Last Click Attribution: the right model for your business?

What Is Last Click Attribution?

Definition

Last Click attribution is a model that credits the last interaction before a conversion. For example, if a user clicks on a Facebook ad, then does a Google search before making a purchase, Google Ads will receive 100% of the credit for the conversion.

Context: When, How, and Why Is It Used?

Last Click attribution gained prominence in the 2000s with the rise of online advertising platforms, particularly Google Ads. Its main advantage is its simplicity—it’s easy to implement and understand. It relies on the idea that the last interaction before the conversion is the one that triggered the final action (purchase, install, sign-up, etc.).

It is particularly used:

• By default, on many analytics tools (Google Analytics, Meta Ads, TikTok Ads, etc.)
• In e-commerce or direct performance contexts, where data volume is high and conversions are quick
• When journey data is incomplete: in the absence of a robust multi-touch solution, Last Click serves as a “default” model

However, this widespread use is based less on analytical relevance than on ease of implementation and technical compatibility with standard tools. Today, more and more marketers are questioning this model in light of fragmented customer journeys.

Advantages of Last Click Attribution

• Easy to Implement: Last Click is the simplest model to set up in an analytics tool or ad platform. It doesn’t require complex data processing or algorithmic modeling: only one touchpoint is considered, which simplifies analysis.
  
• Clarity in Performance Analysis: By giving 100% of the credit to a single source, the model avoids complex attribution splits across channels. This provides results that are easy to interpret and use, even for non-experts.
  
• Alignment with Default Platform Models: Many platforms (Google Ads, Meta, TikTok…) use a Last Click or derivative model by default. Working with this model ensures direct consistency with native tracking tools, without extra adjustments.
  
• Relevant for Short Conversion Cycles: When the decision-making cycle is very short (e.g., impulse purchase, mobile app download, post-click immediate conversion), the last touchpoint is often the one that truly influenced the conversion.
  
• Useful in Retargeting: Last Click can be useful for assessing the effectiveness of remarketing or retargeting campaigns, where the goal is to finalize an already-initiated action. In such cases, the last touchpoint often is decisive.
• Works in Limited Data Environments: When it’s not possible to track all touchpoints (e.g., no cross-device tracking, lack of first-party data), Last Click remains a “fallback” model that still allows for conversion attribution.
  

What Are the Challenges of Last Click Attribution?

Last Click attribution presents several key limitations, especially in environments where purchase journeys are multi-touch, cross-device, and influenced by many touchpoints.

Fragmented Customer Journey

Users interact with a brand through a multitude of channels: social media, email, search engines, influencers, display ads, CTV… They also switch devices (mobile, desktop, tablet). In this context, Last Click only credits a single touchpoint—the final one—and ignores everything that came before.

Consequences:

• Upper-funnel or engagement channels (e.g., TikTok, YouTube, Meta) may be undervalued or completely ignored
• Budget allocation strategies may overly favor bottom-funnel channels like retargeting or branded search
  

Analytics Based on Misleading Metrics

The Last Click model creates the illusion that only the last channel “converts,” leading to major biases in analysis.

Real-world example:
A user discovers an app via a TikTok ad, clicks on an email link later, then does a Google search to download the app. Google Ads receives all the credit—even though it merely captured an already warm intent.

Problems:

• KPIs are inflated for some channels and underestimated for others
• Performance looks good on paper but doesn’t reflect true campaign effectiveness
• Reporting becomes misleading: just because a channel closes the sale doesn’t mean it generated it

Ignores the Traditional Conversion Funnel

The conversion funnel includes several stages: Awareness → Consideration → Conversion → Retention.
Last Click attribution disregards this logic. It removes the temporal and progressive nature of engagement.

Implications:

• Upper funnel importance is neglected, despite its critical role in filling the conversion pipeline
• Campaigns are optimized for short-term wins rather than long-term growth
• Strategic imbalance occurs: campaigns perceived as less profitable (but essential for awareness or consideration) are stopped, harming the overall acquisition strategy

What Are the Alternatives to Last Click Attribution?

Multi-Touch Attribution (MTA)

The multi-touch model distributes conversion value across several touchpoints (first touch, intermediate interactions, last touch, etc.).

It offers a more complete view of the journey and enables more balanced budget decisions.
Example: A U-shaped model gives more weight to the first and last touchpoints, while others use linear distribution.

Single-Touch Attribution (excluding Last Click)

These models use only one touchpoint, but not necessarily the last.

• First Click: credits the very first interaction (useful for identifying acquisition sources)
• Linear Attribution: spreads value evenly, but remains simple

While imperfect, these alternatives may better serve certain goals (brand awareness, upper funnel, etc.)

Attribution Last Click vs First Click

How Do These Models Differ?

• Last Click focuses on the last channel touched before a conversion (purchase, sign-up, install…). It’s often used to assess bottom-funnel campaign effectiveness, targeting already-engaged users.
• First Click highlights the initial channel that triggered the user journey. It’s better suited for evaluating the impact of awareness or acquisition campaigns.
  

Summary:

When to Choose Which Model?

• Last Click: if your goal is closing or immediate profitability, and your decision cycle is short
• First Click: if you want to analyze top-of-funnel channels or drive awareness-based strategy
  

Remember:

What About SKAN & View-Through Attribution?

• SKAdNetwork (SKAN) is Apple’s attribution framework post-ATT. It follows a Last Touch logic, but with privacy constraints (delay, anonymity, etc.).
• View-Through Attribution (VTA) credits a conversion to an ad impression, even without a click. It’s useful for awareness or branding campaigns but requires strict conditions (attribution windows, frequency, etc.).

These approaches add complexity to attribution and further reveal the limits of Last Click in a post-IDFA world.

Key Concepts to Understand

• Attribution Window: the timeframe during which a conversion can be credited to a channel
• Touchpoint: any interaction between the brand and the user
• Cross-Device: ability to track user journeys across devices
• Incrementality: measures the true impact of a channel (vs. conversions that would have happened anyway)
• Probabilistic attribution: attributes a conversion to a channel on the basis of identifiable and verifiable data, such as a user ID or a tracked click.
• Deterministic attribution: relies on statistical estimates, crossing several signals (device, time, geolocation, behavior…) to deduce the probable origin of a conversion, without direct proof.
  

Key Takeaways

• Last Click is simple but reductive
• It ignores earlier interactions that are often critical
• It’s poorly suited to cross-device or multi-channel journeys
• Models like multi-touch or view-through provide more granular insight
• Choose the right model based on your marketing goals and data maturity

Conclusion

Last Click attribution has long been dominant due to its simplicity. But in today’s complex, multi-touch, multi-channel environment, relying on it alone leads to flawed strategic decisions. To truly optimize campaigns and allocate budgets effectively, it’s essential to explore alternative models—ones that are more nuanced and better reflect the reality of customer journeys.