7 Marketing Attribution Models Compared: Which One Fits Your Business?

Choosing the right marketing attribution model can transform your campaigns and boost ROI. Here’s a quick breakdown of 7 models to help you decide:

• First-Touch: Gives all credit to the first interaction. Best for awareness campaigns.
• Last-Touch: Credits the final interaction before conversion. Ideal for short buying cycles.
• Last Non-Direct Click: Focuses on the last non-direct channel. Useful for Google Analytics users.
• Linear: Distributes credit equally across all touchpoints. Works well for longer sales cycles.
• Time Decay: Weights recent interactions more. Great for campaigns with time-sensitive actions.
• Position-Based: Splits credit between the first and last touchpoints (40% each) and middle ones (20%). Balances awareness and conversion efforts.
• Data-Driven: Uses machine learning to analyze and allocate credit based on actual performance. Perfect for businesses with lots of data.

Quick Comparison:

How to Choose:

• Start with simpler models (First-Touch, Last-Touch) if your data is limited.
• Shift to multi-touch models (Linear, Time Decay) as your campaigns grow.
• Use Data-Driven for detailed insights if you have enough data (3,000+ interactions, 300+ conversions in 30 days).

Key Tip: Regularly review your model to ensure it aligns with your goals and campaign performance.

Which Marketing Attribution Model Should I Use?

What Are Marketing Attribution Models?

Marketing attribution models help explain how different touchpoints contribute to customer conversions. These models generally fall into two types: single-touch and multi-touch attribution.

Single-touch attribution gives 100% of the credit for a conversion to one specific interaction in the customer journey. While it’s easy to set up, this approach can oversimplify the process by focusing too much on one touchpoint.

Multi-touch attribution, on the other hand, spreads the credit across several interactions, offering a broader view of how various efforts work together. Though it’s more complex to implement, it provides better insights into customer behavior and campaign effectiveness.

Here’s a quick breakdown of seven common attribution models:

• First-Touch: Assigns all credit to the first interaction.
• Last-Touch: Assigns all credit to the final interaction before conversion.
• Last Non-Direct Click: Gives credit to the last channel before a direct visit.
• Linear: Divides credit equally across all touchpoints.
• Time Decay: Gives more weight to touchpoints closer to the conversion.
• Position-Based: Prioritizes the first and last interactions, with the rest divided among the middle touchpoints.
• Data-Driven: Uses machine learning to allocate credit based on historical data.

Up next, we’ll dive into the First-Touch attribution model, covering how it works, its advantages, and its challenges.

1. First-Touch Attribution Model

The first-touch attribution model gives full credit for a conversion to the very first interaction a customer has with your brand. This method zeroes in on the initial touchpoint, helping you understand which marketing channel first introduced someone to your business.

How It Works
This model tracks the first point of contact in a customer’s journey, such as clicking on a social media ad, reading a blog post, or engaging with any marketing material. For instance, if someone first learns about your brand through a LinkedIn post, then later visits your site directly and makes a purchase, the LinkedIn post is credited entirely for the conversion.

Tools like cookie tracking, unique URLs, and integrated platforms are often used to implement this model.

Key Benefits and Usage

• Helps evaluate top-of-funnel performance and assess brand awareness efforts
• Easy to set up – 44% of marketers choose it for its simplicity
• Ideal for gauging the success of awareness campaigns

Limitations and Considerations

• Ignores interactions later in the customer journey
• Simplifies what is often a complex process
• Best suited for campaigns focused on building awareness

Up next: Last-Touch Attribution, which shifts the focus to the customer’s final interaction.

2. Last-Touch Attribution Model

Last-touch attribution gives all the credit for a conversion to the final interaction before a sale. It’s particularly useful for identifying which channels help close deals and works best with short purchasing cycles.

How It Works

This model focuses solely on the last interaction before a purchase. For example, if a customer first finds your brand on social media, later reads a blog post, but ultimately clicks on a paid search ad to make a purchase, the paid search ad gets full credit for the sale.

Best Use Cases

• Short buying cycles
• Impulse-driven purchases
• Straightforward conversion paths
• Teams just starting with attribution

"With last-touch attribution, you’re measuring how often each marketing channel is responsible for a conversion – in other words, for each sale, you walk back to the final channel that the customer saw or engaged with before making the purchase and give credit to, or attribute, that touchpoint." – The MNTN Team

Key Benefits

• Straightforward: Focuses only on the final touchpoint.
• Clear ROI: Pinpoints which channels directly lead to conversions.
• Less Data Required: Doesn’t need tracking of the entire customer journey.
• Quick Insights: Highlights the channels that close deals right away.

Limitations

While simple, this model ignores earlier touchpoints that contribute to the decision-making process. Customers often interact with multiple channels over time, but only the very last one gets credit.

Real-World Example

Picture a shopper grabbing a candy bar at the checkout counter. In this case, last-touch attribution credits the point-of-sale promotion entirely. This approach works well for quick, impulse decisions but doesn’t account for earlier activities that built awareness or interest.

Next up: Learn about the Last Non-Direct Click Model, which refines this approach by excluding direct visits.

3. Last Non-Direct Click Model

The last non-direct click model is a tweak on last-touch attribution. It skips over direct visits and gives full credit for a conversion to the last non-direct channel a customer interacted with before making a purchase.

How It Works

Here’s an example: If a customer’s path looks like Social Media → Email → Direct Visit → Purchase, the email campaign gets credit for the conversion, ignoring the direct visit.

Best Use Cases

• Ideal for marketing teams using Google Analytics, as this is the default model for non-multi-channel funnel reports.

Key Benefits

• Focuses on the last meaningful interaction by excluding direct visits.
• Simple to implement within Google Analytics.

Technical Considerations

Be aware that dropped referrer data in Google Analytics can sometimes incorrectly label marketing visits as direct traffic, which may distort your results.

Up next: the Linear model, which spreads credit equally across all touchpoints.

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4. Linear Attribution Model

The linear attribution model spreads credit evenly across all touchpoints in the customer journey. For instance, if there are five touchpoints, each gets 20% of the credit.

Best Use Cases

This model works well when all interactions in the journey are considered equally important in driving conversions.

Practical Application

If a customer interacts twice with Campaign A and once with Campaign B, Campaign A would receive 66.7% of the credit, while Campaign B would get 33.3%.

Key Benefits

• Highlights the role of every channel in the customer journey

Next, we’ll look at the Time Decay model, which prioritizes more recent touchpoints.

5. Time Decay Model

The Time Decay model gives more weight to touchpoints that occur closer to the conversion, using a default 7-day half-life. It’s a multi-touch attribution model that adjusts for the changing influence of each interaction over time.

For instance, a touchpoint on the day of conversion gets about 57% of the credit, one from seven days earlier gets 29%, and one from 14 days prior receives 14%.

Why Use Time Decay?

This model has several practical advantages for marketers:

• Distributes credit across all relevant touchpoints but emphasizes recent ones
• Highlights which channels are driving conversions most effectively
• Detects changes in traffic source performance faster
• Filters out short, low-value sessions (like those under 30 seconds) to focus on impactful interactions

Time Decay works well for promotional campaigns where recent efforts need more focus, but earlier touchpoints still play a role.

Up next: the Position-Based model, which splits credit between the first and last customer interactions.

6. Position-Based Model

In the position-based model, credit is distributed with 40% going to the first interaction, another 40% to the last interaction, and the remaining 20% split evenly across all middle touchpoints.

• First touchpoint: 40% credit
• Last touchpoint: 40% credit
• Middle touchpoints: 20% credit (shared equally)

For instance, imagine a SaaS company like Growth Method. A customer might first engage with an email campaign (earning that touchpoint 40% credit). Then, they interact with a LinkedIn sponsored post and a Google Ad (each receiving 10% credit). Finally, they convert after receiving a promotional discount code via email (earning another 40% credit).

When to Use

This model works well if you:

• Have longer sales cycles with multiple touchpoints
• Equally value brand awareness and conversion efforts
• Want to acknowledge the role of both acquisition and closing channels
• Aim to improve both top-of-funnel and bottom-of-funnel strategies

Key Benefits

• Provides balanced credit between discovery and conversion touchpoints
• Keeps mid-journey interactions visible
• Helps align strategies for both acquiring and converting customers
• Simpler to implement compared to more advanced models

Requirements

• Reliable multi-channel tracking
• Accurate identification of first and last touchpoints

Next, we’ll dive into the Data-Driven model, which dynamically allocates credit based on your own data.

7. Data-Driven Model

Data-driven attribution uses machine learning to dynamically assign credit across various customer touchpoints. This approach analyzes multiple factors, such as:

• The number of touchpoints and interactions with each one
• Time gaps between touchpoints
• Types of touchpoints involved
• Devices customers use
• Customer demographics

"Data-driven attribution means you can accurately measure your marketing return on investment (ROI) and optimize your campaigns for the best results." – Neil Patel, Co‑Founder of NP Digital & Owner of Ubersuggest

When to Use

This model works best for businesses that log 3,000+ ad interactions and 300+ conversions within 30 days. It’s particularly useful for understanding complex customer journeys and optimizing marketing budgets based on performance data.

Key Benefits

• Custom Analysis: Builds a model tailored to your specific data.
• Ongoing Accuracy: Becomes more precise as it processes more data.
• Channel Insights: Pinpoints which channels contribute the most to conversions.
• Holistic Perspective: Recognizes the role of all touchpoints in the customer journey.

Requirements

To get the most out of this model, you’ll need:

• Proper tracking systems, like UTM tagging.
• Clearly defined conversion goals and routine data checks.
• Enough time for the model to gather and analyze data effectively.

Current Limitations

Despite its precision, this model has challenges. For example, research shows that less than 20% of marketers effectively measure email marketing ROI, and 23% struggle with tracking ROI for social media. Without strong tracking and measurement systems, the insights gained from this model will be limited.

Implementation Tips

• Set Clear Goals: Know what you want to achieve with attribution insights.
• Ensure Tracking Accuracy: Verify that every channel is tracked properly.
• Audit Your Data: Regularly check for any gaps or inaccuracies.
• Be Patient: Allow the model enough time to collect meaningful data.
• Act on Insights: Use the results to adjust budgets and refine your campaigns.

Next, we’ll compare all seven attribution models side by side for a clearer understanding of their differences. Stay tuned for the quick guide.

Quick Model Comparison Guide

Here’s a quick overview of seven attribution models, comparing their setup effort, data needs, ideal use cases, and primary drawbacks.

Balancing Accuracy and Complexity

Simpler models like First-Touch and Last-Touch are easier to implement but offer limited insights. On the other hand, the Data-Driven model provides deeper accuracy by leveraging machine learning but requires more data and expertise.

Practical Use Cases

For instance, e-commerce businesses with quick purchase decisions often rely on Last-Touch. In contrast, B2B companies with longer sales cycles may benefit more from Linear or Data-Driven models.

Choosing Your Attribution Model

Here’s how to put the Quick Model Comparison Guide into practice:

• Sales Cycle & Scale
  • For short or straightforward customer paths, consider Last-Touch or First-Touch models.
  • If you’re dealing with longer, multi-touch journeys for mid-sized operations, try Linear or Time Decay models.
  • For enterprise-level needs, opt for Data-Driven models (note: this requires at least 3,000 ad interactions and 300+ conversions).
• Data Readiness
  Start with single-touch models while your tracking systems are still developing. As your analytics improve, shift to multi-touch or Data-Driven models.
• Objectives
  • If your goal is building awareness, use First-Touch.
  • For driving conversions, Last-Touch is more suitable.
  • For complex customer journeys, go with Linear or Time Decay models.
• Regular Reviews
  Reassess your attribution model every quarter. This helps align it with your evolving strategy, ensures insights remain actionable, and improves overall performance.

Related posts

• First-Click vs. Last-Click Attribution: Which Should You Choose?
• Marketing Attribution ROI: Measuring Campaign Performance
• B2B Attribution Tracking: Essential Metrics Checklist