ATTRIBUTION THAT TELLS THE TRUTH

Quick Answer: NUUN Digital builds marketing attribution stacks that triangulate MMM, MTA, incrementality testing, and self-reported attribution into one consistent view of what drives revenue — not what platforms self-report. Attribution stops being a vendor argument and starts being a planning tool.

WHAT WE DELIVER

Marketing Mix Modeling (MMM). Statistical attribution of revenue to marketing activities.
Multi-Touch Attribution (MTA). Digital journey attribution across platforms.
Incrementality testing. Geo-holdouts, conversion lift, and clean-room experiments.
Unified Marketing Measurement (UMM). Triangulated MMM + MTA + incrementality.
Budget allocation optimization. Simulation-based spend reallocation.
Attribution governance. Methodology documentation, update cadence, and approval process.

HOW WE DO IT

Audit current attribution. What's claimed, what's verified, where the gaps are.
Design the triangulation stack. MMM + MTA + incrementality — each for what it's good at.
Build models. Bayesian MMM, rule-based or algorithmic MTA, controlled incrementality tests.
Validate with experiments. Geo holdouts prove model accuracy and calibrate drift.
Install for decisions. Budget planning, channel reallocation, creative investment.

WHEN IT FITS

Attribution disagreements between channels, agencies, or teams.
Large marketing budgets where misallocation has meaningful impact.
Privacy-related loss of tracking (iOS 14+, cookie deprecation) requiring modelling.
CFO or board scrutiny requiring defensible marketing accountability.

SELECTED WORK

Confidential retailer — MMM + MTA → reallocated spend → revenue up [X]%, ROAS up [X]%. Read case →
Financial services client — Incrementality testing revealed [X]% of platform-claimed conversions were organic. Read case →

SOURCES & FURTHER READING

Data & Analytics practice
Business intelligence and dashboarding
Predictive analytics
MASB marketing accountability standards — https://themasb.org/
Google Meridian — https://github.com/google/meridian

Frequently asked.

MMM, MTA, or both?

Both. MMM captures long-horizon and offline effects; MTA captures digital journey detail. Used together with incrementality testing, they triangulate to truth. Running one alone produces partial views.

How much data history do I need for MMM?

Typically 2–3 years of weekly data for stable model estimation. Shorter histories work for fast-moving categories; longer histories stabilize seasonal and annual effects.

What tools do you build on?

Meridian (open-source MMM), Robyn (Meta), custom Bayesian MMM on pymc or Stan, Hightouch for data integration. Platform-neutral; we pick what fits your data and scale.

How often should we refresh MMM?

Quarterly refresh baseline; monthly updates for fast-cycle businesses. Models that aren't refreshed drift and lose predictive value.

Can attribution capture the effect of brand media?

MMM captures brand effects where traffic and sales lag-patterns show them. MTA is poorly suited to brand attribution. Incrementality testing (brand lift studies) is the cleanest method for brand-specific attribution.