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 »
Did you know MMM has inbuilt Incrementality testing? Let me elaborate. Incrementality is defined as the additional impact of a marketing on the KPI (sales, TOMA, CAC etc..) over and above what would have been generated organically. Recently, we had an interesting conversation with a brand that ventured into TV ads since past one year.
What? MMM has inbuilt Incrementality testing? Read More »
There are some parallels between efficient and quick MMM delivery and that of a F1 pit stop. Much like a F1 Pit stop, a lot of things needs to come together for an efficient and quick MMM delivery. They are: ◾ Data Analysis Skills: One of the biggest roadblock I have seen in getting MMM
How to deliver robust MMM models quickly Read More »
During the Black Friday – Cyber Monday (BFCM) period, we often notice that brands increase their marketing spends, roll out discounts, try new media channels and launch new campaigns. The resultant sales for some of these brands during this period is higher than any other period in the year. But most marketers still don’t have
Boost your incrementality testing through Causal Experiments Read More »
Aryma Labs is your global partner to drive marketing efficiency in a privacy-first era. Aryma Labs leverages cutting edge Data Science solutions to solve marketing measurement and attribution problems. We are on a mission to help enterprises of all sizes adopt Marketing ROI Solutions for a privacy first era.
