Linear Attribution

Linear Attribution splits conversion credit equally across every touchpoint in the journey. Each interaction gets the same share, regardless of position or timing.

How It Works

Every touchpoint receives an equal slice of total conversion value.

Linear Attribution Example

Customer Journey:

Click on search ad → 25%
Open email newsletter → 25%
Social media post interaction → 25%
Direct website visit and purchase → 25%

Formula:

Credit per touchpoint = Conversion value / Number of touchpoints

Math

For N touchpoints:

Credit per touchpoint = 1/N × 100%

For conversion value V (e.g., $100):

Credit in monetary terms = V/N

Compared to Other Models

Linear AttributionFirst-Touch AttributionLast-Touch AttributionTime-Decay Attribution

Principle: Even split.

First: 25%
Second: 25%
Third: 25%
Last: 25%

Principle: All credit to first.

First: 100%
Others: 0%

Principle: All credit to last.

Last: 100%
Others: 0%

Principle: More credit closer to conversion.

First: 10%
Second: 20%
Third: 30%
Last: 40%

Advantages

Fair Distribution

Linear gives every channel a baseline read, free of position bias. Useful when different specialists own different channels.

Simple

No machine learning. No advanced data integration. Easy to explain and quick to deploy.

Omnichannel Friendly

Omnichannel Benefits

Channel Synergy:

See how channels work together
Map every journey stage
Keep messaging consistent

Avoid Undervaluation:

Top-of-funnel work isn't ignored
Nurturing channels get recognized
Brand awareness gets a fair read

Long Sales Cycles

For B2B and high-value goods with months-long journeys, Linear surfaces the full picture.

Limitations

Influence Differences Get Erased

Not every touch matters equally. A demo or personalized consultation likely beats a random banner click.

Equal Weight Problem

Passive vs Active Interactions:

Habitual email open = 25%
Detailed comparison table review = 25%
Personalized demo = 25%
Random ad click = 25%

Dilution at Scale

Journeys with dozens of touches get sliced into meaningless fractions. The model loses signal.

Ignores Channel Cost

Linear treats an expensive conference and a free blog mention identically. That can misdirect budget.

When to Use It

Multi-channel campaigns with comparable touches: Integrated email, social, content, and paid advertising where every channel nurtures.

B2B with long cycles: Months-long decisions where webinars, materials, and sales touches all add up.

First multi-touch attempt: Teams new to multi-touch, needing a baseline read.

When to Skip It

Avoid Linear Attribution

Short impulse purchases:

E-commerce with 1-2 day cycles
Daily essentials
Time-limited promotions

Complex products with clear hierarchy:

SaaS with awareness, consideration, trial, purchase stages
High-tech B2B
Financial products needing consultation

Setup and Analysis

Data Requirements

Component	Description	Importance
Cross-domain tracking	User tracking across domains	Critical
User identification	Unified User IDs everywhere	Mandatory
Full touch history	Every interaction recorded	Critical
Accurate timestamps	Correct sequence	Important

Key Metrics

graph TD
    A[Linear Attribution Analysis] --> B[Channel Performance]
    A --> C[Journey Analysis]
    A --> D[ROI Optimization]

    B --> B1[Revenue per Channel]
    B --> B2[Cost per Channel]
    B --> B3[Assisted Conversions]

    C --> C1[Average Touchpoints]
    C --> C2[Journey Duration]
    C --> C3[Drop-off Points]

    D --> D1[Channel ROI]
    D --> D2[Budget Reallocation]
    D --> D3[Efficiency Metrics]

Recommendations

1. Document your methodology

Define what counts as a "touch" and how you track it. Critical for consistency.

2. Validate data regularly

Audit attribution windows, exclude bots, verify cross-device accuracy.

3. Segment your analysis

Apply Linear across new vs returning, regions, device types.

Segmentation Ideas

By customer:

B2B vs B2C journeys
High-frequency vs rare buyers
Different price segments

By journey:

Short (1-7 days) vs long (30+ days) cycles
2-5 touches vs 6+
Online-only vs omnichannel

Combine with Other Models

Mixed Approach

Linear for StrategyLast-Touch for TacticsData-Driven for Depth

Use it to size overall multi-channel performance and plan marketing mix.

Quick wins and conversion-channel optimization in short cycles.

ML models for cause-and-effect insights.

Validate with A/B Tests

Incrementality tests: Turn off individual channels to measure real impact
Geo-split tests: Different mixes by region
Holdout groups: With and without specific touches

Modern Challenges

Privacy-First World

With privacy rules and third-party cookie limits, tracking gets harder. Modern responses:

First-party data focus: Owned touchpoints first
Server-side tracking: Backend reliability
Probabilistic matching: ML to link anonymous interactions

Mobile-First Attribution

Mobile demands different handling:

App-to-web bridging: Connect app and web
Cross-device journey mapping: Multi-device paths
Privacy-compliant mobile tracking: IDFA and GAID limits

Mobile Specifics

Mobile-only touches:

Push notifications
In-app messages
App store interactions
Mobile ad formats

Technical limits:

iOS 14.5+ App Tracking Transparency
Android Privacy Sandbox
Cross-app tracking restrictions

About AI participation in writing articles

This article, like many others on our site, was created, written and proofread by a team of developers. Of course, not without the participation of AI assistants. We don't hide this and believe that modern systems are already quite good at handling simple tasks and, relatively speaking, writing an article about Viewport yourself is quite strange. It won't come out significantly better and will take a lot of time. But providing basic understanding to beginner webmasters is necessary. Of course, after the article is written by assistants - there's always proofreading, and this is where not one or two people participate, and only after that the article is published.

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