to validate your MMM models, not calibrate it.
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 a β-hat outlook is more beneficial than Y-hat in Marketing Mix Modeling (MMM)
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 :
Why you shouldn’t use Geo tests to fix priors in Bayesian MMM
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
Want performance guarantees ? choose Frequentist MMM
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,
Adopting MMM for the first time ? Use Frequentist MMM
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
Saas-ification will not fix the core problems in MMM
I increasingly see that more is being talked about ‘How MMM can be done in 1 day’ rather than how the problems of multicollinearity and
Quantum of Marketing Spend vs Pattern of Marketing Spend
In every Marketing Mix Modeling (MMM) project that we undertake, we always study the previous spend pattern of the client. Why? Because how much one
Using Transfer Entropy for Feature Selection in MMM
Few months back we published our whitepaper ‘Granger Causality – A possible Feature Selection Method in MMM’. ICYMI the link is in the resources section.
Why I am bullish on Marketing Mix Modeling MMM
In late 2018, We explored the option of VC funding. Some of the VCs asked us why we focused on Marketing Mix Modeling solutions. We
