When to Use Marketing Mix Models vs. Data Driven Attribution


Marketers today rely on a variety of measurements to gain insights into their campaign efforts. Given the combination of digital, print, and broadcast channels that consumers engage with, understanding the combined impact of marketing tactics across this diverse media mix has become difficult to say the least.

In order to effectively leverage the online and offline channels that drive consumers to purchase, marketers first need to understand when and why to use specific marketing measurement strategies. With this in mind, understanding two fundamental measurements—marketing mix modeling (MMM) and data-driven attribution (DDA)—can help marketers make more informed decisions across their measurement efforts for better campaign results.

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Understanding Marketing Mix Modeling and Data Driven Attribution

Marketing mix modeling and data-driven marketing attribution provide two fundamental yet unique insights for marketing campaign optimization. In order to understand how to use these two measurements for optimal insights, marketers first need to understand the metrics and purpose of each.

Marketing Mix Modeling

MMM has had a place in marketers’ analytics toolkit for decades. This is due to the unique insights marketing mix models can provide. By leveraging regression analysis, MMM provides a “top down” view into the marketing landscape and the high-level insights that indicate where media is driving the most impact.

For example: by gathering long-term, aggregate data over several months, marketers can identify the mediums consumers engage with the most. MMM provides a report of where and when media is engaged over a long stretch of time.

Data Driven Attribution

As the consumer landscape shifted toward diverse digital marketing channels, marketers recognized the need for insights that went beyond high-level measurements. The solution to this need was data-driven attribution. DDA looks at the unique touchpoints across digital channels and scores each those touchpoints based on the impact they have had toward driving consumers down the sales funnel.

With data-driven attribution, marketers can quickly pinpoint which touchpoints across media drive the most engagement. They can then combine those insights across various digital channels to better understand the relationship between channels and their impact on ROI.

Weighing the Pros and Cons of MMM and DDA

When it comes to marketing measurement, relying on any single method simply won’t provide the insights needed to reach and engage today’s consumers. In order to properly combine measurements into a modern unified marketing measurement strategy, marketers need to be able to identify and weigh the benefits and challenges of each measurement they use.

The Pros and Cons of Marketing Mix Modeling

When it comes to initial marketing strategy or understanding external factors that can influence the success of a campaign, marketing mix modeling shines. Given the fact that MMM leverages long-term data collection to provide its insights, marketers measure the impact of holidays, seasonality, weather, brand authority, etc. and their impact on overall marketing success.

As consumers engage with brands across a variety of print, digital, and broadcast channels, marketers need to understand how each touchpoint drives consumers toward conversion. Simply put, marketers need measurements at the person-level that can measure an individual consumer’s engagement across the entire customer journey in order to tailor marketing efforts accordingly.

Unfortunately, marketing mix modeling can’t provide this level of insight. While MMM has a variety of pros and cons, the biggest pitfall of MMM is its inability to keep up with the trends, changes, and online and offline media optimization opportunities for marketing efforts in-campaign.

The Pros and Cons of Data-Driven Attribution

For marketers looking to generate more granular insights, data driven attribution has been a top choice for measurement.  Given how quickly data can be collected using digital channels, DDA helps provide fast-paced insights that can accurately attribute value to channels as consumer engagement shifts. Data-driven attribution allows marketers to make intelligent optimizations at a rapid pace. For example, if marketers know that display ads and retargeting ads are generating the most engagement across digital channels, they can then shift their remaining marketing spend to focus on driving more traffic to the specific channels hosting those ads.

One drawback to consider is that data-driven attribution leverages complex algorithms to properly attribute the value of each marketing touchpoint. For marketers, this complexity means that they need to choose an analytics platform capable of generating these kinds of specific and targeted insights. Using the wrong analytics platform can leave marketers optimizing their efforts in the wrong places, resulting in bad data and misattribution.

Final Thoughts

In order to effectively understand the today’s marketing trends and optimize campaign efforts accordingly, marketers need to first recognize the strengths and weaknesses of the individual measurement methods they use. When it comes to marketing mix modeling and data-driven attribution, both offer valuable insights that can lead to better campaign performance, but marketers are better served using a combination of the two to account for gaps in data coverage.

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