Attribution Models & Types:
What Is Linear Attribution?
Linear attribution is a simple, rule-based method that gives equal credit to every touchpoint in a buyer’s journey. It helps teams understand collective influence across channels, content, and interactions without favoring any specific moment.
Linear attribution assigns the same percentage of credit to every marketing and sales interaction that happens before a conversion, opportunity, or closed-won deal. If there are five touches, each receives 20%. If there are twelve touches, each receives roughly 8.3%. This makes linear one of the most transparent and easy-to-explain attribution models—ideal for showing the combined impact of nurturing, awareness, and mid-funnel engagement.
Key Principles Of Linear Attribution
How Linear Attribution Works Step By Step
A clear process to define touch eligibility, calculate credit, and operationalize insights.
Implementation Steps
- Define valid touches — Identify which interactions count: clicks, form fills, content views, meetings, calls, or ads.
- Set journey boundaries — Determine whether the model applies pre-opportunity, pre-pipeline, pre-conversion, or full lifecycle.
- Group touches by contact or account — In B2B, roll up interactions across buying groups to represent one account journey.
- Apply equal credit distribution — For N touches, assign 1/N of the credit to each touchpoint.
- Integrate into reporting — Add linear attribution metrics to dashboards, funnel views, QBRs, and optimization plans.
- Compare with other models — Use linear alongside time-decay or position-based models to reveal deeper patterns.
How Linear Compares To Other Attribution Models
| Model | Credit Weighting | Best For | Advantages | Limitations |
|---|---|---|---|---|
| Linear Attribution | Equal credit across all touches | Nurture-heavy journeys, multi-channel engagement | Simple, transparent, highlights collaboration | Doesn’t show which touches matter most |
| First-Touch | 100% to first interaction | Awareness insights, early-stage motions | Clear and directional | Ignores nurturing and conversion steps |
| Last-Touch | 100% to last interaction | Conversion analysis | Easy to track, aligns with sales moments | Misses early influence and assists |
| Time-Decay | More weight to recent touches | Long journeys with late-stage urgency | Values recency and activation | May undervalue early discovery |
| W-Shaped | Weights for first, lead-create, opp-create | Milestone-based B2B journeys | Balances discovery and conversion | Assumes fixed milestones |
Client Snapshot: Linear + Time-Decay Accelerates Clarity
A B2B SaaS company adopted linear attribution to show how nurturing programs drove engagement across accounts. By comparing linear with a time-decay model, they identified high-influence touches that were previously hidden. The result: tighter targeting, smarter sequencing, and a 19% lift in pipeline efficiency across two quarters.
Linear attribution becomes far more powerful when paired with multi-model insight that reflects real buying behavior and connects directly to investment decisions.
FAQ: Linear Attribution Explained
Concise answers for leaders, ops teams, and analytics reviewers.
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