Most brands that want Media Mix Modeling face the same two options: pay a vendor tens of thousands of dollars per year for a black-box dashboard, or do nothing and keep guessing.
There's a third option that almost nobody talks about: build it yourself, inside your own environment, with full ownership from day one.
This is what we do at Partners Insights. Here's exactly how it works — every stage, every deliverable, and what you walk away with at the end.
What "In-House MMM" Actually Means
Building MMM in-house doesn't mean your team figures it out alone. It means the model is built in your environment — your cloud platform, your data warehouse, your Python environment — and handed to your team with training, documentation, and full source code.
No vendor subscription. No ongoing access fees. No dependency on a third party to re-run the model or interpret results. The capability lives inside your business permanently.
Our platform, MarketMiX, is built on PyMC-Marketing — the same open-source Bayesian MMM framework developed by contributors at Meta and Google. It uses Geometric Adstock, Hill Saturation curves, and a NUTS sampler via JAX/Numpyro for fast, accurate inference. Here's what a full build looks like from start to finish.
The Six Stages of an In-House MMM Build
Stage 1 — Weeks 1–2
Data Audit & Preparation
Everything starts with data. Before a single line of code is written, we audit what you have and structure it correctly.
- Minimum requirement: 104 weeks (2 years) of weekly data across all channels and revenue/sales output. More history = more reliable decompositions.
- Channels collected: Spend, impressions, or GRPs by channel — paid search, paid social, TV, radio, OOH, email, display, influencer.
- Business outputs: Weekly revenue, units sold, or leads generated (depending on your model objective).
- Covariates: Pricing data, promotions, seasonality indicators, economic indices, and any major external events.
We build the data pipeline in your environment and document every transformation so your team can reproduce it independently.
Stage 2 — Week 2
Model Configuration
MarketMiX uses a YAML configuration file that defines every model parameter — channel variables, adstock decay rates, saturation curve priors, and the target variable. We configure this collaboratively with your team.
- Adstock lag lengths set per channel (e.g., TV typically carries over 4–8 weeks; paid search 0–1 weeks)
- Saturation priors informed by industry benchmarks and your historical spend range
- Holdout validation period defined (typically the last 8–13 weeks of data)
The config file is version-controlled alongside your model code — so every future run is reproducible and auditable.
Stage 3 — Weeks 3–5
Model Training & Validation
The model is trained using Bayesian inference via the NUTS (No-U-Turn Sampler) algorithm, implemented through JAX and Numpyro for speed. This gives you full posterior distributions — not just point estimates, but confidence intervals on every channel's contribution.
- Convergence checks: R-hat values, effective sample sizes, trace plots — all reviewed before results are accepted.
- Out-of-sample validation: The model is tested against the holdout period. We report MAPE and NRMSE to quantify predictive accuracy.
- Posterior predictive checks: Visual verification that the model's simulated data matches observed reality.
If validation metrics fall short, we iterate on priors and configuration before proceeding. You never get results from a model that hasn't earned them.
Stage 4 — Week 5
Contribution Decomposition & Insight Extraction
Once the model passes validation, we extract the insights your team will actually use to make decisions.
- Revenue decomposition: What percentage of revenue came from each channel vs. baseline (what you'd sell with zero marketing spend).
- mROAS by channel: Marginal return on ad spend at current spend levels — tells you where your next dollar works hardest.
- Saturation curves: Where each channel hits diminishing returns. Visually shows when you're overspending.
- Adstock visualisation: The carryover effect of each channel — how long the impact of a campaign lingers after it ends.
Stage 5 — Weeks 6–7
Dashboard Delivery
We deliver two interactive dashboards, both deployed inside your environment:
- MarketMiX Dashboard — Model insights: contribution decomposition, channel efficiency metrics, saturation curves, and model diagnostics. Your team can explore every output without needing to touch the code.
- ResponseOptix — Budget scenario optimiser: input any total budget and constraints, and the tool calculates the allocation across channels that maximises projected revenue. Run unlimited scenarios.
Both dashboards connect directly to your model artifacts (stored as .nc NetCDF files) and update automatically when the model is retrained.
Stage 6 — Week 8
Handover & Capability Transfer
This is what separates an in-house build from a vendor engagement. We don't just deliver outputs — we transfer the capability.
- Training session: Live walkthrough with your team on how to rerun the model, update the data pipeline, and interpret all outputs.
- Full documentation: Model architecture, configuration guide, data dictionary, and retraining schedule recommendations.
- Source code: Everything — pipeline, config, model, dashboards — in your repository. We remove ourselves from the critical path entirely.
At the end of week 8, your team owns a production-grade Bayesian MMM system. No recurring fees. No black box. No dependency on us or anyone else to keep it running.
What You Have at the End
| Deliverable | Description |
|---|---|
| Data pipeline | Automated weekly ingestion and transformation, deployed in your environment |
| Trained model | Bayesian MMM with full posterior distributions, stored as .nc artifacts |
| MarketMiX Dashboard | Interactive model insights: decomposition, mROAS, saturation, diagnostics |
| ResponseOptix | Budget scenario optimiser: unlimited planning simulations |
| Full source code | Pipeline, config, model, and dashboards — all in your version control |
| Documentation | Model architecture guide, data dictionary, retraining playbook |
| Training session | Live handover with your team so they can run and update everything independently |
How Long Does It Take?
A full In-House MMM Build typically takes 6–8 weeks from kick-off to handover. The main factors that affect timeline are data readiness (the cleaner your historical data, the faster we move) and the number of channels in scope.
We've completed builds in as little as five weeks for brands with well-organised data. Brands with fragmented data from multiple agencies sometimes need an extra week at the front end for data consolidation.
Who Is This Right For?
An in-house MMM build is the right call if:
- You're spending $1M+ per year across 4+ marketing channels
- You want to make better budget decisions without depending on a vendor forever
- You have (or plan to hire) a data analyst or marketing scientist who can own the model
- You're frustrated with the black-box nature of SaaS MMM tools
- Privacy changes (iOS 14, cookie deprecation) have undermined your current attribution
"At the end of engagement, the model is yours. We train your team to run it, update it, and build on it — independently. That's the whole point."
If you're not sure whether your data is ready or your team has the capacity to own a model, we offer a free MMM Strategy Session — a 45-minute call where we look at your current setup and give you an honest assessment of what an in-house build would look like for your business.
Ready to Own Your Marketing Measurement?
Book a free 45-minute MMM Strategy Session. We'll review your data, your channels, and your team setup — and give you a clear picture of what an in-house build would look like.
Book a Free Strategy Session