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You just built your Marketing Mix Model (MMM) or got first results from your data science team – great!

Now comes the critical question: what’s next? How do you leverage these insights to effectively optimize your budget across channels like Google Search, Meta, TikTok, or Microsoft Ads?

If you’re feeling uncertain about how to apply these MMM results, you’re not alone. Many performance marketing experts encounter this challenge, and transforming MMM insights into actionable strategies can be complex. 

In this blog, we’ll share valuable lessons we’ve learned from working with clients who, after building their MMM, struggled to make it actionable. More importantly, we’ll explore how to avoid common pitfalls and ensure that your Marketing Mix Model drives real, tangible results for your campaigns.

Table of Contents:

The Real Challenge: Why Many Marketing Mix Models Fail

Did you know that nearly 50% of Marketing Mix Models can’t be applied in real-world scenarios?
(Spoiler: statistic from our own research/QA in 50+ calls with clients)

That’s a problem, especially in performance marketing. Why? A good MMM should do more than just reveal past results and highlight which campaigns or tactics drove higher ROI. Ideally, it should provide clear, actionable steps for your budget allocation, helping you determine the most efficient way to invest across channels. When a model isn’t actionable – like those that remain too general or only focus on broad marketing channels rather than specific tactics – you risk wasting time and resources.

This gap between analysis and action can be very costly for digital marketers. Without actionable insights, you’re left to rely on gut instincts and move money around inefficiently. In worst-case scenarios, poor MMM setups lead to misguided budget shifts that hurt performance.

Why Is This a Problem?

Digital marketing isn’t like traditional channels, where you can not attribute impact to a handful of TV or radio spots. It’s run on a more granular level. Performance marketing is complex, with different tactics across Meta / Facebook Ads, Google Ads, Microsoft Ads, Display, TikTok, etc. A one-size-fits-all channel-level MMM won’t work.

For example, search-brand ads perform very differently from generic search ads, shopping ads, or Performance Max campaigns. Grouping them all together in what we sometimes see as “Google Paid” dilutes the insights so significantly that they are no longer actionable.

Additionally, your business may have strategic boundaries you can’t change, like regional or fixed product budget splits or department-based allocations. A MMM won’t account for this without guidance from the team setting it up. Implementing this segmentation from the start is key to actionable results. 

Last but not least, often, MMM projects are run from the analytics or data science department with few or not enough insights into the performance marketing aspects, and therefore, the abstraction level is too high to be implemented thereafter.

Seven Steps to Make Your Marketing Mix Model Results Actionable

Let’s break it down into a simple, 7-step process to ensure your MMM implementation gives results that are actionable:

  1. Set your boundaries upfront: Identify regions, departments, or other non-negotiable constraints where budget can’t be moved at all. These should be built into your MMM as fixed groups or separations if possible. Important – avoid too much detail: If you slice and dice your campaigns into too many micro-segments, you’ll have statistically not significant results. Strike a balance between granularity and practicality.
  2. Group campaigns by tactics, not just channels: Don’t lump all your Google Ads or Meta Ads into one group. Segment by ad type/tactic — example brand search, generic search, shopping, Performance Max, retargeting. Each tactic operates differently and requires its own segment within the model to generate actionable insights. By categorizing this way, your Marketing Mix Model (MMM) can provide more precise recommendations for budget allocation tailored to each tactic’s unique impact.
  3. Think about your data source: Decide early on what key variables you’re measuring and take into account. Is it spend-to-revenue? Or do you need to add pixel signals from publishers (like clicks, views, and conversions) as intermediate touchpoints? Also ensure to be covering offline signals otherwise, the background will absorb all the contributions.
  4. Collect clean and structured data: Remember, “garbage in, garbage out” – if your input data is messy or inconsistent, your Marketing Mix Model (MMM) results will be, too. Ensure your data sources are clean, structured, and aligned with the tactical segments you’ve defined. Consistency is key to generating actionable insights. When your data is reliable, your model’s recommendations will be as well.
  5. Test your MMM on a small scale first: Before you apply the model across all campaigns, test it on a subset of your data. See if the model provides actionable recommendations for this smaller test case – which you think makes reasonable sense. Adjust the boundaries or grouping if needed.
  6. Ensure clean results: ensure the MMM gives you a clear indication of the ROI of each campaign type/region/department group. Ideally, given as factors of the ROI attributed vs. analyzed with the regressions. The variability of the data needs to be given – so if you have a flat spend/contribution for most channels, the models will not find any interesting insights in comparison to when you’ve movements/changes/seasonality etc.
  7. Integrate results into your optimization: As a final step, implement the guidance for the MMM (ideally factors on pixels or tracking ) into your ongoing optimization process. (Note: if you’re a Nexoya customer, please reach out to your customer success manager to activate the upload for your MMM factors and run the calibration process. This will allow you to see the calibrated MMM factors directly within the Nexoya platform and make it actionable on the weekly optimizations as well as simulations.)

Summary

The key to successful MMM implementation is ensuring the model produces insights that can be directly applied to your performance marketing efforts. Avoid broad channel-level generalizations – instead, group campaigns by tactics, set clear boundaries and ensure data is clean and consistent. By following a structured approach, your MMM can become a powerful tool to guide budget allocation and drive improved performance across your digital marketing channels.