Revenue Attribution
Connecting revenue to the marketing and sales activities that influenced it. First-touch, last-touch, and multi-touch models all have flaws. The real challenge isn't the model — it's getting agreement between marketing and sales on what actually drove the deal.
Attribution is the most argued-about topic in marketing for good reason: careers and budgets depend on the answer.
First-touch says the blog post that generated the lead gets credit. Last-touch says the sales email before close gets it. Multi-touch spreads credit across touchpoints. They're all wrong and all right.
The dirty secret: most attribution is garbage because the data is garbage. Missing UTMs, incorrect source mapping, offline touchpoints ignored, sales touches not tracked. You're debating models built on incomplete data.
The RevOps angle: stop arguing about the perfect model. Start with clean data capture. Then pick a model that's directionally useful and consistent. Imperfect but consistent beats perfect but inconsistent.
Define ItOther Definitions
“Revenue attribution connects marketing touchpoints to pipeline and revenue, enabling data-driven budget allocation. Multi-touch attribution distributes credit across the full buyer journey.”
“Marketing attribution determines which campaigns and channels contributed to conversions and revenue. Models range from simple (first/last touch) to complex (algorithmic multi-touch).”
“Attribution modeling assigns credit to touchpoints in the conversion path. Different models — first interaction, last interaction, linear, time decay, position-based — distribute credit differently.”
Revenue attribution traces which activities influenced deals and revenue. Bizible (Adobe) emphasizes multi-touch for budget optimization. Salesforce acknowledges the spectrum from simple to algorithmic. Google Analytics outlines the standard models.
Common attribution models: (1) First-touch — first interaction gets 100% credit; (2) Last-touch — final interaction before conversion gets credit; (3) Linear — equal credit across all touchpoints; (4) Time-decay — more credit to recent touches; (5) Position-based (U-shaped) — first and last get 40% each, middle shares 20%; (6) Algorithmic/data-driven — ML assigns credit based on actual impact.
The choice depends on goals: brand awareness metrics favor first-touch; sales efficiency favors last-touch; understanding full journeys needs multi-touch.
MistakesCommon Mistakes
Debating models before fixing data capture
Ignoring offline touchpoints (events, phone calls, referrals)
Different teams using different models without alignment
Attribution only for marketing — not including sales touches
Over-indexing on attributed channels while ignoring brand lift
Knowing the definition won't fix the leak.
We find where your revenue engine is broken and show you exactly how to fix it. 6 weeks. Fixed scope.
Experience across
