Data Driven Attribution or Media Mix Modelling?

Data Driven Attribution and Media Mix Modelling (MMM) both help marketers understand marketing channel performance and return on investment. While it’s true that both help to measure marketing success, they differ in their data requirements and capabilities. If you are trying to figure out which one is best suited to your organizational needs, read on.

Below is a table which summarizes the main differences between the two.

Model Type

Media Mix Modelling typically uses a regression or some similar statistical or econometric technique to determine how much each channel impacts your conversion goal. This is more of a correlational measure, as we don’t have a direct link to be certain that any converted users have seen the media. Basically, the data is aggregated and not at the user level.

Data Driven Attribution modelling is commonly viewed as a more causal method, as we can find a  direct link between a user seeing a media channel and converting (or not converting). However, the actual algorithms used to measure the lift from each marketing channel will differ by provider, and may use a range of statistical and machine learning modelling techniques. Basically, the results you will receive may be different.

Data and Data Collection

Media mix modelling (MMM) has a one-time collection process, so if results are needed only once, or even yearly, this is going to be the better option. MMM is typically a better option when you have historical data for all potential drivers of your business readily available. MMM also works well with offline data, such as TV impressions or the number of stores open in a particular region.

Data Driven Attribution works best for the collection of data from online channels and for the ongoing collection and refreshing of models, so if frequent report updates are required, this would be the preferred option. Depending on the attribution provider, tags may be used to collect data or, attribution may work with data collected by your existing analytics platforms. Typically, there will be some time in between  when you initiate data collection, and when there is enough data available for attribution models to run so that  you can get your initial results.

Generally, Data Driven Attribution is the best option for measuring online conversion paths, though some attribution providers have connectors to offline media or offline conversions.

Online or Offline Conversions?

If the majority of your conversions occur offline, MMM makes more sense than Data Driven Attribution. Sure, some attribution providers have integrations available with data collection partners or other connectors to offline data. But the econometric models in MMM readily accommodate offline data. For MMM, the conversion type can be whatever is relevant to your business, provided the historic data is available (typically, this would be sales volumes (online and/or offline)).

Data Driven Attribution is the strongest candidate for online conversion types and can work well with hard conversion goals, such as purchases, and softer conversion goals, such as newsletter downloads.

Reporting Deliverables

If marketing strategy is constantly evolving, and budgets can be shifted easily between channels on a monthly or even weekly basis, then a Data Driven Attribution provider, which allows for daily report updates, is superior.  Reports may include detailed impacts of online effects such as lift analysis, recommended frequency capping, viewability, conversion path reports, and more.
MMM can be refreshed approximately once a year to keep your data recent and relevant, though the cadence of updates will depend on the nature of your business. If budgeting is typically done once a year, this approach can work well. Additionally, it does have the ability to test different budgeting scenarios and their resulting outcomes, whereas Data Driven Attribution may not have this baked in. Depending on the provider of your MMM model, and even though the inherent model is fixed, you may also have the capacity to play out different spending scenarios.  That is, your MMM model may allow you to forecast the ROI of channels based on different spend inputs.

In general, Media Mix Modelling is an approach that works well for companies with significant offline media spend while Data Driven Attribution modelling is most effective for companies with the majority of their marketing activities running online and in large volumes. I hope that this comparison has been helpful in your research! If you have further questions about how you are measuring your marketing success, please contact us.