Marketing Mix Modeling

How understanding seasonality and cyclicity can help you build better MMM models

Leave a Comment / Blog, Econometrics, Marketing Mix Modeling, Statistics / arymalabs@dmin

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 […]

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Standardization before Regularization in MMM

Leave a Comment / Blog, Marketing Mix Modeling, Statistics / arymalabs@dmin

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

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How common are S-curves in Marketing Mix Models (MMM)?

Leave a Comment / Blog, Marketing Mix Modeling, Statistics / arymalabs@dmin

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

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Multicollinearity is one of the enemies of Marketing Mix Modeling (MMM).

Leave a Comment / Blog, Marketing Mix Modeling, Statistics / arymalabs@dmin

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.

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CMMMO = CSO

Leave a Comment / Blog, Marketing Mix Modeling, Statistics / arymalabs@dmin

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

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MMM Model Update – What, Why and When

Leave a Comment / Blog, Marketing Mix Modeling, Statistics / arymalabs@dmin

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

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What is the best Marketing Budget Allocation

Leave a Comment / Blog, Marketing Mix Modeling / arymalabs@dmin

Marketing Budget Allocation One of the important use cases of MMM is the Marketing Budget Allocation. The results of MMM are directly leveraged to provide various ‘What-if’ budget allocation scenarios. Such as: ▪️ Under the same budget, how could we have allocated the spends across media differently to yield a lift in KPI? ▪️ What

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Marketing Mix Modeling (MMM) and MTA are like X-ray into your marketing strategy.

Leave a Comment / Blog, Marketing Mix Modeling, Statistics / arymalabs@dmin

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

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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

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Don’t Stepwise Regression your MMM model

Leave a Comment / Blog, Marketing Mix Modeling, Statistics / arymalabs@dmin

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

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How understanding seasonality and cyclicity can help you build better MMM models

Standardization before Regularization in MMM

How common are S-curves in Marketing Mix Models (MMM)?

Multicollinearity is one of the enemies of Marketing Mix Modeling (MMM).

CMMMO = CSO

MMM Model Update – What, Why and When

What is the best Marketing Budget Allocation

Marketing Mix Modeling (MMM) and MTA are like X-ray into your marketing strategy.

7 key steps to get your Multi Touch Attribution (MTA) right

Don’t Stepwise Regression your MMM model

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