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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 big chunk of the projects in the industry involves dealing with tabular data. This is particularly true of Marketing Measurement and Attribution Industry. However, not many have the knowledge of […]
Looking to get started on MMM? Know your type of Data. Read More »
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 propagated : ▪ High R squared value = good ▪ R square is a sign of predictive power of a model I guess we statisticians are partially to blame for
Why you should not expect high R squared value in your MMM models Read More »
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 CFO would be happy 😅). 2. Spends are more accurate than impressions. 3. Cross-Channel Comparisons – Spend data enables easier comparisons between different marketing channels or campaigns in terms of
MMM pro tip: Use spends instead of Impressions in your model Read More »
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 analysis like correlation. Ill advised suggestions like “All you need is only correlation” will do more harm than good. So I will detail out the advice that I came across
Don’t make million dollar marketing decision just based on correlation Read More »
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 problem (will talk about this in future posts), let me unpack the granularity problem. So what is the Granularity problem? Lets take the example of TV. Many brands spends lot
Unpacking the granularity problem in MMM Read More »
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. They often do so by belittling the importance of goodness of fit. I am sorry to say, but people who say “R squared value and statistical significance measures (e.g. p-values)
Validating MMM models the right way Read More »
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 prevalent in ML circles. But let me elaborate. Ideally if your goal is inference, you don’t need to train/test split your data. In case of prediction, train/test split is justified
Don’t train-test split your data in Marketing Mix Modeling Read More »
If you are familiar with Marketing Mix Modeling (MMM) or just multi linear regression in general, you must have noticed the following effects at some point in time: 1) Signs of variables changing 2) Wide Confidence Intervals 3) Large Standard Errors 4) Inflated R Squared value 5) Overall bad model fit These are tell tale
How Multicollinearity saps the statistical power Read More »
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 added in MMM over and above what a traditional linear regression entails, the core of MMM is still linear regression (or its many variants). Given this background, it becomes imperative
How useful is F-test in Marketing Mix Modeling? Read More »
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 problem (will talk about this in future posts), let me unpack the granularity problem. So what is the Granularity problem? Lets take the example of TV. Many brands spends lot
Unpacking the granularity problem in MMM Read More »
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