Attribution Modeling for Growth Teams
Who gets credit for the conversion? Understanding attribution models helps you allocate budget and understand what's actually working.
When a user converts, which touchpoint gets credit? The first ad they saw? The blog post they read? The retargeting ad they clicked? Attribution modeling answers this question — and the answer shapes how you allocate your growth budget.
Why Attribution Matters
If you don't know what's working, you can't double down on it. Wrong attribution leads to:
- Over-investing in low-value channels
- Under-investing in channels that actually work
- Inability to calculate true CAC by channel
- Bad budget allocation decisions
The Main Attribution Models
Last Click
100% credit to the last touchpoint before conversion.
Pros: Simple. Easy to implement. Cons: Ignores everything that came before. Over-credits bottom-funnel.
First Click
100% credit to the first touchpoint that acquired the user.
Pros: Values awareness and top-of-funnel. Cons: Ignores nurturing and conversion touchpoints.
Linear
Equal credit across all touchpoints.
Pros: Recognizes full journey. Cons: Doesn't reflect varying impact of touchpoints.
Time Decay
More credit to touchpoints closer to conversion.
Pros: Balances journey while emphasizing recency. Cons: May still undervalue awareness.
Position-Based (U-Shaped)
40% to first touch, 40% to last touch, 20% distributed among middle.
Pros: Recognizes importance of acquisition and conversion. Cons: Arbitrary weights.
Data-Driven (Algorithmic)
Machine learning determines credit based on actual conversion patterns.
Pros: Reflects true impact. Cons: Requires significant data. Black box.
Choosing the Right Model
There's no universally correct model. Choose based on your business:
| Business Type | Recommended Model |
|---|---|
| Long sales cycle | Position-based or time decay |
| Short sales cycle | Last click is often fine |
| Content-heavy | First click or position-based |
| Paid-heavy | Data-driven or linear |
| Early stage | Start simple, add complexity |
Multi-Touch Attribution in Practice
Setting It Up
- Track all touchpoints: Every ad click, page view, email open, demo request
- Stitch identity: Connect anonymous to known users
- Define conversions: What counts as a conversion?
- Choose time windows: How far back do you look?
- Select model: Start with position-based, evolve to data-driven
Tools for Attribution
- Google Analytics 4: Built-in models, limited customization
- Segment + Warehouse: Build your own in SQL
- Attribution tools: Rockerbox, Triple Whale, Northbeam
- Marketing platforms: HubSpot, Marketo have built-in attribution
The iOS 14+ Problem
Post-ATT, mobile attribution is harder:
- Less deterministic matching
- More reliance on modeled conversions
- SKAdNetwork limitations
Adapt by:
- Investing in first-party data
- Using probabilistic attribution
- Triangulating with incrementality tests
Incrementality: The Gold Standard
Attribution shows correlation. Incrementality shows causation.
Run holdout tests:
- Split audience randomly
- Show ads to one group, not the other
- Measure conversion lift
Incremental lift = (Treatment conversion rate - Control conversion rate) / Control rate
True CAC = Spend / Incremental conversions
Common Attribution Mistakes
Trusting one model blindly: Look at multiple models and understand the story.
Ignoring offline touchpoints: Podcasts, events, word-of-mouth aren't tracked but matter.
Too short lookback window: B2B journeys can be months long.
Not connecting to revenue: Conversion attribution without revenue attribution is incomplete.
Platform attribution bias: Every platform wants credit. Cross-reference with independent measurement.
Attribution will never be perfect. But having a model — understanding its assumptions, and knowing where it might be wrong — is infinitely better than no model. Use attribution to guide decisions, but validate with incrementality tests before making big bets.