Partners Insights

Free Resource — Marketing Measurement

MMM Readiness
Checklist

A 22-point assessment to find out if your brand is ready to build Media Mix Modeling in-house — and exactly what to fix if you're not.

Format Self-assessment

Time to complete ~5 minutes

Total checkpoints 22 items

Sections 4

Most brands that invest in MMM either aren't ready for it — or are more ready than they think. The difference usually isn't budget or team size. It's data structure, channel complexity, and whether someone in the organisation is empowered to act on the output.

This checklist walks you through the four things that actually determine MMM readiness: your data, your marketing mix, your team, and your business context. At the end, you'll have a score that tells you clearly whether to move forward, what to fix first, or whether to build a data foundation before investing in modeling.

Tick each item that genuinely applies to your business right now. Don't tick aspirationally.

Count your total across all four sections at the end.

Check your score band — then read the recommendation that matches where you are.

Section 1 of 4

Data Readiness

MMM is a statistical model — it's only as good as the data you feed it. This section checks whether your data is structured, consistent, and complete enough to produce reliable output.

We have at least 12 months of weekly channel-level spend data

Ideally 2+ years. MMM needs enough observations to identify patterns. Less than 12 months produces unstable estimates.

1pt

We have weekly revenue or conversion data for the same period

This is your dependent variable — what the model tries to explain. It must align with your spend data in timing and granularity.

1pt

Our spend data is broken down by channel, not just total marketing spend

You need to know what you spent on paid search vs. paid social vs. TV vs. display etc. — separately, each week.

1pt

We have records of promotions, discounts, and major price changes

The model needs to separate your media effect from your promotion effect — otherwise it misattributes revenue lift to the wrong cause.

1pt

We can export spend and revenue data from our existing systems

Even basic exports from your ad platforms and your CRM or e-commerce platform are sufficient. Perfect data is not required.

1pt

Our revenue data comes from one source of truth (not split across platforms)

If your revenue figure differs between your Shopify dashboard, Meta, and Google — you need to agree on one number before modeling.

1pt

Section maximum: 6 points

Section 2 of 4

Marketing Mix Complexity

MMM adds the most value when you're running multiple channels and struggling to understand how they interact. If you're running a single channel, simpler analysis tools may be sufficient.

We are running 3 or more paid marketing channels simultaneously

The more channels you run, the harder it is to isolate each one's contribution — and the more MMM adds value over simpler methods.

1pt

We spend across both online and offline channels

Offline channels (TV, radio, OOH, podcasts) can't be tracked with pixels. MMM is one of the only rigorous ways to measure their impact.

1pt

Our total paid media spend is over $500K per year

Below this threshold, the cost of building an MMM typically outweighs the budget optimisation gains. Growth Analytics may be a better starting point.

1pt

We have channels that get no click-through data (TV, OOH, radio, podcasts, direct mail)

These "dark" channels are invisible to attribution tools. MMM can measure them. If you run any, this is a strong argument for MMM.

1pt

We have tried to measure channel contribution before and found the results inconsistent or confusing

If your platforms give you conflicting numbers, that's a symptom of the problem MMM solves — not a reason to avoid it.

1pt

Our media mix has changed significantly in the past 2 years

If you've added or dropped major channels, or shifted budgets substantially, an MMM update would capture the new reality better than older benchmarks.

1pt

Section maximum: 6 points

Section 3 of 4

Team & Organisational Readiness

A model no one acts on is worthless. This section checks whether your organisation is structured to actually use MMM output to make better decisions.

Someone owns the question: "What is actually driving our revenue?"

This person doesn't need to be a data scientist. They need to care about the answer and have the authority (or access) to act on it.

1pt

A decision-maker would reallocate budget based on model output

If the CMO or CFO would override the model based on gut feel regardless of what it shows, the ROI of building it drops significantly.

1pt

We have someone with basic data literacy who can liaise with a modeling team

They don't need to code. They need to understand what "channel-level weekly spend" means and be able to pull a CSV export.

1pt

Marketing measurement is a stated priority for the next 6–12 months

If measurement isn't a priority, the build will stall during data collection. MMM requires some internal buy-in and time commitment.

1pt

We are willing to share spend and revenue data with an external partner under NDA

This is a practical blocker for many companies. If legal or compliance would prevent data sharing, that needs to be resolved first.

1pt

Section maximum: 5 points

Section 4 of 4

Business Context

Certain business structures make MMM significantly more or less effective. This section flags context that affects model quality and output reliability.

Our business has recognisable seasonal patterns or demand cycles

Seasonality is a feature, not a problem. MMM models it explicitly. Highly unpredictable or event-driven revenue is harder to model.

1pt

Our revenue is primarily driven by one main product or category

Highly fragmented product ranges with very different marketing dynamics add significant modeling complexity. A focused product mix produces cleaner results.

1pt

Our media mix has been broadly stable for at least 12 months

Stability means the model has enough variation to learn from each channel. Dramatic budget swings in a short window reduce model reliability.

1pt

We have a defined goal that requires budget optimisation (not just spend maintenance)

MMM's primary output is a reallocation recommendation. If you're in a "maintain spend" mode with no appetite to shift budget, the output won't be actioned.

1pt

We are open to the model challenging our current channel assumptions

MMM sometimes shows that a high-ROAS channel is actually low-incrementality, or that a channel you've deprioritised is quietly driving revenue. You have to be willing to hear it.

1pt

Section maximum: 5 points

Your Readiness Score

Count your ticks across all four sections and find your band below.

Your score: 0 / 22

17–22

Ready Now

Your data, team, and business context are well-suited for MMM. A build will produce reliable, actionable output. Move forward.

11–16

Nearly There

A few gaps to address first — likely in data structure or organisational alignment. These are fixable in weeks, not months.

0–10

Foundations First

MMM would be premature right now. Focus on building data foundations and channel consistency before investing in modeling.

Score by section

Data Readiness

/ 6

Marketing Mix

/ 6

Team & Org

/ 5

Business Context

/ 5

Want to talk through your score?

Book a free 30-minute MMM Strategy Session. We'll look at your specific situation, tell you honestly what's possible, and give you a clear next step — whether that's working with us or not.

Book a Free Strategy Call

No pitch. No pressure. Just an honest assessment of where you stand.

MMM ReadinessChecklist

Your Readiness Score

Want to talk through your score?

MMM Readiness
Checklist