What is last-click attribution?

Last-click attribution is a common way to understand where credit for a sale or conversion should go. It works by giving all the credit to the final touchpoint a customer engaged with before they converted. For example, if someone clicked on a Google ad, then later clicked on a Facebook ad, and finally clicked on an email link right before buying something, the email link would get all the credit for that sale. This model is popular because it is simple to understand and easy to set up in most analytics platforms. It gives a clear, straightforward answer to the question: "What was the very last thing that made them convert?" However, while simple, it often doesn't tell the whole story of how a customer decided to buy, as many other interactions might have happened along the way.

How last-click attribution works

Last-click attribution operates on a simple principle: the final interaction before a conversion receives full credit. Imagine a customer's journey looking like this: they see a social media ad, then search on Google and click an organic search result, then click a paid search ad, and finally click an email link that leads to a purchase. In this scenario, the email link would be attributed 100% of the conversion value. All previous touchpoints, like the social media ad, organic search, and paid search ad, would receive no credit in a last-click model. This model is often the default in many advertising platforms because it is easy to track and report. It helps marketers quickly see which immediate actions are driving conversions at the very end of the customer journey. For example, if you are running a retargeting campaign, last-click attribution can show you how effective those final ads are at bringing customers back to complete a purchase.

Why it matters and its limitations

Last-click attribution matters because it provides a clear, actionable insight into the immediate drivers of conversion. It is particularly useful for optimizing campaigns designed to capture demand from customers who are already close to making a purchase. For instance, if a specific paid search ad consistently shows up as the last click before a sale, you know that ad is effective at closing deals.

The missing journey

However, its biggest limitation is that it ignores all the earlier steps in a customer's journey. Most purchases are not spontaneous; customers often interact with multiple marketing channels over days or weeks before converting. A last-click model fails to acknowledge the awareness and consideration phases. For example, a customer might first discover a product through a blog post (content marketing), then see a display ad (paid advertising), and finally convert after clicking a brand search ad. Last-click would only credit the brand search ad, overlooking the initial blog post and display ad that introduced the product and built interest.

Impact on strategy

Relying solely on last-click attribution can lead to misallocated marketing budgets. Marketers might overinvest in channels that generate last clicks, such as retargeting or brand search ads, while underfunding channels that are crucial for creating initial awareness and nurturing leads, like content marketing, social media, or top-of-funnel display ads. This can result in a short-sighted strategy that prioritizes immediate conversions over long-term customer acquisition and brand building.

When to use it and alternatives to consider

Last-click attribution can be a suitable model for businesses with very short sales cycles or for specific campaign objectives, such as a flash sale where the goal is to drive immediate action. It is also useful for quick analysis of bottom-of-funnel campaign performance. However, for a more complete understanding of customer behavior, it is wise to consider other attribution models.

Exploring other models

Many other attribution models exist, each offering a different perspective on the customer journey:

• First-click attribution: Gives all credit to the very first interaction.
• Linear attribution: Distributes credit equally across all touchpoints.
• Time decay attribution: Gives more credit to touchpoints closer in time to the conversion.
• Position-based (U-shaped) attribution: Gives more credit to the first and last interactions, with less credit to middle interactions.
• Data-driven attribution: Uses machine learning to assign credit based on actual data from your account, often seen as the most sophisticated option.

By exploring these alternatives, marketing teams can gain a more nuanced view of which channels and interactions truly contribute to conversions throughout the entire customer journey. This helps in making more informed decisions about budget allocation and campaign optimization. In summary, last-click attribution is a simple and widely used model for assigning conversion credit, focusing on the final customer interaction. While easy to implement, it has significant limitations by ignoring the full customer journey. Understanding its pros and cons, and exploring alternative attribution models, is key for any marketing team aiming for comprehensive campaign analysis and effective budget allocation.