Geo Models

Geo models enable marketers to build more accurate marketing mix models

Breaking your data down into different geographical regions can improve the accuracy of your model. This is because there are more likely to be differences day to day between variables at the regional level, which get smoothed out at the national level. So by predicting regional sales instead, the model has more variance to go on. 

For example, if you happen to spend more in the midwest on some days than you did in other regions, the model will be able to pick up that signal to paint a better picture of the impact of your spending. 

Unlike breaking the weekly data down to daily, you're not likely to introduce any day-of-week patterns. In addition, by keeping the regions relatively large (north, south, midwest, west), you can avoid adding a lot of noise to the data set.

Building local nuance into models via geo models helps add dimensionality while providing more data to aid generalization and predictive accuracy. 

What is a geo model?

It’s very common to lack sufficient data to build an effective marketing mix model (MMM). 

Generally speaking, it’s advised you aim for 7 to 10 observations per variable included in the model. So, with 3 years of weekly data, you can support 22 variables. 

While that may seem considerable, it’s typically not enough to account for the standard marketing mix, and when you factor in transformations like diminishing returns and adstocks, you might be lacking sufficient data. So for new businesses and startups with less than 3 years of data, it’s essential to maximize resources. One way to do that is by creating a geo model. 

A geo model involves splitting data into geographical regions. So, if you advertise in the US and have decent data for each state, you can split data into 50 states. 

Naturally, this provides more variance, thus influencing the bias-variance trade-off and increasing model performance to a point. It is possible to go too far, though, and it’s often a matter of including useful geo regions without pushing variance too high. 

By using geo models, you can tease out different changes and interactions at lower levels, helping negotiate issues like multicollinearity. Regional analyses can be engineered, e.g., by setting up a deprivation test to turn off spending in randomly selected areas and scale testing, where spending is increased in different regions to understand strengths and weaknesses in the marketing mix. 

Building Geo-Level marketing mix models 

First, let’s look at a marketing mix model at the national level. Here, there are three variables in our dataset: the price of pies, advertising costs, and whether or not it was a holiday that week. 

The accuracy here is good, as measured by R-Squared (R2) and the Normalized Root Mean Square Error (NHMSE). So the model says that our three variables predict 94% of the changes in sales (R2) and that on an average day, we’re inaccurate by 8.8% (NRMSE).

Here, advertising drives sales at $17 for every $1 spent. When we increase the price by $1, we lose $19,000 in revenue (sales here are in $1,000s). The variable is statistically insignificant, so we must be wary of our results. Further, we should be wary of our advertising ROI as the model indicates between -$42 and $77 return on ad spend (ROAS).

MMM with national data

Geo-modeling

If we want to convert our above model down to a more granular geo level, we first need to change our data. So, we were predicting national data on a given week, which looks something like this:

MMM data
National data

Regional data looks like this:

MMM data
Regional data

This is assuming we actually possess regional data. If you don’t, then you can use interpolation, where you take the region's population as a percentage of the whole multiplied by the national figures.

Of course, this is an estimate and subject to bias, so it’s highly recommended you obtain genuine regional figures. 


As you can see below, our coefficients have now changed to reflect regional data. It now says the holiday is worth $66k in revenue vs. the $266k indicated at national level. That’s because we have four regions (north, south, east, and west). 66 x 4 is 244, so it roughly equates to what we expect for the whole. 

Holiday revenue is roughly 1/4 of what it was at national level, which is correct

As a genuine experiment, there are genuine limitations. For example, the R2 and NRMSE are both worse than before. You might conclude that the model is worse than it was before, but advertising is now statistically significant, and the model has correctly located a $10 to $25 return on ad spend. This model is much more confident about the impact of ad spending. 

Advertising ROI is now within the range of $10 to $25

How accurate was the geo-level data?

In this model, the example data is generated using random number generations. This enables us to see how close the models are to the ground truth

Here, the national and geo models are close to the ground truth. The geo model was more accurate for price, with a coefficient of $4.91 vs. $19.03 at the national level. 

Overall, the geo model produces a better margin of error and tightens the advertising range, proving that advertising was statistically significant. 

You won’t know the ground truth in real life, so statistical significance and margin of error are critical. You can’t look at R2 and NRMSE in isolation, as the model is only effective if it can explain the underlying mechanisms driving the numbers.

Summary: Geo models

Geo models help solve the age-old issue of not having enough data for a model. National data provides a generalization that can be broken down into smaller regions to supply the model with additional variance. There are limits, however, as using too much regional data will introduce noise and other issues. 

Splitting national data into regions is a good place to start. In the US, you might go for Northeast, Southwest, West, Southeast, and Midwest. 

In the UK, you might go for England, Scotland, Wales, and Northern Ireland. You could even add individual cities, like London and the South East, for the UK. 

Once you plug regional numbers into your model, pay attention to what changes. In the above example, the margin of error and statistical significance improved to produce an accurate advertising ROI range.

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Frequently Asked Questions

What is a geo model?

A geo model is a model split into different geographical areas. For example, in marketing, you might have national sales as an output you want to predict. However, national sales may not provide sufficient data - and it's too general. In this case, you could split data down by regions to supply more detail to the model. The same concept applies in other domains too. For example, it wouldn't be a lot of good predicting weather for an entire country. In such an example, you're best off getting as granular as you possibly can to predict weather at local level - which is something we take for granted today.

What is geo modeling in marketing?

When building marketing models such as marketing mix models (MMMs), we often find we have data across different regions or time periods. For example, we might have monthly, weekly or daily data, and national, regional and local data. When we have the choice, we should use data that gives us a granular, detailed view of what's going on. However, this can go too far, as we don't want to add too much noise and variance to the model. In marketing mix modeling, using regional data enables marketers to supply more detail to the model than if they were using national data only. This is the purpose of geo modeling - to build a more detailed model.

How can marketers get more data?

When building a marketing mix model, a common issue is owning insufficient data. Luckily, there are various ways to engineer new data using feature engineering, interpolation, and extrapolation. However, you might have more data than you think. For example, if you're building a model with national variables, consider if you have regional or local sales data. If you're selling in the US, you'll have national sales figures, but could also break down sales by region (Northeast, Southwest, West, Southeast, and Midwest). Supplying the model with regional data might yield better results. This is a common-sense solution to not having enough data at national level when regional data is present.
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