How Position-Based Attribution Works in PPC

Position-based attribution, also known as the U-shaped model, splits conversion credit across key stages of a customer’s journey. It gives 40% of the credit to the first interaction, 40% to the final interaction, and divides the remaining 20% among any middle touchpoints. This method highlights both the initial ad that sparks interest and the final ad that drives the conversion, while still acknowledging the role of mid-journey interactions.

For example, in a campaign with four touchpoints:

• The first ad and last ad each get 40% of the credit.
• The two middle ads share the remaining 20%, with 10% allocated to each.

This model is especially useful for understanding how ads across the funnel contribute to conversions, helping marketers allocate budgets more effectively. Although Google Ads no longer supports position-based attribution, its principles remain relevant, and data-driven attribution has become the preferred alternative for dynamic credit distribution.

Key Takeaways:

• Credit Distribution: 40% to the first and last touchpoints, 20% split among the middle.
• Use Case: Ideal for campaigns with multiple interactions, especially in longer sales cycles.
• Implementation: While no longer available in Google Ads, its approach influences modern attribution strategies like data-driven models.

How Position-Based Attribution Works

Credit Allocation in the U-Shaped Model

In the U-shaped attribution model, 40% of the credit is given to both the first and last touchpoints, while the remaining 20% is divided equally among the middle interactions. Here’s how it breaks down:

• For two touchpoints, the credit is split 50–50.
• For three touchpoints, the middle one gets 20%.
• For four touchpoints, each middle interaction receives 10%, and so on.

The first touchpoint is crucial because it marks the initial engagement. The last touchpoint is equally important, as it directly leads to the conversion. Any interactions that happen in between share the remaining credit evenly.

This model treats each interaction as a separate event, regardless of whether they occur within hours or over several weeks. These clear rules make it straightforward to implement, as shown in the example below.

Step-by-Step Example

Let’s take a look at how this plays out in practice. Imagine a software company running a PPC campaign. A potential customer, Sarah, follows this multi-touch journey before purchasing a $2,000 project management software license:

• Day 1: Sarah clicks on a Google search ad after searching for "project management software." This is her first touchpoint.
• Day 5: She clicks on a LinkedIn display retargeting ad and reads case studies - this is middle touchpoint #1.
• Day 8: While researching competitors, Sarah clicks on a display ad on a business website - this becomes middle touchpoint #2.
• Day 12: Sarah searches for the company name directly, clicks on a branded search ad, and completes the purchase - this is the final touchpoint.

Here’s how the $2,000 conversion value is distributed:

• The first touchpoint (Google search ad) gets $800 (40% of $2,000).
• The final touchpoint (branded search ad) also gets $800 (40%).
• The two middle interactions (LinkedIn and display ads) split the remaining $400, with $200 (10%) allocated to each.

This breakdown provides a more complete picture than last-click attribution, which would have credited the entire $2,000 to the branded search ad. That approach might lead the company to undervalue upper-funnel efforts like search ads and display retargeting. However, the U-shaped model highlights the critical role these touchpoints played in Sarah’s decision-making process.

Google Ads/Analytics Attribution Models Explained - Beginner Level Tutorial

Setting Up Position-Based Attribution in PPC Campaigns

Now that we’ve covered the basics of the U-shaped model, let’s dive into how you can implement position-based attribution in your PPC campaigns. This involves configuring your ad platforms and ensuring all key touchpoints are tracked effectively.

Setting Up Position-Based Attribution in Google Ads

Google Ads makes it relatively simple to apply position-based attribution through its built-in attribution modeling tools. Start by heading to the Tools & Settings menu, then select Attribution under the Measurement section. Here, you’ll find your current attribution model - likely set to "Last click" by default.

To change this, click on Attribution models to view your options. Choose Position-based, which assigns 40% of the credit to both the first and last interactions, with the remaining 20% distributed among other touchpoints.

Before finalizing the change, review the model comparison tool to see how this shift could impact your conversion values and ROI. Keep in mind, switching to position-based attribution often highlights the value of upper-funnel campaigns that may have been overlooked under last-click models.

Once selected, the position-based model is applied to all conversion actions in your account. This adjustment will affect both your reporting and automated bidding strategies. For example, Smart Bidding algorithms will begin optimizing based on the new attribution model. Be prepared for temporary fluctuations in performance as the system adjusts.

It typically takes 24–48 hours for the new attribution model to fully take effect across your campaigns. Once implemented, focus on tracking all customer interactions to get the most out of your data.

Tracking and Monitoring Touchpoints

Accurate and comprehensive tracking is essential for position-based attribution. Here’s how to get started:

• Set up Google Analytics 4 (GA4) alongside your Google Ads conversion tracking. GA4 allows for cross-device tracking, ensuring you capture every interaction, whether it’s mobile research or desktop purchases.
• Revisit your conversion windows. The default 30-day click window might not be sufficient for longer sales cycles, such as those common in B2B or high-consideration purchases. Adjust this to 60 or 90 days if necessary.
• Create custom conversion actions to track various stages of your funnel. Beyond final purchases, consider tracking micro-conversions like newsletter signups, demo requests, or whitepaper downloads. These additional touchpoints enrich your position-based attribution data.

Customizing Attribution Settings

The standard 40-40-20 split works well for many businesses, but it’s worth tailoring your approach to fit your sales cycle and goals. While Google Ads uses a fixed position-based model, there are still adjustments you can make:

• Adjust the lookback window: For example, an e-commerce retailer might stick with a 30-day window, while an enterprise SaaS company could benefit from extending it to 90 days.
• Analyze conversion lag data: Use Google Ads reports to understand when conversions typically happen. If most occur within a week, a shorter window might suffice, avoiding over-crediting early touchpoints.
• Create separate models for different goals: A newsletter signup may require a different approach than a high-value purchase. While Google Ads applies attribution at the account level, tools like GA4 can help you customize models for specific conversion goals.

For accounts with sufficient data - around 15,000 clicks and 600 conversions in 30 days - data-driven attribution becomes an option. This model uses machine learning to allocate credit based on your campaign’s actual performance patterns. While it can provide more precise insights, it requires significant traffic to work effectively.

The fixed 40-40-20 split is ideal for emphasizing first and last touchpoints, but you should regularly evaluate its performance. Use the Model comparison tool in Google Ads to monitor changes in campaign metrics and adjust your bidding strategies as needed. Campaigns that seemed weak under last-click attribution might prove their value under position-based attribution, potentially justifying higher budgets.

For more tips and resources to refine your PPC attribution strategy, check out the Top PPC Marketing Directory at https://ppcmarketinghub.com.

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Position-Based Attribution vs Other Models

After breaking down the U-shaped model, let’s see how it stacks up against other attribution models. Comparing these approaches can give you a clearer picture of how conversion credit is distributed and help you make smarter decisions for your PPC campaigns. Each model has its own way of allocating credit, which impacts how you measure success and allocate budgets.

Quick heads-up: Google Ads has phased out position-based attribution, along with first-click, linear, and time-decay models. These have been replaced by data-driven attribution or can be switched to last-click attribution. While Google now leans on data-driven attribution, these older models are still useful for historical analysis and comparison.

Attribution Models Comparison Table

This table highlights the distinct strengths and weaknesses of each model. It also sets the stage for understanding when position-based attribution might be the right fit.

Position-based attribution stands out by allocating credit to both the first and last touchpoints. Unlike last-click, which zeroes in on the final interaction, or first-click, which emphasizes initial awareness, the U-shaped model’s 40-40-20 split recognizes the critical moments of first engagement and final conversion, while still acknowledging the in-between steps.

When to Use Position-Based Attribution

Although Google Ads no longer supports position-based attribution, there are specific scenarios where it shines. Here’s when this model would have been especially effective:

• Longer sales cycles: For industries like B2B software or high-value goods, where early interest and final actions are critical, the 40-40-20 split captures the importance of both moments while still accounting for the research phase in the middle.
• Blending brand awareness with conversion goals: If your campaigns target both upper-funnel brand awareness and lower-funnel conversions, this model helps you understand the value of initial engagement alongside the final conversion driver.
• Multi-channel campaigns: Position-based attribution works well when different channels play distinct roles. For example, display ads might drive early awareness (earning first-touch credit), search ads might seal the deal (getting last-touch credit), and social media interactions might nurture interest along the way.

Since position-based attribution is no longer available in Google Ads, data-driven attribution is now the go-to option. This model uses machine learning to analyze your specific campaign data and dynamically assigns credit based on actual contributions to conversions. While it’s more complex than the fixed 40-40-20 split, it often delivers more precise insights.

Here’s an example: A customer searches for "restaurant tuscany", then "restaurant florence", then "3 star restaurant florence", and finally "3 star restaurant abigaille florence." They click on an ad for each search and make a reservation after clicking the ad for "3 star restaurant abigaille florence." In the Last-Click model, the keyword "3 star restaurant abigaille florence" gets 100% of the credit. In the Data-Driven model, each keyword would receive partial credit based on its role in driving the conversion.

If you’re still using last-click attribution, check out the Model comparison report in Google Ads. It can show you how data-driven attribution might change the way you value keywords, ad groups, or campaigns. This insight can guide you in adjusting budgets and bidding strategies.

While position-based attribution may no longer be an option, its core idea - acknowledging the contributions of multiple touchpoints - remains relevant. Data-driven attribution takes this concept further by leveraging your actual campaign data to create a more dynamic and accurate credit distribution system.

Best Practices for Position-Based Attribution in PPC

While modern PPC strategies often lean toward data-driven or last-click attribution models, position-based attribution remains a useful tool for analyzing historical performance and optimizing select platforms. To make the most of this model, focus on strategic planning, consistent monitoring, and leveraging the right tools. Let’s dive into how you can fine-tune credit allocation and improve campaign results.

Optimizing Credit Allocation

The U-shaped attribution model, which typically assigns credit in a 40-40-20 split, can be adjusted to better fit your campaign's unique needs and customer behavior. For example:

• Shorter sales cycles might benefit from a 30-50-20 split, allocating more weight to the middle touchpoints for quicker conversions.
• Longer sales cycles may call for a 50-30-20 distribution, emphasizing early interactions to nurture prospects over time.

To refine these allocations, analyze your conversion timelines. If most conversions happen within 24–48 hours of the first touchpoint, middle interactions may hold less importance. On the other hand, campaigns with longer customer journeys - spanning weeks or months - should prioritize nurturing touchpoints to maintain engagement. Align your credit distribution with campaign goals: emphasize early interactions for building brand awareness and later touchpoints for driving conversions. Be sure to document your decisions so you can evaluate their impact and make adjustments as needed.

Monitoring Campaign Performance

After setting up your credit allocation, regular performance monitoring is essential to keep your attribution model aligned with customer behavior. Weekly reviews of attribution data can help you track trends and assess the role of each touchpoint.

Keep an eye on changes in conversion path lengths. If customers start needing more touchpoints before converting, it could signal increased competition or market challenges. Conversely, shorter paths might indicate improved ad relevance or stronger brand loyalty. Use custom reports to analyze performance across attribution models, focusing on metrics like CPA (cost per acquisition) and touchpoint distribution. Setting alerts for significant shifts can help you quickly identify potential issues, whether they're technical glitches or changes in market conditions.

Finding Tools for Attribution Modeling

To implement effective attribution modeling, you’ll need the right tools and expertise. The Top PPC Marketing Directory is a great place to discover resources tailored to your needs, including advanced tools and expert services.

The directory features performance tracking tools that offer detailed control over attribution and support cross-platform tracking. Many of these tools include advanced features that can complement or even replace native platform options. Additionally, analytics experts listed in the directory can help you design custom attribution frameworks and interpret complex data to inform your strategy.

You’ll also find A/B testing platforms that allow you to validate any changes to your attribution model through controlled experiments. For automated optimization, bid management tools with attribution capabilities can adjust bidding strategies based on contributions from multiple touchpoints, ensuring your keywords and audience targeting are optimized for maximum conversion value.

Key Takeaways on Position-Based Attribution in PPC

Position-based attribution operates on a 40-40-20 split, distributing credit across the first, last, and middle touchpoints in a customer’s journey. This approach highlights how each stage contributes to conversions, making it a solid choice for campaigns with moderately complex paths to conversion.

One of the standout features of this model is its adaptability. You can tweak the credit distribution to suit your business objectives. For instance, if your goal is to boost brand awareness, you might focus more on early touchpoints. On the other hand, for longer sales cycles, adjusting the middle interactions can yield better results. This makes it a practical alternative to last-click attribution, offering deeper insights without the complexities of fully data-driven models.

To make the most of this model, consistent monitoring and fine-tuning are essential. Regularly analyzing attribution data helps you spot changes in customer behavior and adjust your strategies and budgets accordingly.

Leverage tools like the Top PPC Marketing Directory for advanced tracking and analytics support. These resources can help you transform raw data into actionable strategies, allowing you to refine credit allocation and align it with your campaign goals.

Whether you're focused on driving awareness, nurturing leads over a longer sales cycle, or optimizing for quick wins, position-based attribution provides the insights needed to make smarter decisions. By aligning your bidding and targeting strategies with these insights, you can take your PPC performance to the next level.

FAQs

What’s the difference between position-based attribution and data-driven attribution in PPC campaigns?

Position-based attribution focuses on giving more weight to the first and last touchpoints in a customer’s journey. This makes it a practical choice for identifying how initial and final interactions contribute to conversions. Its simplicity helps spotlight these key moments, but it doesn’t dive deeply into the role of the touchpoints in between.

On the other hand, data-driven attribution takes a more advanced approach by using machine learning to evaluate the actual influence of each touchpoint throughout the customer journey. This method provides a clearer, more detailed picture of how various interactions combine to drive results. It’s particularly useful for managing complex, multi-channel PPC campaigns. While position-based attribution keeps things straightforward, data-driven attribution stands out as a stronger tool for refining strategies and improving ROI.

Why did Google Ads discontinue position-based attribution, and what is the recommended alternative?

Google Ads decided to phase out position-based attribution because it wasn’t widely adopted and didn’t accurately reflect how customers engage with ads throughout their journey. It represented only a small share of attribution usage and struggled to capture the complete path a customer takes.

Marketers are now encouraged to adopt data-driven attribution, which leverages machine learning to evaluate user behavior and distribute credit more accurately across various touchpoints. This method provides better insights into which interactions play the biggest role in driving conversions, making it easier to fine-tune campaign performance.

How can businesses adjust the position-based attribution model to match their sales process and objectives?

Businesses can adjust the position-based attribution model by tweaking how credit is distributed among touchpoints in the customer journey. For example, if the first interaction has less impact on conversions, they can assign it a smaller share of the credit and shift more weight to the middle or final touchpoints. The default setup usually splits credit as 40-40-20 - with 40% going to the first and last interactions and 20% to the middle - but this can be modified to better match the nuances of their sales process.

By customizing these weights, businesses can align the model with their specific goals and customer behavior patterns. This approach provides clearer insights into which interactions are most effective, helping them make smarter decisions about budget distribution and campaign strategies.

Related posts

• Checklist for Building Custom Attribution Models
• Position-Based vs. Last Click Attribution
• How to Implement Multi-Touch Attribution