The Chief Scrutinizing Officer (CSO) Every Marketing Mix Modeling (MMM) project or for that matter any Data Science project needs a Chief Scrutinizing Officer (CSO). Here is how the skill of scrutinizing comes in handy: 1. Scrutinizing the data: The first step in any Data Science is to examine the data carefully. In our recent […]
Wondering when to update your MMM model? Here is a guide 👇 Predominantly, a Machine Learning model needs to be updated for two reasons: 1. Model Drift 2. Business Reasons Let’s first breakdown scenarios where MMM models need updating. ◾️ Seasonality – Some brands in CPG/ FMCG space exhibit seasonality. For e.g., Packaged Juices may
MMM Model Update – What, Why and When Read More »
Marketing Mix Modeling (MMM) and MTA are like X-ray into your marketing strategy. Much like an X-ray. They tell you: ✅ Which strategy is broken. ✅ Is your revised strategy healing (performing) well. ✅ Does any anomaly point to future problems. Given the recessionary trends worldwide, now more than ever MMM & MTA are the
Marketing Mix Modeling (MMM) and MTA are like X-ray into your marketing strategy. Read More »
7 key steps to get your Multi Touch Attribution (MTA) right !! We have built 30 Markov Attribution models for companies across geographies in the last 3 yrs. Here are the 7 key steps to make your MTA project a success. 📌 Reality is different from toy examples: Things don’t work as easily as illustrated
7 key steps to get your Multi Touch Attribution (MTA) right Read More »
So recently a client hired us to build MMM models for them after failed attempts to in-house the MMM capability. Earlier their in-house Machine Learning engineers (with no statistics background) had built their MMM models thinking that it is just ‘linear regression’. We sat down with the MLEs and wanted to know how they went
Don’t Stepwise Regression your MMM model Read More »
How Marketing Mix Modeling (MMM) helped me learn Linear Regression from first Principles. Some of you have appreciated my posts on Linear Regression and other statistics topics. Some of you also often ask me resources to learn Linear Regression. I always provide a list of books or articles that I have personally read. But when
Why we report both confidence Interval and Prediction Interval in our MMM models. MMM is a type of linear regression but with lot more bells and whistles (check the link under resources for a primer on MMM). If you must have noticed in Linear Regression, the confidence interval are always narrower than the prediction interval.
Why we report both confidence Interval and Prediction Interval in our MMM models Read More »
Selecting MMM models via AIC? Some key pointers 👇 The Akaike information criterion (AIC) is given by: AIC = 2k -2ln(L) where k is the number of parameters L is the likelihood The underlying principle behind usage of AIC is the ‘Information Theory’. Talking about information theory, we have been researching and implementing these concepts
Selecting MMM models via AIC? Some key pointers Read More »
Calibration vs Validation A lot of people use calibration and Validation interchangeably. The two are not the same. ▪ Calibration In a regression setting, calibration of a model is about understanding the model fit. Goodness of fit measures like R squared values, P value, Standard Error, Cross validation and within sample MAPE/MAE/RMSE inform you how
Calibration vs Validation in MMM Read More »
Often I come across posts and comments from people where they make claims like ‘Linear regression is all about predictions’. Well they are wrong but I don’t quite blame them. Thanks to the machine learning take over of statistical nomenclatures, any prediction task is now labelled as ‘Regression task’ !! This is of course two
Why Linear Regression is not all about predictions Read More »
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