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Most mid-market marketing leaders don’t have a creativity problem. They have a measurement problem.
You’re investing across paid search, paid social, content, events, affiliates, and more. Revenue is coming in. Your pipeline is progressing. But when it’s time to explain which investments actually drove growth, the story often leans heavily on last-click attribution.
We all know how this plays out.
Paid and organic search get all the attribution credit. Other valuable brand touches—your local meetups, your thriving user community, your CEO's LinkedIn posts, your engaging YouTube series—aren't tracked or attributed at all. You end up with an incomplete picture of what’s driving revenue, and you lose the confidence of the employees who own the under-credited channels.
Marketing mix modeling offers a more defensible way forward. Instead of relying on surface-level channel attribution, it uses statistical analysis and unified data to connect spend to revenue.
A marketing mix modeling refresher
Before we jump into the Hack the Stack-style tutorial, let’s do a quick refresher on Domo MMM, which runs on Snowflake and uses Stella’s statistical modeling engine to connect marketing spend to revenue contribution.
If you’re rethinking how marketing performance analytics should work inside your organization, here are five ways builders use marketing mix modeling inside Domo to change the conversation:
- Unify fragmented marketing data: Bring together data across paid channels, CRM systems, revenue data, and external factors so attribution reflects the full business picture, not just platform reports.
- Measure incremental revenue impacts. Focus on how channel performance drives revenue growth, moving beyond leads and last-click conversions.
- Model future budget scenarios. Evaluate the potential impacts of reallocations or reductions on pipeline and revenue before committing spend.
- Support marketing budget optimization. Use statistically defensible analysis instead of relying on intuition or historical averages.
- Strengthen executive marketing reporting. Present a modeled revenue contribution that stands up in the boardroom.
Now that we’re clear on the value, we’ll show you how to set up your own marketing mix modeling.
How to set up Domo MMM Premium in 8 steps
- Find the Domo MMM app
If you’re a Domo customer:
- Go to Apps in your main navigation.
- Select Domo MMM from your installed apps.
If you’re new to Domo MMM, your team will receive a dedicated instance configured for modeling. A Stella GPT notebook instance supports the statistical analysis behind the scenes, but you won’t need to manage it directly.
Once you’re inside the app, you’re ready to begin preparing your data.
- Integrate your data into Domo
Marketing data rarely starts clean. It lives across platforms like Google Analytics, Adobe Analytics, CRM systems, paid media platforms, and even offline channels like TV or billboards. Before modeling begins, those sources need to be unified and integrated.
You (or your Domo team) will:
- Connect paid media spend.
- Connect CRM and revenue outcomes.
- Include organic web data.
- Add offline channel data where available.
- Standardize time periods (typically weekly).
All of this flows into a centralized Domo DataSet that becomes the foundation for your model.
Aim for at least two years of historical data. That range helps validate seasonality and long-term trends.
Don’t worry if some rows include zeros. If you paused paid spend during the holidays, for example, the model accounts for that variation. Gaps don’t break the analysis but, rather, inform it.
- Map your data
Once your master Domo DataSet is ready, upload it into Domo MMM and complete the setup form.
You’ll define:
- Date column
- Revenue column
- Channel spend columns
- Time granularity
This step tells the model how to interpret your structure.
- Add control variables
Not every revenue driver is paid media. Maybe you hosted a major event, launched a new pricing strategy, or rolled out a big outbound push.
Control variables allow you to account for those business moments so the model doesn’t mistakenly attribute their impact to paid channels.
There’s no strict limit. Add relevant controls that help the model isolate true channel contribution.
- Add custom variables
Sometimes you don’t have full spend data, but you still want to represent influence.
Custom variables allow you to introduce binary indicators:
- 1 = event occurred that week
- 0 = it did not
For example, if your CEO appeared on a major podcast or you launched a short-term brand campaign without tracked spend, you can still account for that activity.
This ensures your marketing attribution model reflects reality as closely as possible.
- Enrich with IROAS data
If you have historical incremental return on ad spend (IROAS) data, you can include it here. While the model will calculate the impact using your uploaded data, prior IROAS benchmarks can improve calibration and strengthen analysis.
This step is optional but valuable for mature teams.
- Confirm your analysis
Before running the full model, you’ll see a preview summary of your inputs.
Review your channel mappings, revenue totals, date ranges, and variable selections. Once confirmed, launch the analysis.
- Review your results
After processing, you’ll receive modeled outputs. Here’s how to interpret the headline metrics:
- Attributed revenue (e.g., $53M): This represents the portion of revenue the model can statistically link to marketing activity, not total company revenue.
- Model fit score (e.g., 0.87): This indicates how well the model explains revenue variation. Scores above 0.8 means the analysis is strong and reliable enough for decision-making.
- Prediction accuracy (e.g., within 14 percent): This reflects how closely projected revenue aligned with actual results. From a statistical standpoint, this level of accuracy suggests the analysis is sound.
Below these summary metrics, each channel receives its own performance card, showing incremental contribution and return. This is where optimization begins.
Model your marketing mix on infrastructure you can trust
Domo MMM, powered by Snowflake and Stella's statistical modeling, links marketing spend to revenue impact, showing you what drives results. Your data stays secure in your cloud, and your model reflects your historical performance.
Your get projections based on statistical analysis, not guesswork. And there’s even more depth inside Domo MMM, including deeper scenario modeling and channel reallocation tools.
If you want marketing reporting that stands up in executive conversations, explore Domo MMM or request a demo today. If you want the full walkthrough, explore the complete Domo MMM Premium tutorial and see how to refine spend, strengthen executive reporting, and drive more confident marketing decisions.
For now, thanks for reading this edition of Hack the Domo Stack!
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