MMM Service Definition
For Digital Marketing Agencies Offering MMM to Small and Medium-Sized Clients
Powered by MMM Pilot
Marketing Mix Modeling has long been the exclusive preserve of enterprise brands — guarded by six-figure consulting fees, data science teams, and months of turnaround time. That barrier no longer exists.
With MMM Pilot, you can now offer your clients a rigorous, data-backed picture of what is actually driving their revenue — and what is quietly wasting their budget.
1. Who This Service Is For
Ideal Client Profile:
- Small-to-medium businesses (SMEs) — e-commerce, retail, B2B services, or hospitality.
- Monthly paid media spend between €7,500 and €150,000 across two or more channels.
- Currently measuring performance through platform-reported metrics (Google Ads ROAS, Meta Ads Manager) — without an independent view of what is actually working.
- No in-house data science capability. Decision-making power sits with the Marketing Manager, CMO, or a hands-on business owner.
Their Core Pain Point:
They run Google Search, Meta, and maybe email or OOH. Each platform claims credit for every sale. No one can tell them which one is actually generating incremental growth — or which one has quietly stopped pulling its weight.
2. The Service: What You Deliver
An MMM Analysis uses your client’s historical sales and spend data to build a statistical model of their marketing universe. The model isolates what is driving their revenue — advertising, seasonality, promotions, or organic demand — channel by channel.
The output is a clear answer to the question every client is asking: “Where should our budget go?”
Core outputs include:
- Contribution Breakdown: How much of total revenue each channel (and organic base) is responsible for.
- Spend Efficiency curves: Where each channel delivers strong returns — and where it starts hitting diminishing returns.
- Carry-Over Effect: How long each channel’s influence lingers after an ad is seen.
- Budget Optimization: A recommended budget reallocation based on each channel’s efficiency curve, with a projected revenue impact.
The analysis is built on Robyn, Meta’s open-source marketing mix modeling framework — providing statistical rigor your clients can trust and you can stand behind.
3. Service Tiers and Commercial Models
Because MMM Pilot keeps tooling costs low (starting at $105/month for agency use), you can make MMM accessible to clients who could never have justified the traditional price of entry.
Tier 1: The Flight Plan (One-Time Engagement)
The right starting point for any client who has never done MMM before.
Recommended price: €1,500 – €2,500
Adjust based on data complexity, number of channels, and required presentation depth.
What’s included:
- Data intake guidance — templates, instructions, and a clear checklist to extract historical data with minimal client effort.
- Impact Analysis covering up to 5 media channels, spanning 12–24 months of history.
- Contribution Breakdown, Spend Efficiency analysis, and Carry-Over Effects per channel.
- A Budget Optimization recommendation for the next planning cycle.
- Executive Summary report.
- A 60-minute Strategy Session to walk through findings and agree on next steps.
Tier 2: The Co-Pilot (Ongoing Retainer)
For clients who want to keep steering — not just check the map once.
Recommended price: €400 – €800/month
Model refreshed quarterly; monthly strategy check-ins included.
What’s included:
- Quarterly model refresh incorporating new spend and sales data.
- Budget reallocation recommendations ahead of each new campaign cycle.
- Scenario planning for budget increases, seasonal shifts, or new channel additions.
- Priority access to scheduling and analysis turnaround.
For agencies: The Starter tier is your entry point. The Co-Pilot retainer is your retention engine. And the Gain-Share model is where the real upside lives — particularly for clients with media budgets above €30,000/month.
Alternative Commercial Model: Gain-Share Fee
For clients with significant media spend where MMM-driven optimizations unlock measurable savings.
This is an alternative to — or an addition to — the flat-fee tiers above. Rather than charging a fixed price, you agree with the client to take a percentage of the verified budget savings generated by acting on the MMM recommendations.
How it works:
- Establish a baseline: the client’s current media spend and the revenue it generates, measured over the most recent quarter.
- Deliver the MMM Analysis and agree on a set of budget reallocation recommendations.
- The client implements the recommendations over the following quarter.
- At the end of the period, measure the outcome: did the client achieve the same (or better) revenue with less spend?
- Calculate the verified saving — the reduction in media spend with equivalent or improved revenue performance.
- Apply the agreed gain-share rate to that saving.
Suggested gain-share rate: 15–25% of verified savings
Agree in advance on the measurement methodology, the baseline period, and any factors that would invalidate the comparison — e.g., a major external market event.
Why this model makes sense for larger clients:
| Client Monthly Spend | Efficiency Gain | Monthly Saving | Your Fee (20%) |
|---|---|---|---|
| €30,000 | 10% | €3,000 | €600/month |
| €60,000 | 10% | €6,000 | €1,200/month |
| €100,000 | 12% | €12,000 | €2,400/month |
| €150,000 | 15% | €22,500 | €4,500/month |
For a client spending €100,000/month, a 12% efficiency improvement — fully achievable when MMM reveals a structurally over-invested channel — generates a €2,400/month fee on top of your retainer, with zero additional delivery work. The model runs on a quarterly refresh cycle regardless.
Important: The figures above are illustrative only. Actual savings will depend on the quality of the data, the client’s ability to implement the recommendations, and external market conditions that the model cannot predict. The gain-share fee should always be calculated against verified, agreed outcomes — never against projected figures alone.
What protects both parties:
- Define “verified saving” precisely in the contract: same KPIs, same measurement window, agreed attribution of causality.
- Cap the gain-share period (e.g., 6 months) so the model does not create open-ended obligations for the client.
- Include a minimum floor: the gain-share only applies if savings exceed a defined threshold (e.g., ≥ 5% of baseline spend), preventing disputes over marginal fluctuations.
- Run the gain-share alongside a Co-Pilot retainer, not instead of it — the retainer ensures the model stays current and the relationship stays active.
For agencies: The gain-share model repositions you from a service provider to a performance partner. It aligns your incentives with your client’s — and it makes the conversation about ROI rather than cost. For clients with media budgets above €50,000/month, this framing often makes MMM much easier to sell.
4. Scope of Service
In Scope
- Data collection guidance (templates and instructions provided)
- Historical data preparation and validation
- Full MMM run — Contribution Breakdown, channel ROI, Spend Efficiency, Carry-Over Effects
- Budget Optimization scenarios
- Executive Summary and recommendations report
- Findings presentation (remote, 60–90 minutes)
Out of Scope
- Campaign execution or media buying
- Real-time or campaign-level attribution
- User-level tracking or click-path analysis
- Data engineering or API integrations to client systems
- B2B companies with sales cycles exceeding three months (MMM’s predictive value is limited in these cases — be transparent about this upfront)
5. Data Requirements
The analysis requires your client to provide:
Essential:
- Weekly or monthly revenue / sales volume — minimum 12 months, ideally 24.
- Marketing spend per channel per period.
- Promotional calendar (discounts, seasonal campaigns, product launches).
Recommended (improves model accuracy):
- Pricing history (if prices have changed)
- Competitor activity (if tracked — Google Trends data can serve as a useful proxy)
Minimum viability threshold: At least 12 months of weekly data, spend across two or more active channels, and a defined revenue metric. If a prospective client falls below this, communicate it clearly and propose a data audit as a preliminary step.
6. Honest Limitations (Because Transparency Builds Trust)
Set expectations correctly from the first meeting:
- MMM is backward-looking. It tells you what drove results historically, not what will guarantee results tomorrow.
- It works with aggregated data — it cannot tell you which individual user converted and why.
- Its accuracy depends directly on the quality and completeness of the data you put in.
- It is a strategic planning input, not a real-time bidding tool.
On implementation and outcomes: Even when MMM recommendations are implemented correctly, actual results may differ from projections. Marketing performance is shaped by many factors the model cannot fully anticipate — competitor activity, macroeconomic shifts, seasonal anomalies, changes in consumer behaviour, and more. The model provides directional guidance based on historical patterns; it does not eliminate uncertainty or guarantee a specific outcome. Agencies should communicate this clearly to clients before any engagement begins.
On model accuracy: In some cases, the model may not achieve the minimum accuracy threshold (R² ≥ 0.75) — due to data quality issues or structural characteristics of the client’s business. When this occurs, the engagement should not proceed to a standard deliverable without an explicit conversation about what can be delivered and at what confidence level. See the Handling Low Model Accuracy guide for the full decision framework, including delivery options and the commercial remedies available to both parties.
Clients who understand these boundaries from the start are far more satisfied — and far more likely to renew.
7. Success Indicators
| Metric | Target |
|---|---|
| Model fit (R²) | ≥ 0.75 |
| Data completeness | ≥ 80% of required data points |
| At least one budget reallocation recommendation | Per channel analyzed |
| Client comprehension of findings | Decision-makers can summarize the top 3 insights without prompting |
| Client satisfaction | ≥ 8/10 post-delivery survey |
| Starter → Co-Pilot conversion | Target: 40% of Starter clients |