GLOSSARY

Marketing Attribution

Marketing attribution assigns credit for outcomes to touchpoints — rule-based, MTA algorithmic, or MMM aggregate — blended for strategy and tactics.

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Quick answer
Marketing attribution is the practice of assigning credit for revenue or conversions to the marketing touchpoints that influenced them. Rule-based models, multi-touch attribution (MTA), and marketing mix modeling (MMM) each answer the question differently; mature programs triangulate all three with incrementality experiments so spend decisions rest on convergent evidence rather than a single methodology.

WHAT IT IS

Attribution methods fall in three tiers. Rule-based (last-click, first-click, position-based) is transparent but biased. Algorithmic multi-touch attribution (MTA) models credit data-driven using logistic regression, Shapley, or survival models — more accurate on granular touchpoints but compromised by walled-garden opacity and privacy changes (iOS ATT, cookie deprecation). Marketing Mix Modeling (MMM) uses aggregate time-series data to quantify channel elasticity — resilient to privacy changes, slower feedback, board-grade. Modern practice blends MMM for strategy with MTA and incrementality (geo/holdout tests) for tactical decisions.

HOW IT WORKS

The MASB Cross-Platform Measurement Framework and Google's Meridian open-source MMM library both formalize these blends. Attribution is never 'true' — only more or less useful for the next decision.

WHEN TO USE

Invest in attribution when media spend is meaningful, when board scrutiny of marketing ROI is rising, or when platform opacity is undermining existing reporting.

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Related questions.

What is marketing attribution?
Marketing attribution is the practice of assigning credit for revenue or conversions to the marketing touchpoints that influenced them. It answers the CFO's question: which channels, campaigns, and spend levels actually produced the pipeline or sales we closed this quarter?
What are the main attribution approaches?
Rule-based models (first-touch, last-touch, linear, time-decay, position-based), multi-touch attribution (MTA) using statistical or machine-learning models on user-level data, and marketing mix modeling (MMM) using aggregate spend and outcome data. Most mature programs run both MTA and MMM in parallel.
Which attribution model is most accurate?
None in isolation. Post-iOS 14 and cookie deprecation, MTA based on user-level data has eroded. MMM, which operates on aggregate data, has regained prominence. Leading programs triangulate MTA, MMM, and incrementality experiments; no single model is the source of truth.
What does a good attribution program deliver?
A shared view of marketing's contribution to revenue, updated at a cadence the business can act on (typically monthly), with channel-level ROI, marginal return, and recommended spend reallocations. Attribution that lives only in a dashboard — not in the spend decision — has failed.
How does NUUN Digital build attribution?
We build measurement as the architecture for decisions, not for reporting. Every engagement with a spend decision ends with an MMM, an MTA where data allows, and an incrementality program that validates both. Cross-checks are the standard, not a nice-to-have.

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