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Mobile attribution is a method for determining which campaigns, media partners, and channels delivered specific app installs.
Mobile attribution is the process of tying app installs to marketing activities, such as ads or campaigns, that lead users to install or engage with a mobile app. Think of it as the detective work behind tracking a user’s journey from seeing an ad to taking action, like downloading your app or making an in-app purchase.
For instance, if a user sees an ad on Instagram and installs the app afterward, mobile attribution tools track this sequence and credit the app install to that specific Instagram ad.
With mobile ads being such a massive part of digital ad spending — expected to hit $247.68 billion by 2026 — getting attribution right is key to optimizing your marketing performance.
Mobile attribution is key to unlocking smarter marketing strategies. Without it, you’re left in the dark about which campaigns are actually driving results, leading to wasted ad spend and missed opportunities. It highlights what’s working and what’s less successful, helping you double down on high-ROI channels while cutting back on the underperforming ones.
Mobile attribution also tracks how in-app events impact the bigger picture, ensuring every campaign is optimized for success.
What’s more, mobile attribution is crucial in improving user acquisition and retention. Knowing which ads bring in high-quality users allows you to refine your strategy for better engagement and higher lifetime value. With mobile now accounting for a remarkable 96.5% of all US native display ad spending, proper attribution ensures you’re making the most of your budget.
Here’s a step-by-step breakdown of the mobile attribution process:
Mobile attribution kicks off when a user interacts with an ad on their mobile device. This could be anything from clicking a banner to watching a video ad, or even just seeing an ad (view-through attribution). Each interaction forms a “touchpoint” — a crucial data point for tracking user behavior.
When the user interacts with the ad, the ad platform (Facebook or Google Ads, for example) captures key data points, such as:
Note: These data points are necessary to match the user’s journey later in the process.
After the user clicks the ad, they’re usually redirected through a URL or a link that contains tracking parameters (UTM tags or other custom parameters). This allows the mobile attribution provider to log the click before forwarding the user to the app store.
For example, if the ad uses an attribution SDK (software development kit) like AppsFlyer, this SDK is integrated into the app’s code. The SDK captures the user’s interaction with the ad and logs it into the system, assigning it a unique tracking ID.
Fun Fact: As of March 2024, AppsFlyer is the go-to attribution SDK for Android apps, leading the market with 48.51% of all installs.
Once the user is redirected to the app store (typically Google Play or Apple’s App Store), they can download and install the app. When the user first opens the app, the attribution SDK captures the install event.
During this step, the SDK collects essential data such as the install timestamp, device information, and any additional parameters (like campaign IDs) that help match the install to the original ad click.
Once the app is installed, the SDK continues to track what the user does in the app — whether it’s making an in-app purchase, signing up, or other key actions. This post-install data helps in multi-touch attribution, where credit is given to multiple ads or touchpoints that guided the user along the conversion path.
This is where the “attribution” happens. The system tries to match the install or conversion event back to the original click or ad interaction.
There are two main methods:
An “attribution window” is an important concept in mobile attribution. It’s the time frame during which a user’s interaction with an ad can be linked to a conversion.
There are two types of attribution windows:
Once the attribution provider (like AppsFlyer) determines which ad or marketing channel is responsible for the conversion, they send the data back to the advertiser in a process called postback. The attribution provider compiles all the touchpoints and conversion data, creating detailed reports that allow marketers to evaluate their campaigns’ performances.
With the attribution data in hand, marketers can now optimize their campaigns. They can monitor which channels are performing well, adjust ad spend, and even create segmented campaigns to retarget users who didn’t convert initially.
Multi-touch attribution becomes particularly useful here, allowing marketers to see the full journey — not just the first or last interaction — so they can better optimize for future conversions
Attribution models determine how credit for a conversion is spread across the various touchpoints a user interacts with. Each model weighs these interactions differently, helping marketers understand which channels are pulling their weight in driving conversions.
The right model for you will depend on your business and the specific insights you’re looking for — we’ll discuss this in more detail later.
First-click (or first-touch) attribution assigns 100% of the credit for a conversion to a user’s first interaction with a company or an ad. It’s great for measuring how effective your initial touchpoints are at creating awareness, but doesn’t account for other interactions that may influence the conversion down the line.
In last-click (or last-touch) attribution, all of the conversion credit is given to the final touchpoint a user interacts with before completing the desired action (such as purchasing or downloading an app). This method is widely used (especially in eCommerce) because of its simplicity, but it ignores earlier touchpoints that set the stage for the conversion.
Multi-touch attribution (MTA) divides credit among multiple touchpoints that occur throughout the user’s journey. Depending on the specific rules, credit can be distributed evenly or weighted based on the relative importance of each interaction. MTA provides a more holistic view of the user journey, giving marketers a better understanding of the contributions of different channels.
Time-decay attribution assigns more credit to touchpoints closer in time to the actual conversion. This model assumes that more recent interactions are more influential, making it particularly useful in campaigns with a long sales cycle. Earlier touchpoints still get some credit, but their influence diminishes over time.
The U-shaped model (a form of position-based attribution) gives the majority of credit to the first and last touchpoints in the user journey, usually around 40% each. The remaining 20% is distributed among the middle interactions. This model is helpful for marketers who want to emphasize the importance of both awareness and conversion, while still acknowledging the role of interim interactions.
Building on the U-shaped approach, the W-shaped model gives significant credit to an intermediate touchpoint that represents a key milestone — like signing up for a product demo or attending a webinar. Typically, 30% of the credit goes to the first, middle, and final touchpoints, with the rest divided among the others. This model works well for B2B sales cycles that have key milestones before a final conversion.
View-through attribution gives credit to display ads that users see but don’t immediately click. If a user later converts through another channel, part of the credit is still assigned to the ad they only viewed. It’s a solid model for gauging the impact of display or video ads on brand awareness, even when they don’t result in direct clicks.
Each mobile attribution model emphasizes different touchpoints, which can drastically change how you interpret campaign performance. Here’s how you can decide which model suits your needs:
The first step is understanding what you want to achieve with your attribution analysis. Are you focusing on:
Grasping how complex and lengthy your customer’s journey is makes all the difference.
Got a product that requires several touchpoints and longer sales cycles? Multi-touch or W-shaped attribution models can give you a full picture of every interaction along the way. But if the journey’s more straightforward and conversions happen fast, a simple last-click or first-click attribution model will do the job just fine.
Here’s an overview of the different attribution models to help with your decision:
| Attribution Model | Best for… | Advantages |
| First-click attribution | Brand awareness, initial engagement focus | Understand the first touchpoint |
| Last-click attribution | Direct conversions, short decision cycles | Simple, highlights final conversion driver |
| Multi-touch attribution | Long customer journeys, complex funnels | Covers every step of the journey |
| Time-decay attribution | Long journeys, recency focus | Prioritizes recent interactions |
| U-shaped attribution | Lead generation, balancing awareness and conversion | Gives weight to both first and last touchpoints |
| W-shaped attribution | B2B sales cycles, key milestones | Emphasizes critical conversion touchpoints |
| View-through attribution | Display/video ad performance | Credits non-clicked impressions |
Mobile attribution methods may sound simple on paper, but in practice things aren’t always so straightforward. Here are a few pitfalls to be aware of:
When you run a campaign across different platforms — say, an ad network and an attribution provider — you might notice that they give you completely different results. That’s known as a discrepancy.
The issue often stems from different tracking methods, like how each platform measures conversions. For example, one might credit a display ad, while another attributes the same conversion to a paid search ad. This mismatch can throw off your ability to make informed decisions and optimize your campaigns.
Sometimes marketers feel the urge to tweak attribution models to fit their own expectations. While that may sound reasonable, it introduces bias. Manual adjustments can distort the objective data attribution tools are designed to provide. Over-reliance on these tweaks makes it harder to get a true picture of performance, ultimately leading to misguided optimization based on skewed data.
Ad fraud is every digital marketer’s nightmare. Fraudsters inflate your metrics by faking clicks, installs, or in-app events, leaving you with wasted ad spend and misleading data. Tactics like click spamming (flooding systems with fake clicks) or click injection (sneaking fraudulent clicks right before an install) make it nearly impossible to determine real user interactions.
Privacy regulations are making it increasingly difficult to track user behavior. With Apple’s App Tracking Transparency (ATT) from iOS 14.5, users now have to opt in for apps to track them across other apps and websites. The result? Only about 21% of users agree to this kind of tracking, as of 2022.
Google is also tightening privacy, with plans to phase out third-party cookies, which further complicates tracking users across devices and channels.
These shifts mean marketers need to adapt by using privacy-friendly attribution techniques, like iOS’s SKAdNetwork. But here’s the trade-off: these newer methods offer less detail, making it harder to create personalized marketing strategies. Instead, marketers are left to work with aggregated data, figuring out how to measure campaign success without crossing privacy lines.
Mobile attribution helps you understand which marketing campaigns and channels are getting results, like app installs or conversions. When you know what’s working, you can focus your budget on high-ROI activities and cut down on wasted ad spend.
Attribution in a mobile app tracks a user’s journey — from tapping on an ad to installing or engaging with the app. This enables marketers to give credit where it’s due, identifying which campaign or channel drove the action.
Mobile attribution is challenging due to issues like discrepancies between platforms, fraud (such as click spamming), and evolving privacy regulations (like Apple’s App Tracking Transparency), which limit the ability to track users across apps and devices.
