Why Heteroscedasticity matters in Marketing Mix Modeling (MMM)
MMM is something that always leads you to reminisce about the learning one had or hadn’t during their statistics course. 15 years ago, when I was
Marketing Attribution
How a statistical technique that helped solve German Tank problem during WW2 is helping us get accurate attribution in Marketing Mix Modeling (MMM)
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.
Confidence intervals in a way are barometer of your Marketing Mix Models
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
Looking to get started on MMM? Know your type of Data.
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 you should not expect high R squared value in your MMM models
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
MMM pro tip: Use spends instead of Impressions in your model
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
Don’t make million dollar marketing decision just based on correlation
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
Unpacking the granularity problem in MMM
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
Validating MMM models the right way
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.
How useful is F-test in Marketing Mix Modeling?
As most of you know, one of the interesting application of linear regression is Marketing Mix Modeling (MMM). Even though additional bells and whistles are
