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Marketing mix modelling vs. attribution

Marketing mix modelling vs. attribution

What's the difference between MMM and attribution modelling? What are pros and cons of both approaches and is there a all-in-one alternative?
Marketing mix modelling vs. attribution

What is attribution?

Attribution is about assigning value to the touch-points that guide a user through the sales funnel based on a specific logic. While this sounds promising in theory, there are multiple reasons why it often fails to produce reliable results in practice.

Challenges of traditional attribution models:

  • Models like multi-touch, U-shape, etc assume the entire customer journey is trackable. In reality, tracking is often incomplete due to challenges like cross-device browsing and cookie restrictions.

    Attribution challenges

  • They focus on clicks and clicks alone, neglecting visually focused channels like Video and Social.
  • They are even less effective for brands with long consideration cycles because of limited attribution windows.
  • The models are based on heuristic logic. The U-shape, for example, states that the first and the last touch are the most important, which isn’t based on proven reasoning.

What is Media Mix Modelling (MMM)?

MMM studies past sales and marketing data to predict how future marketing actions might affect your business goal. The aim? Smartly allocate your marketing budget.

  • How much historical data is required for MMM? You need 1-2 years of past data.
  • How much does MMM cost? For mid-sized businesses, it’s between £7,000 and £35,000. For bigger companies, it starts at £85,000.

How MMM is different from common attributions like multi-touch?

  1. It aims to understand the role each channel and campaign plays in the overall success of the business. Instead of pinpointing tracked touch-points like multi-touch attribution.
  2. MMM analyses the link between sales and ad spending using multi-linear regression. Models like multi-touch or last-click, base their value on a touchpoint’s place in the customer journey. Which is built on a false promise that we actually can track the whole customer journey.

Challenges of Media Mix Modelling:

  1. High entry point. It’s best for those with a sizable marketing budget and diverse channels like TV and radio to evaluate advertising effectiveness.
  2. MMMs often don’t give enough weight to activities that build brand awareness. This is because they mainly look at sales, not awareness. But, if you want to focus on brand awareness, you can adjust how you use MMM.
  3. It’s not real-time. MMM is retrospective, meaning it looks at past data. It doesn’t offer real-time insights.

What to choose MMM or attribution?

While MMM offers robust insights compared to traditional attribution methods, it comes with its own set of challenges. It requires a significant time investment, substantial costs, and the expertise of in-house analysts to refine and process the data. Moreover, MMM is designed for broader, strategic planning rather than granular, day-to-day optimisations.

All-in-one alternative

For precise real-time insights on your marketing mix, turn to SegmentStream’s attribution. It uses machine learning and first-party data to navigate cookie restrictions and accurately measure your marketing’s incremental impact.

Unlike multi-touch attribution, which struggles with challenges like GDPR, iOS17, ITP, and Safari.

Watch this video to see an example of how businesses drive insights from SemgentStream’s report.

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