MMM feels out of reach for your team size.
Everything you read assumes huge budgets, perfect data, and in-house data scientists — none of which you actually have.
Analyze Pillar · Media Mix Modeling
Media mix modeling that doesn't need a PhD to explain.
This program gives mid-market teams a realistic way to understand how search, social, brand, and upper-funnel work together — without pretending you have Netflix-level data or a full-time data science team.
- Use historical data to understand channel contribution.
- Model scenarios for shifting spend across major channels.
- Turn outputs into simple guardrails your media team can actually use.
Designed for budgets big enough to matter, but not big enough to fund an internal econometrics team.
Example View · Channel Contribution & Scenarios
Baseline contribution
- Modeled revenue contribution by channel.
- Incremental vs non-incremental patterns.
- Sensitivity to spend changes.
Scenario planning
- "What if we pull 20% from Brand and push to Search?"
- "What happens if we cut Meta by 30%?"
- "How much should we invest in new channels?"
Output: Practical Guardrails
Not just charts — downstream guidance like "don't take paid social below X% of total spend if you want to protect demand."
When "what should our mix be?" is guesswork
MMM doesn't need to be perfect to be useful. It just needs to be better than vibes.
You don't need academic-level models to make better mix decisions. You need a rigorous, explainable way to tie historical spend to outcomes — and a way to answer "what if" questions with something more than opinion.
Brand and upper-funnel are always on trial.
When last-click and platform views dominate, it’s hard to justify continued investment in channels that don’t show clean direct response.
Planning happens in silos.
Search, social, brand, and offline efforts all plan in their own tools with no shared view of how they work together.
You’re making big mix decisions on gut.
Leadership wants to know: "What if we shifted 15–20% out of X into Y?" Right now, those answers are mostly opinion.
Our approach to media mix modeling
Inputs, model, decisions — in that order.
We're not chasing the fanciest algorithm. We're chasing a model that your leadership and media teams can understand and act on — and that can be updated as you collect more data.
Data
Get the inputs good enough to model on.
We start with the data you actually have — platform spend, GA4 or analytics, and (where available) revenue or high-quality proxy metrics.
- Audit and clean core inputs rather than chasing perfection.
- Align metric definitions with finance and growth.
- Clarify what we can and can’t reasonably answer.
Model
Apply modeling that fits your scale and reality.
We use practical, transparent methods (often Bayesian or regression-based) that balance rigor with interpretability.
- Model channel-level contribution and sensitivities.
- Account for seasonality, promotions, and major events.
- Quantify uncertainty so nobody over-believes the charts.
Decisions
Turn results into guardrails, not gospel.
We package outputs into clear guidance for planners and buyers — where to avoid cutting too deep and where there’s room to experiment.
- Channel mix guardrails and recommended ranges.
- Scenarios for shifts up/down by channel or cluster.
- Guidance on where to use MMM vs where to use attribution.
A pragmatic, staged MMM engagement
Start with a useful model. Earn your way to a great one.
We treat MMM as something you grow into — not a one-off project that drops a static model in your lap and disappears.
Phase 1
1Scoping & feasibility
Clarify objectives, data availability, and constraints. Decide what questions MMM should answer for your team in the next 3–6 months.
Phase 2
2Data prep & first model
Prepare data, build initial models, and pressure-test results with your growth and finance stakeholders.
Phase 3
3Scenario planning & guardrails
Run key mix scenarios, agree on guardrails, and embed outputs into planning, not just reporting.
Phase 4
4Iteration & enablement
Optional: ongoing refresh cadence and support as you collect more data and refine your mix.
Analyze → Optimize
MMM shouldn't replace attribution. It should complement it.
We position media mix modeling as a higher-altitude view, layered on top of channel- and campaign-level measurement. Together, they're how you decide both where to invest and how to execute.
Who this is for
- Paid media and growth leaders managing multi-channel spend in the low-to-mid seven figures or more.
- Teams that have already invested in basic measurement and want a more strategic view of mix.
- Leadership that wants something better than "we'll see" when shifting large chunks of budget across channels.
If you're still fighting basic tracking fires daily, start with GA4 / GTM and attribution work first. MMM lands best on top of a stable measurement foundation.
Next step
Let's talk about whether MMM is the right next move.
Tell us roughly what your current mix looks like and what decisions are hardest today. We'll use the session to assess readiness and recommend whether MMM should be now, later, or not at all.
The intake form below routes to our senior team; we'll be straightforward if something simpler will give you more leverage first.