Position-Based (U-Shaped) Attribution

Position-Based gives the biggest credit to the first and last touchpoints, typically 40% each. The remaining 20% spreads evenly across middle touches.

The "U-shape" name comes from the chart: weight piles up at the beginning and end of the journey.

How It Works

The model treats lead generation and conversion as the most influential moments. Both initial brand introduction and final closing action get recognized.

Standard Distribution

graph LR
    A[First touch<br/>40%] --> B[Middle touches<br/>10% each]
    B --> C[Last touch<br/>40%]
    C --> D[Conversion]

    style A fill:#e1f5fe
    style C fill:#e1f5fe
    style B fill:#f3e5f5

Practical Example

John's Journey:

Email campaign (first touch) → 40% credit
LinkedIn post (middle) → 10% credit
Google Ads (middle) → 10% credit
Promo code in email (last touch) → 40% credit

Total: 100%, distributed across all touches.

Configurable Proportions

You can adjust the split. Total must reach 100%.

Standard DistributionConservative DistributionBalanced Distribution

First touch: 40%
Last touch: 40%
Middle touches: 20% equally

First touch: 30%
Last touch: 50%
Middle touches: 20% equally

First touch: 35%
Last touch: 35%
Middle touches: 30% equally

Compared to Other Models

Model	First Touch	Middle	Last Touch	Features
First-Touch	100%	0%	0%	Focus on acquisition
Last-Touch	0%	0%	100%	Focus on conversion
Linear	25%	25% each	25%	Even split
Position-Based	40%	20% equally	40%	Acquisition + conversion balance
Time-Decay	10%	Increases toward end	40%	Recent touches weighted higher

vs Time-Decay

Time-Decay credits touches near conversion. U-Shaped weighs both first and last equally.

Difference: Time-Decay focuses on closing channels. U-Shaped balances acquisition and conversion.

Advantages

Recognizes Acquisition and Conversion

Lead generation and closing both matter. Middle touches matter too, but less. This reflects how channels actually work together.

Balanced

Position-Based fixes single-touch flaws:

First touch gets credit for starting the journey
Last touch gets credit as the closer
Middle touches aren't ignored but aren't overrated

Simple Enough

More complex than Last-Click or First-Click, but still understandable.

Implementation Best Practices

For successful Position-Based:

Set up comprehensive analytics

Track all interactions accurately
Visualize attribution flow

Integrate all channels

Combine data from every channel
Include online and offline

Focus on high-impact touches

Identify key conversion drivers
Prioritize first and last

Limitations

Oversimplifies Complex Journeys

By focusing on the ends, U-Shape can miss what happens in the middle. The full journey may not get fairly captured.

Undervalues Middle Touches

Social media and SEO often drive long-term success but get only thin credit here.

Ineffective for Short Journeys

For quick journeys with few touches, Last-Click may give clearer signals.

When Not to Use U-Shape

Avoid for:

High-value products with long sales cycles
Businesses where nurturing is critical
Cases where middle touches drive conversions

Ideal Scenarios

E-commerce and Low-Value Goods

The model works well when lead gen and conversion are the goals. For online stores, understanding both initial click and final purchase matters.

SaaS and Subscriptions

Subscription businesses benefit from understanding both onboarding and conversion touches. U-Shape highlights both.

Omnichannel

For integrated campaigns across social, email, PPC, and content, Position-Based attributes value cleanly.

Example

Integrated Campaign: - Starts with social media advertising (40% credit) - Continues with email nurturing (20% distributed) - Ends with direct purchase (40% credit)

Customization

Weights

Modern platforms let you adjust the split.

Conservative B2BAggressive B2CBalanced

First touch: 30%
Last touch: 50%
Middle touches: 20%

Long sales cycles where the closer matters most.

First touch: 50%
Last touch: 30%
Middle touches: 20%

Acquiring new customers in competitive markets.

First touch: 35%
Last touch: 35%
Middle touches: 30%

Equal weight to acquisition and closing.

Advanced Rules

Custom credit rules add flexibility:

Position rules: Lower credit for certain positions
Channel rules: Boost weight for priority channels
Time rules: Account for gaps between touches

Technical Aspects

Data Requirements

Complete journey tracking

Unique user IDs
Data from every channel
Cross-device tracking

Quality touchpoint data

Accurate timestamps
Traffic source info
Conversion event data

Integrations

CRM
Marketing automation
Business intelligence

Modern Challenges

Privacy and Tracking

Current obstacles:

Cookie blocking: 30-40% of users block
Browser limits: Safari ITP shortens cookie lifespan
Privacy regulation: GDPR and similar
Cross-device ID: Linking unified user is hard

Alternatives and Evolution

W-Shaped Attribution

Extended U-Shape that adds credit to lead-gen as a third key point alongside first and last.

Data-Driven Attribution

ML determines real channel contribution. Replacing rule-based models in larger orgs.

Custom Attribution

Marketers assign credit by their own insights. Most flexible, also most complex.

Measuring Effectiveness

Key Metrics

Metric	Description	Purpose
Attribution ROI	ROI accounting for attribution	Overall effectiveness
Channel Contribution	Each channel's conversion share	Budget optimization
First-Touch Volume	First-touch counts by channel	Acquisition effectiveness
Last-Touch Quality	Quality of closes	Conversion capability

A/B Testing Models

Compare Models

Process:

Run multiple models in parallel on same data
Analyze differences in channel evaluation
Validate through incrementality tests
Pick the optimal model for your business

Future

Cookieless World

With third-party cookies fading, the industry develops:

First-party data strategies
Server-side tracking
Privacy-preserving technologies
Cohort-based analysis

AI and ML Integration

Modern platforms add ML for dynamic Position-Based weight adjustment based on:

Historical conversion data
Seasonal patterns
Journey changes
Real-time channel effectiveness

Our platform offers flexible Position-Based configuration with adjustable weights for your business. We're building privacy-compliant solutions for accurate journey tracking under modern data privacy rules.

We're also implementing automatic weight optimization through machine learning, for maximum accuracy in evaluating each touchpoint's contribution.

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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