Digital Marketing Trainer and Consultant

What is position-based attribution?

ajayaggarwal

Introduction
Position-based attribution, also known as the U-shaped attribution model, is a method used in Google Ads to distribute conversion credit unevenly across the different touchpoints a customer interacts with before converting. This model gives the most credit to the first and last interactions, while splitting the remaining credit equally among the middle interactions. The idea behind this model is that the first click plays a vital role in introducing a customer to your business, and the last click plays an equally crucial role in closing the deal. The middle interactions, while important, receive less emphasis.

This model is ideal for advertisers who believe that both initial awareness and final conversion-driving actions are more influential than the research or consideration steps in between. It’s a great way to give credit to the ads that start and finish a customer journey, especially for businesses using full-funnel strategies that combine brand awareness with performance marketing.

Understanding Position-Based Attribution
In a typical position-based attribution model, the first click gets 40% of the credit, the last click gets 40%, and the remaining 20% is distributed equally among any middle interactions.

This distribution reflects a strategic balance between brand-building (the first interaction) and conversion-driving performance (the last interaction), while still acknowledging the role of the middle interactions.

For example, if a user clicks on 4 ads before making a purchase:

  • The first interaction gets 40%

  • The last interaction gets 40%

  • The 2 middle interactions each get 10%

Example: Myntra’s Position-Based Path

Let’s say a customer is shopping for festive clothing and follows this journey:

  1. Watches a YouTube ad from Myntra showing a new festive collection (First click)

  2. Clicks on a Search ad for “latest kurta sets”

  3. Clicks on a Display remarketing ad

  4. Clicks on a Shopping ad for a specific kurta and makes the purchase (Last click)

With position-based attribution:

  • YouTube ad (1st click) gets 40% credit

  • Shopping ad (last click) gets 40% credit

  • Search and Display ads (middle interactions) each get 10%

This model gives Myntra useful insights. It shows that the YouTube ad was very effective in attracting the customer, while the Shopping ad sealed the deal. The Search and Display campaigns played supporting roles.

Benefits of Position-Based Attribution

1. Rewards Both Awareness and Conversion Efforts
This model acknowledges the importance of the first touchpoint, which introduces the customer to your brand, and the last one, which leads to a sale. It’s ideal for advertisers who invest in both brand awareness and performance.

2. Balanced and Strategic
Unlike last-click, which only credits the final ad, or linear, which spreads credit equally, position-based attribution strategically emphasizes key moments: introduction and conversion.

3. Great for Full-Funnel Campaigns
If you use a mix of YouTube (top-of-funnel), Search (mid-funnel), and Shopping (bottom-funnel) campaigns, this model highlights how top and bottom campaigns are crucial, while still recognizing middle campaigns.

4. Ideal for Mid-Length Customer Journeys
If your typical customer interacts with 3–5 ads before converting, this model fits well. It captures the full path without overcomplicating the weighting.

5. Encourages Investment in High-Impact Ads
By revealing the value of both early-stage and late-stage campaigns, advertisers can allocate budgets more effectively to the touchpoints that matter most.

Limitations of Position-Based Attribution

1. Underestimates Mid-Funnel Touchpoints
The model assumes middle interactions are less important, which might not always be true. Sometimes, a mid-funnel ad provides key information that helps the user decide.

2. Arbitrary Credit Distribution
The 40%-20%-40% split is a general assumption. It might not reflect the real influence of each step in every user journey. Some businesses may need more flexible modeling.

3. Doesn’t Adapt to User Behavior Automatically
Unlike Data-Driven Attribution (DDA), which uses machine learning, position-based attribution doesn’t adjust based on actual performance. It’s fixed and based on rules.

4. Less Suitable for Extremely Short or Long Journeys
If a customer clicks only once or twice, this model may not apply effectively. Similarly, for long B2B or research-heavy buying cycles, more nuanced models like DDA may be better.

Example: Skechers Campaign

Imagine Skechers is promoting new performance walking shoes. A potential buyer:

  1. Clicks a YouTube ad showing a walking shoe lifestyle video (First click)

  2. Later clicks a Search ad after Googling “best walking shoes for daily use”

  3. Finally clicks on a Shopping ad showing a discounted pair and makes a purchase (Last click)

Here’s how position-based attribution would assign credit:

  • YouTube ad (First click): 40%

  • Search ad (Middle interaction): 20%

  • Shopping ad (Last click): 40%

This model shows Skechers that both the initial video campaign and the Shopping ad played major roles in the purchase, while the Search ad helped the user transition between research and decision.

When to Use Position-Based Attribution

  • When your business focuses heavily on both brand awareness and conversion ads

  • If you want to measure first and last touchpoints more seriously

  • When your customer journeys typically involve 3 to 5 steps

  • If you’re running cross-channel campaigns (like YouTube + Search + Shopping)

  • When you need a model that’s more advanced than last-click but simpler than DDA

How to Set Up Position-Based Attribution in Google Ads

  1. Log in to your Google Ads account

  2. Click Tools & Settings > Measurement > Conversions

  3. Select the conversion action you want to edit

  4. Click Edit settings

  5. Under Attribution Model, choose Position-Based

  6. Click Save

Once applied, Google Ads will use this model to report and optimize campaign performance accordingly.

Comparison with Other Attribution Models

  • First Click: Gives all credit to the first interaction

  • Last Click: Gives all credit to the last interaction

  • Linear: Gives equal credit to all touchpoints

  • Time Decay: Gives more credit to touchpoints closer to conversion

  • Data-Driven: Uses AI to assign credit based on actual behavior and impact

  • Position-Based: Gives 40% to the first and last, and splits 20% across the middle

Each model serves a specific goal. Position-based is ideal when you want to acknowledge both entry and exit points in the customer journey without complex algorithms.

Conclusion

Position-based attribution is a powerful and practical model in Google Ads that strategically distributes credit across a user’s conversion path. By giving 40% credit to both the first and last interactions, and the remaining 20% to middle interactions, it allows businesses to value both awareness and performance efforts. Brands like Myntra and Skechers can use this model to ensure their YouTube, Search, and Shopping campaigns are appropriately recognized for starting and finishing the customer journey. While not as sophisticated as data-driven attribution, position-based attribution offers a smart middle-ground for advertisers who want clarity, fairness, and better optimization insights for multi-touch advertising strategies.

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