When we talk about application of statistics during world war 2, somehow the image of the airplane with red dots (survivorship bias) comes to mind.
Are you confused about the meaning of ‘statistic’ in statistics? You’re not alone. Many blogs and posts on the internet use the term loosely. In
A lot of people blame Confidence Intervals and its ‘unintuitive’ nature for switching to Bayesian side of things. But if you are in Marketing Mix
One key advice I give to statisticians/data scientists looking to get started on Marketing Mix Modeling (MMM) is – Know your type of Data. A
Why customers should not expect their Marketing Mix Models (MMM) to have very high R squared value. Somehow over the years, two myth has been
1. Spends are more actionable and transactional, as you can measure direct impact on sales. Spends data directly reflects the financial investment in marketing (your
Marketers are sometimes given bad advice that they should not go for advanced methods for marketing impact measurements. Instead they are suggested to adopt simple
One of the complaints many have with respect to MMM is that it does not provide granular insights. While there are multiple solutions to this
I often come across posts where people overtly prescribe out of sample error and incrementality testing as the only way to validate the MMM model.
In MMM, You don’t need to train/test split your data. Okay, some of you might be shocked since I am going against the conventional wisdom
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