Econometrics

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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Calibration vs Validation in MMM

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

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

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Validating MMM models the right way

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

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)

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Unpacking the granularity problem in MMM

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

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

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How to deliver robust MMM models quickly

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

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

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Boost your incrementality testing through Causal Experiments

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

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

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You can do MMM with small marketing spends

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

MMM is for enterprises of all sizes. Of late I came across a lot of posts proclaiming that MMM is not ideal for SMBs. I want to dispel some myths on this. 📌Small Marketing Spends Small marketing spends is not a show stopper. Not statistically and not marketing wise either. There are various statistical techniques

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Why Heteroscedasticity matters in Marketing Mix Modeling

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

MMM is something that always leads you to reminisce about the learning one had or hadn’t during their statistics course. 15 years ago, when I was a student, I attended one statistics seminar. In it the professor told “Always be testing your assumptions”. That statement rings true even now, especially in MMM. In MMM, inference

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MMM pro tip: Use spends instead of Impressions

Leave a Comment / Blog, Econometrics, Marketing / arymalabs@dmin

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 their

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

Calibration vs Validation in MMM

Validating MMM models the right way

Unpacking the granularity problem in MMM

How to deliver robust MMM models quickly

Boost your incrementality testing through Causal Experiments

You can do MMM with small marketing spends

Why Heteroscedasticity matters in Marketing Mix Modeling

MMM pro tip: Use spends instead of Impressions

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