In every Marketing Mix Modeling (MMM) project that we undertake, we always study the previous spend pattern of the client. Why? Because how much one spends in marketing channels gives us only partial information. The other half is about the spend pattern in those channels. A brand with a ‘single burst’ strategy may not be […]
Quantum of Marketing Spend vs Pattern of Marketing Spend Read More »
Do you use the right tools to evaluate your MMM model? Having the right tools to measure your MMM model’s accuracy is as important as specifying the model correctly. Contrary to popular belief, MMM is just not about retrodiction. If the model is well specified, it can also be used for forecasting (for a reasonable
Do you use the right tools to evaluate your MMM model? Read More »
Few months back we published our whitepaper ‘Granger Causality – A possible Feature Selection Method in MMM’. ICYMI the link is in the resources section. In this blog I want to highlight another useful feature selection method – Transfer Entropy. As you must have guessed, the method is a information theoretic method. In case you
Using Transfer Entropy for Feature Selection in MMM Read More »
MMM is not new. CPG/FMCG domain has been adopting MMM for nearly 30 years. The statistical methodology behind it, is proven and time tested. Why then should one innovate ? Through this post, I would like explain why. 🎯 Reason 1: MMM is being adopted by new domains. There has been increasing adoption of MMM
Why MMM Needs Innovation Read More »
Decomp RSSD – The business fit calibration During the Meta panel discussion (link in Resources section), I had mentioned about Robyn’s Decomp RSSD metric. In our earlier posts too, both my co-founder Venkat and I extensively covered the topic of calibration and validation extensively. Calibration of MMM model can be achieved through goodness of fit.
Why Decomp RSSD is a Business Fit Metric Read More »
How to use AIC to select the best Marketing Mix Model (MMM). Firstly, let’s look at what is AIC and the most common misunderstanding associated with it. 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
How to use AIC to select the best Marketing Mix Model (MMM). Read More »
Seasonality ≠ Cyclicity Be it time series analysis or Marketing Mix Models (MMM), the distinction between seasonality and cyclicity is important. Many confuse seasonality with cyclical time series. Here is a quick distinction. Seasonal: A seasonal pattern is a fluctuation which occurs at regular time intervals. These time intervals are predictable. Seasonality does not always
How understanding seasonality and cyclicity can help you build better MMM models Read More »
Standardization before Regularization in MMM In my last post (link under resources), I covered the topics of Multicollinearity and Endogeneity. And how solving for Multicollinearity can lead to Endogeneity. To solve for Multicollinearity, many adopt regularization like Lasso or Ridge. But here are some key points to keep in mind. 👉 L1 and L2 are the
Standardization before Regularization in MMM Read More »
How common are S-curves in Marketing Mix Models (MMM)? Before we dwell on the S-curves, let’s talk about the Hill function. Did you know that the Hill function used for transforming media variables to capture diminishing effect has its origins in Biochemistry and pharmacology!! The equation was formulated by Archibald Hill in 1910 to describe
How common are S-curves in Marketing Mix Models (MMM)? Read More »
Multicollinearity is one of the enemies of Marketing Mix Modeling (MMM). Multicollinearity generally occurs when there is a high correlation between independent variables. Multicollinearity does not affect predictive power of the model but it causes a lot of issues when one needs to infer or attribute the changes in dependent variable to the independent variables.
Multicollinearity is one of the enemies of Marketing Mix Modeling (MMM). Read More »
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