MMM and
Measurement

Platform-reported ROAS systematically over-credits paid channels. Most attribution stacks lean on a single vendor, whether Triple Whale, Northbeam, or platform attribution, and each carries its own bias. Brands with meaningful media budgets need triangulation to reach the truth about what is actually driving incremental revenue.

Without triangulation, brands make budget decisions on biased data. The result is either overspending on channels that look good in platform reporting but are not actually incremental, or underspending on channels that are working but undercounted. Both are expensive.

Three independent measurement layers, cross-validated against each other.

First, triangulation MMM: three open-source and platform models running in parallel. Meta Robyn, a custom PyMC Bayesian implementation, and Google Meridian. Each model has known biases. Running three at once and cross-validating surfaces what any single model would miss.

Second, multi-vendor incrementality: Triple Whale, Northbeam, Recast for Bayesian geo-MMM, and Morpheus for additional incrementality testing. Each vendor rests on different methodology assumptions. Using several in tandem shows where they agree, which is high confidence, and where they disagree, which is where investigation belongs.

Third, custom executive dashboards in Looker Studio that translate channel spend into P&L language: CAC, payback, AOV, LTV, MER, contribution margin. Reporting built to drive business decisions rather than vanity metrics.

Measurement is often one piece of a wider growth mandate. When it is, a Fractional Growth engagement can fold the MMM build into broader operating ownership.

This triangulation stack is not theoretical. It has been built and run in-house against real revenue, where cross-validating several models in parallel consistently surfaces budget that platform reporting misallocates. The efficiency gains come from reallocating away from channels that platform reporting overstates and toward channels that triangulation shows are genuinely incremental.

The methodology draws on more than a decade of senior in-house and agency experience across a portfolio of 250+ consumer brands.

• 01

  MMM Build Project

  Four to six weeks. Build the triangulation framework from scratch, including data pipeline, model orchestration, and the dashboard layer. The outcome is an operational MMM with documented methodology and an internal team trained to maintain it.

• 02

  Measurement Audit

  Two to three weeks. Assess the current attribution stack, identify gaps, and deliver recommendations on what to keep, change, or add. The outcome is a written audit and a prioritized recommendation roadmap.

• 03

  Ongoing Advisory

  Monthly retainer. Quarterly recalibration of MMM models, ongoing optimization of the incrementality testing program, and advisory on budget reallocation. The outcome is continuous improvement of the measurement architecture.

Pricing is scoped to each engagement rather than sold in fixed tiers. Every engagement begins with a free initial consultation, where we define the work and the cost together.

Mid-market consumer brands with meaningful media budgets. Teams that already use Triple Whale or Northbeam but want triangulation. Companies that have outgrown platform-reported ROAS and need to make budget decisions on actual incrementality.

The following are representative scenarios. They illustrate typical scope and outcome shape, not actual client work. Real case studies will replace them as engagements progress.

• A mid-market consumer brand with existing Triple Whale attribution. Scope: a five-week triangulation MMM build with Meta Robyn, PyMC, and Google Meridian running in parallel, plus geo-MMM via Recast. Outcome shape: identification of roughly 20 to 30 percent reallocatable spend, moving from over-credited prospecting toward under-credited retention channels and Amazon DSP.

• A larger consumer brand running multi-vendor measurement, with Triple Whale and Northbeam producing conflicting outputs. Scope: a three-week measurement audit comparing vendor methodologies, finding the root causes of variance, and delivering a recommendation roadmap. Outcome shape: clear guidance on which vendor to weight more heavily per channel, and which channels need further incrementality testing.

• An established consumer brand seeking ongoing advisory. Scope: a monthly retainer covering quarterly MMM recalibration, incrementality testing oversight, and budget reallocation advisory. Outcome shape: steady improvement of measurement architecture and budget efficiency across a twelve-month relationship.

Do you require a specific data warehouse or stack?

No. The methodology works with standard ad platform data (Meta, Google, TikTok), web analytics (GA4), and ecommerce platform data (Shopify, Amazon). Custom data warehouse work is included where it is needed.

How long does the MMM build take to show results?

The build itself runs four to six weeks. Initial budget reallocation insights usually emerge within the first two to three weeks of model calibration. Full results from reallocated spend typically take a full quarter to validate.

Do you train an internal team?

Yes. Knowledge transfer is part of every build engagement. The framework should run without external dependency after handoff.

How is this different from what Triple Whale or Northbeam already give me?

Those are single-vendor attribution tools, each with its own bias. Triangulation runs several independent models and vendors in parallel and cross-validates them, so you see where they agree, which is high confidence, and where they diverge, which is worth investigating. The point is not to replace your vendor but to know when to trust it.

What does an engagement need from our team?

Read access to ad platform, analytics, and ecommerce data, plus a few hours from whoever owns reporting. The modeling and dashboard work happens on our side; your team is involved at kickoff, a mid-point review, and handoff.