Attribution Models Compared: First-Touch vs Last-Touch vs Linear vs Time-Decay vs Data-Driven
The same customer journey gets credited completely differently depending on the attribution model you pick. Here is what each model does, where it lies to you, and when to use it.
• First-Touch: credits discovery — flatters awareness channels, hides what closes
• Last-Touch: the platform default — flatters retargeting & branded search, hides what started the journey
• Linear / Time-Decay / Position-Based: rules-based attempts to split credit across the journey
• Data-Driven: algorithmic credit from your own paths — most accurate, but only with clean, high-volume data
• The catch: no model is “true.” Each platform applies its own, so the same sale is counted more than once across Meta, Google, and Shopify
Pros
- • Simple to understand
- • Rewards demand creation
- • Good for top-of-funnel
Cons
- • Ignores everything after discovery
- • Over-credits awareness channels
- • Hides what closes the sale
Best for: Brands focused on net-new demand and awareness
Pros
- • Platform default
- • Easy to report
- • Rewards channels that close
Cons
- • Ignores discovery and nurture
- • Over-credits branded search & retargeting
- • Punishes upper funnel
Best for: Short, simple buying journeys
Pros
- • Acknowledges the full journey
- • No single-channel bias
- • Easy to explain
Cons
- • Treats a banner view like a demo
- • Rarely matches real influence
- • Can flatter weak touchpoints
Best for: Long journeys where every step genuinely matters
Pros
- • Reflects recency of intent
- • Balances funnel stages
- • Sensible default for considered buys
Cons
- • Still under-credits discovery
- • Decay rate is arbitrary
- • Needs full-path data
Best for: Considered purchases with a clear closing run
Pros
- • Rewards both discovery and close
- • Keeps nurture visible
- • Popular compromise
Cons
- • The 40/20/40 split is a guess
- • Mid-funnel under-valued
- • Not data-driven
Best for: Teams that care about both creation and conversion
Pros
- • Based on your actual data
- • Adapts to your funnel
- • Most accurate when fed clean data
Cons
- • Needs volume & full-path signal
- • A black box to most teams
- • Garbage-in, garbage-out
Best for: High-volume brands with reliable tracking
Journey: Google search → Meta retargeting → Email → Purchase (€200)
| Model | Meta | ||
|---|---|---|---|
| First-Touch | €200 | €0 | €0 |
| Last-Touch | €0 | €0 | €200 |
| Linear | €66.67 | €66.67 | €66.67 |
| Position-Based | €80 | €40 | €80 |
Same journey, same €200 — but Google looks like a hero under First-Touch and worthless under Last-Touch. Now multiply this across thousands of orders and you see why two dashboards never agree, and why blended metrics like MER exist.
There is no single “correct” attribution model — only the one that matches your buying journey and the decision you are trying to make. A short, impulse-buy journey survives last-touch; a long, multi-session, considered purchase does not.
The bigger trap is comparing channels across different models. Meta defaults to its own view-through-inclusive model, Google to data-driven, and Shopify to last-non-direct click. Add the dashboards together and you will “sell” far more than you actually did. That is why most operators anchor strategy on a blended, un-double-countable number (MER) and use per-model attribution only for tactical optimisation.
💡 Rule of thumb: pick one model as your internal standard, hold it constant, and reconcile platform numbers against Shopify as your source of truth.