Introduction
In digital marketing, it is essential to understand the customer journey and identify which marketing efforts are contributing to conversions. Attribution models help marketers and advertisers assign credit to various touchpoints in that journey. Google Ads offers several types of attribution models to evaluate performance across campaigns. Among these, Linear Attribution is one of the most balanced and straightforward models. It evenly distributes conversion credit across all touchpoints a customer interacts with before converting, rather than favoring the first or last click.
Linear attribution is particularly useful for businesses with multi-channel or full-funnel marketing strategies. It ensures that every ad, keyword, and campaign that played a role in guiding the user towards the final action receives fair recognition. By treating each step as equally important, advertisers can get a clearer understanding of how different ads interact and influence customer behavior.
Understanding Linear Attribution
The linear attribution model is based on the principle of equal contribution. If a user clicks on four different ads before converting, each ad interaction is assigned 25% of the conversion value. This method assumes that all interactions had an equal influence on the customer’s decision to convert.
This is different from models like Last Click Attribution (where 100% of the credit goes to the last interaction) or First Click Attribution (where all credit goes to the first touchpoint). Linear attribution spreads credit across the full conversion path, making it more inclusive and suitable for understanding the overall effectiveness of your advertising efforts.
Example: Myntra’s Customer Journey
Imagine a user looking to buy an outfit for an upcoming festival. Their journey goes like this:
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They watch a YouTube video ad from Myntra showcasing festive fashion trends.
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Later, they search “best ethnic dresses” and click a Search ad from Myntra.
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A day later, they see a Display ad offering a 20% discount on dresses and click it.
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Finally, they click on a Shopping ad for a specific product and complete the purchase.
Under the linear attribution model, each of these four interactions would get 25% of the conversion credit. This means Myntra can evaluate how YouTube ads (which may have started the customer journey), Search ads (which provided more intent-driven engagement), Display ads (which encouraged return visits), and Shopping ads (which closed the sale) all played equally meaningful roles in converting the customer.
How to Set Up Linear Attribution in Google Ads
To implement linear attribution in your Google Ads account:
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Sign in to your Google Ads account.
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Click on Tools & Settings (the wrench icon in the top right).
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Under “Measurement,” click on Conversions.
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Select the conversion action you want to edit.
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Click Edit Settings.
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Scroll to the Attribution model section and select Linear.
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Save your changes.
Once set, your reporting and Smart Bidding (if used) will begin optimizing based on linear attribution data, and each campaign or ad involved in the path to conversion will reflect its fair share of credit.
Benefits of Linear Attribution
1. Balanced View of the Conversion Path
Linear attribution provides a more holistic view of your marketing performance. It gives equal credit to each step, allowing advertisers to see the value of both upper-funnel and lower-funnel activities. This is especially important when users interact with multiple ad formats and keywords before converting.
2. Encourages Investment in Top and Middle Funnel Campaigns
Unlike last-click models that reward only closing ads, linear attribution highlights the role of brand awareness and consideration-stage ads. This allows advertisers to justify budget allocation to YouTube, Display, and early-stage search campaigns that may not directly drive immediate conversions but contribute to eventual sales.
3. Simple to Understand and Implement
Linear attribution is straightforward. There are no complex algorithms or machine learning involved, which makes it easier for small businesses and new advertisers to grasp and implement. This simplicity also makes performance reports easier to interpret.
4. Reduces Optimization Bias
When advertisers rely solely on last-click attribution, they may undervalue or turn off campaigns that actually help drive conversions indirectly. Linear attribution helps reduce this bias by giving recognition to all influential touchpoints.
5. Works Well with Consistent Conversion Paths
If users typically engage with your ads in predictable, multi-step journeys, linear attribution ensures fair representation of each touchpoint without needing advanced modeling.
Limitations of Linear Attribution
1. Assumes All Touchpoints Are Equal
In reality, not all touchpoints have the same impact. Some interactions may be far more persuasive or influential than others. Linear attribution doesn’t distinguish between a casual initial view and a decisive product click, which can skew optimization if not considered.
2. May Not Be Suitable for Short Conversion Paths
If your customer journey is usually one or two steps, the benefits of using linear attribution diminish. In such cases, a model like last-click or first-click might be more useful and practical.
3. Lacks Precision Compared to Data-Driven Attribution
Data-driven attribution uses machine learning to understand which touchpoints truly drive results, assigning credit proportionally. Linear attribution, while fair, does not reflect actual user behavior or outcomes. As a result, it may not provide as precise guidance for automated bidding or campaign optimization.
4. Doesn’t Account for Time or Position in the Journey
Linear attribution treats all clicks the same regardless of when they happened or where they fall in the conversion path. A click that occurred two weeks before the sale is given the same value as one that happened just seconds before.
Example: Skechers Multi-Touch Marketing
Consider Skechers, a global footwear brand promoting a new line of walking shoes. A user sees a Display ad on a fitness website, clicks it, and browses products. A few days later, they watch a YouTube ad with a product demo. Later, they click a Search ad while looking for “lightweight Skechers shoes.” Finally, they click a Shopping ad and make a purchase.
Using the linear attribution model, each of these four touchpoints gets 25% of the conversion credit. Without this model, Skechers might ignore the Display or YouTube campaigns if they used last-click attribution, missing the opportunity to optimize their top-funnel strategy.
When to Use Linear Attribution
Linear attribution is a good fit for businesses with the following characteristics:
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Running multi-channel marketing strategies involving awareness, consideration, and conversion campaigns
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Wanting a simple and balanced approach without needing advanced algorithms
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Interested in seeing how various campaigns work together instead of focusing on just one type
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Managing campaigns with relatively predictable customer journeys involving multiple interactions
If your campaigns involve YouTube, Display, Shopping, and Search in combination, and your customers usually interact with more than one ad before converting, linear attribution offers a well-rounded performance view.
Comparison with Other Attribution Models
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First Click Attribution credits only the first interaction
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Last Click Attribution credits only the final interaction
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Time Decay Attribution gives more credit to interactions that happened closer to the conversion
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Position-Based Attribution splits credit heavily between the first and last touchpoints, often in a 40%-20%-40% format
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Data-Driven Attribution uses machine learning to assign credit based on the real influence of each interaction
Compared to these, linear attribution is the most evenly distributed and unbiased model. It may not reflect true influence like data-driven attribution but serves as a fair and accessible alternative for advertisers without advanced resources.
Conclusion
Linear attribution in Google Ads offers a clear, fair, and simple way to measure the performance of all touchpoints in a user’s journey. By dividing conversion credit equally among all ad interactions, it ensures that both top-funnel and bottom-funnel campaigns are acknowledged for their role in driving conversions. Brands like Myntra and Skechers, which rely on multi-channel advertising, benefit from this model as it highlights the interconnected nature of customer interactions.
