Attribution model
Also: Attribution, Marketing attribution, Multi-touch attribution, MTA, Credit assignment, Modele d'attribution, Attribution marketing
A framework for assigning credit to the touchpoints that contributed to a conversion, so you can measure which channels and interactions actually drive results.
What it is
An attribution model is a set of rules (or a statistical method) that decides how much credit each marketing touchpoint receives for a conversion. A conversion can be a purchase, a signup, a qualified lead, or any outcome you care about. Because customers rarely convert after a single interaction, attribution answers a practical question: of all the ads, emails, searches, and visits that preceded this outcome, which ones deserve the credit, and how much?
Why it matters
Budgets and decisions follow credit. If a channel is under-credited, it gets defunded even when it drives value; if it is over-credited, spend is wasted. Attribution turns a messy customer journey into numbers you can act on.
- Budget allocation: move spend toward touchpoints that genuinely influence conversions.
- Channel evaluation: compare paid search, social, email, and organic on a consistent basis.
- Accountability: connect marketing activity to revenue and pipeline.
Common model types
- Last-touch: 100% credit to the final touchpoint. Simple but ignores earlier influence.
- First-touch: 100% credit to the first touchpoint. Good for measuring demand generation.
- Linear: equal credit to every touchpoint.
- Time-decay: more credit to touchpoints closer to conversion.
- Position-based (U-shaped): heavy credit to first and last, the rest shared.
- Data-driven (algorithmic): uses statistical or machine learning methods (for example Shapley values or Markov chains) to estimate each touchpoint's marginal contribution from observed data.
How it is used in practice
Teams pick a model that matches their sales cycle and data quality, wire it into analytics or a marketing platform, then review reports by channel and campaign. Rule-based models are transparent and easy to explain. Data-driven models are more accurate but need clean, well-joined event data and careful validation. Attribution is correlational, not causal, so mature teams pair it with incrementality tests (holdouts, geo experiments) to confirm true lift.
Worked example
A customer journey to a 200 EUR purchase:
1. Clicks a paid search ad
2. Reads a blog post (organic)
3. Clicks an email, then buys
How the 200 EUR is credited:
- Last-touch: Email 200, others 0.
- First-touch: Paid search 200, others 0.
- Linear: each channel about 67.
- Position-based (40/20/40): Paid search 80, Blog 40, Email 80.
Same journey, four very different stories. Choosing the model is a business decision, not just a technical one.
See also
Frequently asked questions
What is an attribution model?
An attribution model is a set of rules or a statistical method that decides how much credit each marketing touchpoint receives for a conversion, whether that conversion is a purchase, a signup, or a qualified lead. Because customers rarely convert after a single interaction, the model determines which ads, emails, searches, and visits get credited and by how much. Budgets tend to follow that credit, so the choice of model directly shapes spending decisions.
What is the difference between first-touch, last-touch and multi-touch attribution?
Last-touch gives 100% of the credit to the final interaction, first-touch gives 100% to the initial one, and multi-touch spreads credit across the whole journey. Multi-touch itself comes in several forms: linear (equal credit everywhere), time-decay (more credit near the conversion), and position-based or U-shaped (typically 40/20/40 between first, middle and last). Single-touch models are simpler to explain but hide the influence of everything in between.
How do I choose which attribution model to use?
Match the model to your sales cycle and the quality of your data. Rule-based models like last-touch or position-based are transparent and easy to defend internally, which matters when several teams argue over the same budget. Data-driven models are more accurate but require clean, well-joined event data and careful validation, so they are not a starting point for a team whose tracking is still incomplete.
Can an attribution model prove that a channel caused the sales?
No. Attribution is correlational, not causal: it describes which touchpoints appeared in journeys that converted, not what would have happened without them. Mature teams therefore pair attribution reports with incrementality tests such as holdout groups or geo experiments to confirm real lift before reallocating budget.
Concretely, how much does the model change the numbers on the same journey?
Enough to reverse a budget decision. Take a 200 EUR purchase preceded by a paid search click, a blog visit, then an email click: last-touch credits the email with the full 200 and the rest with zero, first-touch credits paid search with 200, linear gives each channel about 67, and a 40/20/40 position-based model gives 80 to paid search, 40 to the blog and 80 to the email. Same journey, four different conclusions about which channel to fund.