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Linear attribution is a measurement model that assigns equal credit for conversions across each marketing channel or touchpoint along a customer’s journey.
Linear attribution is a multi-touch attribution model that measures how different touchpoints influence a customer’s journey before they complete a desired action. Unlike other models that give more weight to the first or last interaction, linear attribution assigns equal credit to every touchpoint.
Today’s multi-channel marketing world has customers interacting with brands across various platforms and touchpoints. Linear attribution tracks the entire user journey, ensuring that every marketing effort is recognized. This balanced method helps you make informed decisions and optimize your strategies effectively.
Linear attribution maps out all the touchpoints a customer interacts with before converting and gives equal credit to each.
Let’s say you’re running a campaign to promote your app. If a user first sees your ad on Instagram, then visits your landing page from a Google search, reads a blog post, and finally downloads your app after clicking a remarketing ad, each of these interactions gets 25% of the credit for the conversion.
By valuing each interaction equally, you can see the full impact of your marketing ecosystem. If data shows that email campaigns and remarketing ads play significant roles in conversions, you can allocate more resources to these channels for better results.
Like other marketing attribution methods, linear attribution has its pros and cons. While it can help you understand your customer journey, improve user experience, and allocate resource across channels, the insights it provides may lack nuance.
Linear attribution is a valuable attribution measurement tool when you want to grasp the combined impact of multiple touchpoints. It’s especially handy for multi-channel campaigns and early-stage marketing, where every interaction counts.
On the other hand, it’s tricky to appropriately weigh touchpoints. Does a user spending five minutes exploring your app have the same importance as following you on social media? And why, or why not? This remains a constant challenge. For instance, one user clicking on a push notification might have more influence on their decision to purchase than another user who also clicked.
For deeper insights into specific touchpoints or complex sales cycles, other attribution models may offer more precise and actionable data. But if you’re looking for a straightforward model that accounts for all relevant touchpoints, linear attribution is a solid starting point.
Let’s take a closer look into when using linear attribution makes sense and when it doesn’t.
Suppose your marketing strategy involves various channels like social media, email marketing, influencer partnerships, and paid ads. In this case, linear attribution helps you see how these channels collectively help you achieve your marketing goals.
Imagine a user who first sees an Instagram ad, then reads a blog review, and finally clicks on a remarketing ad to download your app. Under the linear model, each interaction gets equal credit, giving you a balanced view of your app marketing efforts.
In the early stages of marketing your product, when you’re still figuring out what works, linear attribution offers a broad understanding of how different touchpoints contribute to user acquisition. This helps you fine-tune your strategy without prematurely prioritizing one channel over another.
If your business relies on maintaining consistent engagement across various touchpoints, linear attribution highlights the importance of each interaction.
For example, a fitness app might engage users through blog posts, social media updates, email newsletters, and in-app notifications. Linear attribution ensures every engagement point is recognized, encouraging sustained efforts across all channels.
If certain touchpoints have a significantly higher impact on conversions, linear attribution might not be ideal.
For example, if data shows a demo video on your app’s landing page is the primary driver of downloads, giving it the same credit as a less impactful touchpoint, like a single social media post, won’t accurately reflect its importance. In such cases, models like time decay or position-based attribution, which give more weight to key interactions, might be better.
For apps with complex sales cycles, where the user journey involves multiple stages of engagement and decision-making, linear attribution may oversimplify the process. An enterprise app that requires demos, consultations, and multiple follow-ups before a download is completed would benefit more from a custom attribution model that reflects the true journey.
If your goal is to optimize resources by investing more in high-impact channels, linear attribution might not provide the granular insights you need. For example, if you need to decide whether to invest more in Facebook ads or influencer partnerships for your gaming app, an attribution model that differentiates the impact of these channels, such as data-driven attribution, can offer better guidance.
Choosing the right attribution model depends on your marketing goals and customer journey. While linear attribution offers an equitable view, models like first touch, last touch, time decay, U-shaped, and W-shaped provide nuanced insights for optimizing different aspects of your marketing strategy.
Read on for a brief introduction to each of these models.
First-touch attribution gives all the credit for a conversion to the first interaction a user has with your brand. This model is great for understanding which marketing channels create initial awareness.
Last-touch attribution credits the very last interaction before conversion. This model helps you see which touchpoints are best at closing the deal.
Time decay attribution assigns more credit to touchpoints closer to the conversion. You can use this model when timing significantly impacts your decisions.
U-shaped attribution, or position-based attribution, gives most credit to the first and last interactions, with the remaining credit distributed among the middle touchpoints. It’s up to you to determine the percentage of credit assigned to each. This model highlights the importance of both initial engagement and conversion interactions.
W-shaped attribution extends the U-shaped model by giving significant credit to three key touchpoints: the first interaction, a critical mid-funnel interaction, and the last interaction. This model shows the impact of pivotal touchpoints throughout the customer journey.
Let’s suppose you’ve launched a marketing campaign for your gaming app. The user sees an Instagram ad, reads a blog post, gets an email, and finally clicks on a remarketing ad to download the app.
Here’s how different attribution models would handle this:
Linear attribution assigns equal credit to every touchpoint a customer interacts with before completing a desired action, providing a balanced view of your marketing efforts. For example, if you see an Instagram ad, read a blog post, get an email, and finally click on a remarketing ad to download an app, each touchpoint would receive 25% of the credit for the conversion.
Linear attribution offers a comprehensive look at the customer journey, encourages consistent engagement across all touchpoints, and helps in making data-driven decisions. It supports omnichannel strategies and ensures fair credit distribution, avoiding overemphasis on any single touchpoint.
To decide if linear attribution suits your needs, consider your marketing strategy’s complexity and goals. Linear gives equal credit to each touchpoint in a customer’s journey, making it perfect for multi-channel campaigns or long sales cycles where each interaction has equal influence. However, if some interactions are more influential, consider a more advanced model.
