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What is the impact of granularity on Marketing Mix Modelling?
What is the impact of granularity on Marketing Mix Modelling?
Antti Heliste avatar
Written by Antti Heliste
Updated over 9 months ago

What is granularity in Marketing Mix Modelling?

One of the most critical factors in effective Marketing Mix Modelling is granularity, which refers to the level of detail in the data and model. Granularity is typically considered in three dimensions:

  1. Time granularity: This refers to the frequency at which you collect data, such as daily, weekly, or monthly.

  2. Media granularity: This pertains to the depth of media data you collect, such as at the media level (e.g., TV), media channel level (e.g., individual TV channel), or campaign level (e.g., ad or ad campaign on TV).

  3. Sales granularity: This indicates the level at which you measure the impact of marketing, such as on total sales (e.g., clothes), product category sales (e.g., sports clothes), or individual product level (e.g., a specific sports t-shirt).

These dimensions of granularity can vary based on business environments and desired modelling outputs.

Why is granularity important?

The significance of granularity in MMM is profound, influencing several aspects of modelling:

  1. Result accuracy: Increasing time and media granularity generally increases the number of data points in the model, bringing several benefits:

    1. More data points: A daily-level data set provides 365 data points per year, compared to 52 from weekly data and just 12 from monthly data. More data points typically result in more accurate modeling since individual points are less likely to distort the results. In Bayesian models, more data also means that priors have less power over the results.

    2. More variation in data: For instance, a company may allocate the same TV budget monthly, but distribute it unevenly among weeks. Greater data variation often leads to more accurate measurement of marketing effectiveness. It also aids in determining the diminishing returns curves, which are crucial for optimizing media budgets across various mediums and campaigns. (For further discussion, refer to Chan and Perry (2017).)

  2. Optimization opportunities: Improving time, media, and sales granularity results in a more detailed MMM, thereby expanding optimization opportunities. For instance, if you have MMM results at a product level instead of a product category level, it allows you to optimize marketing for individual products by focusing on the best performing product instead of just shifting budgets between categories. Similarly, weekly MMM results enable marketing optimization on a weekly level, allowing more precise timing of campaigns than when using monthly level data. Moreover, if you measure the impact of individual campaigns instead of the media as a whole, you can optimize marketing at the campaign level by repeating the most impactful campaigns.

    Using aggregated data often leads to the "tyranny of the average", where only the average for the entire measurement group is visible, not the best and worst performing parts. For instance, you won't see the best and worst-performing products in a product group if you only measure marketing on the group level. However, if you measure marketing at a lower level, you'll typically find more components to optimize, and the range of performance differences usually expands. This means you'll have more optimization opportunities. (For further discussion, refer to Heliste (2019).)

    The example below illustrates this phenomenon by showing the distribution of ROI between product categories, product groups, and individual products:

    1. Product category level: Measuring at this level only reveals the ROI for 10 different parts (5 categories with an ROI of 0; 3 with an ROI of 1; 2 with an ROI of 2). The range of the ROIs is also very narrow, leaving little room for optimization.

    2. Product group level: Measuring at this level for sports clothes reveals results for 100 different groups, offering much more parts that could be optimized. The range of the ROIs is also wider, meaning that more significant optimizations are possible. For instance, we could reallocate the budget from lowest-performing categories to the highest-performing ones.

    3. Product level: Measuring at this level for sports t-shirts gives us results for 1000 different t-shirts, creating extensive yet complex optimization opportunities. The range of the ROIs is also very wide, meaning that the optimization opportunities are even bigger than on higher levels. However, manually optimizing marketing on this level is very difficult.

Overall, a higher level of granularity increases result accuracy, helps in measuring diminishing curves, and rapidly increases and improves optimization opportunities. The benefits of granularity, however, will vary across business environments depending on various factors, such as the complexities of product and media hierarchies.

ROI distribution by sales group using different measurement granularities.

What challenges come with high granularity?

While high granularity offers many benefits, it also presents its own set of challenges:

  1. Equal granularity requirement: In Marketing Mix Modelling, it is typically required to have data at the same level of aggregation across all sources. Achieving this can be challenging, leading to the common practice of aggregating data to match the source with the least granularity. An alternative solution involves disaggregating low granularity data, such as dividing monthly spend evenly across weeks. However, this method often requires assumptions that may not be accurate. (For further discussion, refer to Chan and Perry (2017).)

  2. Lack of data: Paradoxically, a higher granularity can lead to a lack of data. This depends on how granularity is being increased. Take sales granularity as an example. Usually, this entails dividing the model into multiple sub-models (i.e., one model for each sales category to be measured). If you're attempting to analyze the impact of marketing on a specific t-shirt, but you have only advertised it once, the model's accuracy will likely be low due to insufficient data points. This issue can also surface when enhancing media granularity; if certain media are seldom used, you may only have a few data points for each of them, resulting in lower quality estimates for these media.

  3. Issues with data collection: Obtaining reliable, detailed data can pose challenges, particularly if the collection process is not adequately automated. Poor data quality can, in turn, lead to unreliable results.

  4. Impact of higher-level activities and cross-category influences: Promoting your brand or a product category often benefits individual products too. Similarly, promoting specific products can increase sales of other products. When measuring marketing effectiveness of individual products, it's essential to consider these effects, for example, by allocating a portion of the spending from higher-level activities or from promoting other products.

  5. Adstock and carryover effects: When using daily data, the impact of marketing often affects multiple days in the time series. However, with monthly data, most of the impact likely falls within the same month. Therefore, considering adstock and carryover effects is crucial in high granularity modeling.

  6. More complicated models: Greater granularity usually results in more data points or/and parallel models, increasing the demand for computing power. Similarly, factoring in adstock, high-level activities, and cross-category effects will make the model more complex.

How to determine the level of granularity?

Choosing the appropriate level of granularity depends primarily on your business needs and capabilities:

  1. Business needs: Are you looking to optimize marketing at the product group level versus the product level, or at the media level versus the campaign level? If you do not intend to make optimizations at lower levels of sales and media dimensions, then it may be unnecessary to increase granularity for those dimensions. However, enhancing time granularity is generally advantageous as it simply expands the data points for the model.

  2. Optimization capabilities: Can you make the hundreds or thousands of different optimization decisions required to properly optimize marketing at a low level? Generally, the more decisions you have to make, the more automation is needed in decision-making. If high granularity Marketing Mix Modeling (MMM) is not combined with automated decision-making, optimizing marketing can become challenging. In some instances, it may be sensible to use different tools at various levels of granularity. For example, an MMM tool at a high level for high-level budget allocation and a promotion optimization tool at lower levels for optimizing promotions.

  3. Data collection capabilities: Do you have the resources to obtain the necessary data for the modeling? If you desire a higher level of granularity, you will need to undertake more data collection and ensure the data is of high quality. Therefore, it is important to invest in automating data collection.

  4. Modelling capabilities: Do you have the capabilities and resources to build and run more complex and heavier models? If not, it might make sense to resort to simpler higher-level modelling.

References

Chan, D. and Perry, M., 2017. Challenges and opportunities in media mix modeling. Google Inc, 16. https://research.google/pubs/challenges-and-opportunities-in-media-mix-modeling/

Heliste, A., 2019. Adapting marketing mix modelling for the retail marketing environment–A road map for development (Master's thesis). https://aaltodoc.aalto.fi/items/6ec36105-5a78-4d1b-8fbd-b41a2176103c

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