What is last-click attribution?

Last-click attribution is a mobile attribution model that assigns 100% of a conversion’s credit to the final touchpoint, while earlier interactions are excluded. It assumes that the final engagement had the most direct influence on the user’s decision to convert.

In a typical user journey, a person may encounter multiple touchpoints before converting. For example, seeing a display ad, reading a review, clicking a social post, and finally tapping a search ad that leads to an app install or purchase. In this model, the last recorded engagement becomes the conversion source. This approach has historically been the default model across many ad and analytics platforms.

Last-click attribution is easy to set up, interpret, and communicate, giving marketers a clear understanding of which campaign, channel, or ad led directly to a conversion.

It works well for performance and direct-response campaigns, like paid search, retargeting, or affiliate marketing, where conversions happen soon after engagement. The model also supports quick optimization, helping app marketers identify which campaigns drive installs or in-app actions and adjust spend or creatives in real time.

By focusing solely on the last interaction, this model overlooks earlier touchpoints that build awareness or influence consideration. This narrow view can undervalue upper-funnel marketing and distort overall channel performance. The model also tends to favor closing channels, such as branded search, retargeting, or direct traffic, which often receive full credit even when other campaigns contributed earlier.

Additionally, last-click attribution lacks cross-device and cross-platform accuracy. Users frequently switch devices or environments before converting, but the model attributes the conversion only to the last device or session, masking the true multi-channel journey.

Different attribution models distribute conversion credit in various ways, helping marketers understand how each touchpoint contributes to a conversion. Common alternatives include:

• First-click attribution: This credits the first interaction that introduced a user to an app, product, or service. It highlights awareness-stage channels and helps identify what drives initial engagement, but it does not account for later interactions that influence conversion.
• Linear attribution: This divides credit equally across all touchpoints. It assumes every interaction contributes equally, providing a balanced but simplified view of complex user journeys.
• Time-decay attribution: This assigns greater weight to recent interactions, assuming that touchpoints closer to conversion are more influential.
• Position-based (U-shaped) attribution: This gives the first and last interactions the most credit, with the remainder distributed across middle touchpoints, recognizing both discovery and closing influence.
• Multi-touch attribution (MTA): Also known as multi-channel attribution, this model assigns equal or weighted credit across multiple interactions in a user’s journey, rather than attributing the conversion to a single touchpoint.
• Data-driven attribution (DDA): This uses machine learning and historical conversion data to determine how much each touchpoint contributes, assigning credit dynamically based on real performance patterns.

To get accurate insights from last-click attribution, use it purposefully and alongside complementary models and tools.

• Use selectively for lower-funnel measurement: Apply last-click attribution to channels that drive immediate conversions, such as search, retargeting, or affiliate campaigns. Avoid using it as the sole model for full-funnel analysis.
• Pair with other attribution models: Combine last-click with multi-touch, position-based, or data-driven attribution to capture the complete customer journey. Comparing model results helps identify undervalued channels and balance investments.
• Reevaluate models regularly: Review and test attribution models as AI-driven analytics and privacy regulations evolve. Regular updates ensure measurement accuracy and relevance.
• Integrate privacy-safe frameworks: Support last-click with privacy-compliant attribution methods. Combining deterministic, probabilistic, or aggregated data helps maintain accuracy in privacy-first environments.

Adjust primarily relies on deterministic, last-touch attribution to link app installs and in-app events to marketing engagements. Conversions are attributed to the final qualifying ad click or impression within the attribution window, unless a later engagement triggers reattribution. This provides a standardized way to credit the last interaction that led to a conversion. Last-click attribution in Adjust operates within defined attribution and reattribution windows, ensuring consistent crediting of installs, re-engagements, and other conversion events.

In addition to its last-touch framework, Adjust includes complementary tools such as multi-touch measurement and view-through attribution (VTA). These features help marketers analyze assisting interactions and impression-based contributions alongside click-based results, providing broader insight into campaign performance while keeping last-touch attribution at the core of measurement.

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