Definition
Position-based attribution assigns most credit to the first and last touchpoints, with the remaining credit spread across the middle touches.
Example
A 40/40/20 model gives 40% to first touch, 40% to last touch, and 20% to the middle steps.
How to use it
- Use when you believe first touch creates demand and last touch captures it.
- Be explicit about the rule (for example 40/40/20) to keep comparisons stable.
- Validate with lift tests when budget decisions are material.
Common mistakes
- Changing the split across reports and breaking trend comparisons.
- Using it as a causal truth without incrementality checks.
Why this matters
This term matters because it affects how you interpret performance and make budget decisions. If you use inconsistent definitions or windows, ROAS/CPA can look "better" while profit gets worse.
Practical checklist
- Write a 1-line definition for "Position-based Attribution (U-shaped)" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Sanity-check with a related calculator from the same category on MetricKit.
- Read the related guide (e.g., Attribution vs incrementality: what to trust, when, and how to test) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- Break-even ROAS Calculator: Estimate the break-even ROAS based on contribution margin assumptions.
- Target ROAS Calculator: Estimate a target ROAS to cover variable costs plus a desired margin buffer.
- Paid Ads Funnel Calculator: Model CPM -> CTR -> CVR to estimate CPC, CPA, ROAS, and profit per 1,000 impressions (with margin and variable costs).
- ROI Calculator: Calculate Return on Investment (ROI) for a campaign or project.
- Incrementality Lift Calculator: Estimate incremental conversions, incremental ROAS, and incremental profit from a holdout test.
Guides
- Attribution vs incrementality: what to trust, when, and how to test: A practical guide to attribution vs incrementality: common attribution models, window pitfalls, how MER/marginal ROAS fit in, and how to run holdout/geo tests.