Linear Attribution
A multi-touch attribution model that distributes conversion credit equally across every touchpoint in the customer journey, giving the same weight to the first, middle, and last interactions.
Also known as: equal attribution, even-weight attribution
Why It Matters
Linear attribution is the most democratic multi-touch model. Every touchpoint gets an equal vote, which prevents any single channel from monopolizing credit. This makes it a useful baseline model that reveals how credit shifts compared to single-touch approaches.
The model works well when you genuinely believe every interaction contributes equally to the conversion decision. For some customer journeys - particularly shorter ones with only 2-3 touchpoints - this assumption is reasonable. Each interaction played a role, and there is no strong reason to weight one more than another.
Linear attribution is also valuable as a comparison benchmark. When you analyze the difference between linear results and other models (time-decay, position-based), the differences highlight which channels benefit from recency weighting versus position weighting, helping you understand the role each channel plays in the journey.
Industry Applications
A DTC mattress brand with a typical 3-touchpoint journey (ad click, review site visit, direct return visit) uses linear attribution. Each touchpoint gets 33% credit, which gives their review site partnerships significant attributed value that was invisible under last-touch.
A project management tool uses linear attribution across their 5-touchpoint average B2B journey. The model reveals that documentation pages (which never appear as first or last touch) are present in 60% of conversion paths and receive substantial credit, validating the documentation team's contribution to revenue.
How to Track in KISSmetrics
Select the linear attribution model in KISSmetrics Attribution Reports to see how credit distributes evenly across touchpoints. Compare linear results against first-touch and last-touch to understand how the shift to multi-touch changes your channel rankings. Look for channels that gain significant credit under linear (indicating they play frequent but middle-journey roles) as these are often undervalued.
Common Mistakes
- -Assuming every touchpoint truly contributes equally, which is rarely true - a 2-second accidental pageview should not receive the same credit as a 30-minute webinar
- -Using linear attribution for channels with very different roles (awareness versus conversion) without questioning whether equal weighting makes sense
- -Not filtering out low-quality touchpoints (bot traffic, accidental clicks) before applying the model
- -Treating linear as the definitive multi-touch model rather than one option among several
Pro Tips
- +Use linear attribution as your starting multi-touch model, then graduate to position-based or data-driven models as your analytics maturity grows
- +Compare linear versus last-touch results to quantify how much credit is being redistributed to non-closing channels
- +Apply minimum engagement thresholds before counting something as a touchpoint in the linear model
- +Use linear attribution to make a fair case for channels that consistently appear mid-journey but never get credit under single-touch models
Related Terms
Multi-Touch Attribution
An attribution approach that distributes conversion credit across multiple touchpoints in a customer's journey rather than assigning all credit to a single interaction, reflecting the reality that most conversions involve multiple marketing influences.
Position-Based Attribution
A multi-touch attribution model that assigns the most credit to the first and last touchpoints in the customer journey (typically 40% each) while distributing the remaining credit equally among middle interactions.
Time-Decay Attribution
A multi-touch attribution model that assigns increasing credit to touchpoints closer to the conversion event, based on the assumption that more recent interactions had greater influence on the purchase decision.
Attribution Model
A set of rules or algorithms that determine how credit for conversions and revenue is assigned to the marketing touchpoints in a customer's journey, shaping how channel ROI is measured and budget is allocated.
Data-Driven Attribution
An attribution model that uses machine learning algorithms to analyze actual conversion paths and assign credit to touchpoints based on their measured impact on conversion probability, rather than using predetermined rules.
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