Crossing The Lines of Attribution

Online Marketing Attribution and Its Impact on Customers

Online marketing attribution is a core part of digital marketing. It helps marketers understand which channels influence customers and drive revenue.

Attribution assigns value to every interaction a customer has before converting. When done correctly, it shows what actually works—and what doesn’t.

Cross-channel attribution takes this further. It tracks touchpoints across the entire funnel, even when they happen on different devices or platforms. This gives businesses better insight into customer behavior and helps them spend ad budgets more effectively.

All attribution depends on one thing: data quality.

Why Data Quality Matters in Attribution

An attribution model is only as good as the data behind it. Many marketers focus on choosing a model before checking whether their data is accurate and complete.

Others try to track every possible touchpoint. This often leads to complex systems that are expensive and hard to use.

A strong attribution model should:

• Track all meaningful touchpoints
• Connect interactions across devices
• Assign fair value to each channel
• Support better campaign decisions

Good data makes attribution useful. Poor data makes it misleading.

Types of Attribution Models

Attribution models fall into two main categories:

• Rule-based attribution models
• Statistical attribution models

Each approach assigns credit differently.

Rule-Based Attribution Models

Rule-based models use fixed rules to assign credit to touchpoints. They do not use historical data. This makes them simple—but less accurate.

These models offer a basic view of the customer journey.

Common Rule-Based Attribution Models

• Time decay
• Positional
• First click
• Last click

Time Decay Attribution

Time decay attribution gives credit to every touchpoint. Touchpoints closer to the conversion receive more credit.

This model assumes early interactions create awareness, while later ones drive action.

Time decay works well for:

• Long sales cycles
• Scheduled or sequential campaigns
• Tracking engagement over time

Example:
A customer sees a travel destination on CTV, searches online, clicks a social ad, and later converts after hearing an audio ad. Time decay gives credit to each interaction, with more weight on the final steps.

Positional Attribution

Positional attribution, also called U-shaped attribution, gives the most credit to the first and last touchpoints.

Typically:

• 30–40% goes to the first interaction
• 30–40% goes to the final interaction
• The rest is shared across middle touchpoints

This model values both discovery and decision-making. However, it may miss insights when conversions happen mid-journey.

First Click and Last Click Attribution

First click and last click attribution give 100% of the credit to one touchpoint.

• First click focuses on discovery
• Last click focuses on conversion

These models are easy to use but often inaccurate. They ignore all other interactions and can distort reporting.

They can also cause double counting. Platforms like Google and Meta do not share data. Using an independent partner like Stirista helps unify attribution and remove duplicate conversions.

Statistical Attribution Models

Statistical attribution models use data and algorithms to assign credit. They analyze historical performance and compare touchpoints to see what truly drives conversions.

The most advanced form is Multi-Touch Attribution (MTA).

How Multi-Touch Attribution Works

Multi-Touch Attribution uses three main data sources.

Key Data Inputs

• User identity and characteristics
• Media touchpoints
• Sales and conversion data

What MTA Reveals

• Which audiences respond to specific messages
• How ad formats influence behavior
• Which channels drive conversions
• Where budgets should shift

Example:
A retailer notices a loyalty member browsing cookware on both a phone and a work computer. MTA connects these interactions and enables targeted ads that lead to a purchase.

Identity-Driven Attribution at Stirista

At Stirista, identity is the foundation of attribution.

Stirista combines identity-level data with omnichannel activation. This creates a real-time view of customer behavior, including demographics, interests, browsing activity, and purchase intent.

With accurate identity data, marketers can:

• Launch omnichannel campaigns with confidence
• Measure performance accurately
• Retarget customers across channels
• Improve retention and revenue

Explore Identity-Powered Attribution

The goal is simple: help marketers generate revenue using identity-driven data that works.

Explore identity-powered attribution to improve performance and make smarter marketing decisions.