Mobile attribution is a critical and complex component of the mobile marketing world. It is crucial in helping businesses and developers understand if all the efforts they are putting into their app marketing campaigns are effectively yielding the return they expect and hope for.
Mobile attribution refers to a technology that attributes installs and in-app events to advertisements and, consequently, to the sources/channels that generated them. It essentially serves the same purpose for mobile apps as what pixels do for tracking activities on websites.
This technology is important, if not fundamental, because it allows app developers to track their user acquisition efforts and measure the performance of their various marketing channels and partners. By using mobile attribution, developers can understand which advertisements or sources are driving the most installs and in-app actions, helping them make data-driven decisions about their marketing strategies and optimize their advertising campaigns.
It is in this context that AppsFlyer, one of the most employed mobile attribution platforms, has gained a prominent role, thanks to its unique features.
In this article, we’ll try to analyze its perks, always keeping in mind the bigger picture of the mobile attribution world.
- Attribution models
- Attribution technology: deterministic vs. probabilistic
- iOS 14.5 and App Tracking Transparency (ATT)
- SKAdNetwork vs. AppsFlyer’s Aggregated Advanced Privacy (AAP)
- KPIs measurements
Attribution models determine how credit is, in fact, attributed to different marketing clicks and impressions. Let's take a look at some of the most commonly used attribution models.
Click-Through Attribution
Click-through attribution focuses on the direct interaction between a user and an ad that leads to an app installation or conversion. When a user clicks on an ad and subsequently installs the app, click-through attribution attributes the success of this conversion to that specific click.
There is, though, a temporal window in which the attribution can be considered successful. If the install happens after this temporal window, it will not be attributed to that specific ad.
AppsFlyer supports click-through attribution and allows marketers to assess the effectiveness of their ad campaigns and optimize ad placement and content.
View-Through Attribution
View-through attribution tries to analyze the influence of display ads and video impressions on user actions. This model attributes conversions to users who have viewed an ad but did not click on it directly.
Just like click-through attribution, there is a temporal window in which the install has to take place in order for it to be attributed successfully. In this case, though, this window is shorter, and usually allows for successful attributions only within 24 hours.
AppsFlyer can also help understand the actual weight of impression-based advertising, thanks to its view-through attribution criteria.
Last click vs. multi-touch attribution
· Last click attribution is without a doubt the simplest attribution model. It assigns the success of a conversion or app installation solely to the last touchpoint that a user interacted with before the desired action. This model is surely very straightforward but it may carry the risk of oversimplifying the user journey, which means it can sometimes neglect the influence of other touchpoints that may have contributed to the conversion.
· On the other hand, a multi-touch attribution model recognizes the entirety of the journey, this way considering its rare linearity and its uniqueness. As a matter of fact, it considers all marketing touchpoints that have played a role in a conversion and distributes credit to each touchpoint based on its relative impact. The complexity of multi-touch attribution models can vary, with some giving more weight to certain touchpoints, while others distribute credit more evenly.
The technology behind mobile attribution is crucial for its accuracy. There are two main ways to do it: through deterministic technology and probabilistic technology.
· Deterministic attribution provides highly accurate attribution as it is based on actual ID matching using specific identifiers, referred to as IDFA (Identifier For Advertisers) for iOS users and as GAID (Google Advertising ID) for Android users. However, deterministic attribution comes with certain limitations, especially user-privacy-related challenges.
· Probabilistic attribution, on the other hand, employs statistical modeling to make educated assumptions about the identity of users across devices. In contrast to deterministic attribution, probabilistic attribution is less precise, but offers a more privacy-friendly approach.
Appsflyer supports both deterministic and probabilistic attribution, providing advertisers and their partners with the most complete mobile attribution technology.
In 2021, Apple's iOS 14.5 update brought significant changes to the mobile attribution world, particularly with the introduction of the App Tracking Transparency (ATT) protocol. This update obliged app developers to explicitly obtain users’ consent to track their data for personalized advertising.
In the case in which users negate the consent, that would prevent their IDFAs to be tracked, making deterministic attribution not applicable.
AppsFlyer had to adapt to this radical change and learn how to comply with this new requirement.
Following the launch of iOS 14.5, Apple came out with its own attribution platform, called SKAdNetwork, which allows advertisers to measure the effectiveness of their mobile ad campaigns while still protecting user privacy.
It operates within the Apple ecosystem and regulates the flow of user data to safeguard privacy. However, SKAdNetwork has its limitations, including limited post-install event tracking and delayed data reporting. And that is where AppsFlyer’s Aggregated Advanced Privacy platform intervenes to help.
The Aggregated Advanced Privacy platform (AAP) is a technology developed by AppsFlyer to address privacy concerns while still providing valuable attribution data. It's worth noting that while AppsFlyer allows for data aggregation from SKAdNetwork, they have also created their proprietary solution to ensure a level of privacy protection that is similar to what SKAdNetwork offers.
AppsFlyer's AAP is able to augment SKAdNetwork by offering a broader range of analytics and insights. This includes in-app events, user engagement, and cohort analysis. The ability to work seamlessly across multiple platforms and provide a complete view of user behavior and marketing performance makes AppsFlyer’s AAP really stand out.
App developers may find that AppsFlyer's AAP allows them to get a more comprehensive view of their app's performance, even beyond what SKAdNetwork can provide. The platform's flexibility and cross-platform features makes it an invaluable tool for developers’ efforts to optimize user acquisition and engagement.
AppsFlyer’s AAP allows businesses to collect and process data in a privacy-compliant manner, ensuring they can continue tracking and measuring app performance while respecting user privacy preferences. Through the integration of SKAdNetwork, Apple's privacy-centric attribution framework, navigation and user privacy preservation can coexist.
In addition to installs, AppsFlyer's attribution allows you to monitor essential metrics that provide valuable insights into your app's performance and the return on your marketing efforts.
Among several KPIs that AppsFlyer allows to analyze, let’s take a look at some very common and known ones.
- Retention Rate (RR) measures the percentage of users who continue to engage with your app over time. It is a crucial metric for assessing an app’s ability to retain users after the initial installation. Higher retention rates often indicate that your app offers value, resulting in users coming back for more.
- Average Revenue Per User (ARPU) is the average amount of revenue generated by each user over a given period. It is a fundamental metric for understanding the monetary value of your user base. ARPU is calculated by dividing the total revenue by the number of active users during the same period.
- Lifetime Value (LTV) represents the total expected revenue that a user is likely to generate throughout their entire engagement with your app. This metric helps you estimate the long-term value of your users and make data-driven decisions about user acquisition and retention strategies.
- Return on Investment (ROI) quantifies the profitability of your marketing campaigns. It measures the ratio of revenue gained compared to the costs incurred for running those campaigns. A positive ROI indicates that your marketing efforts are generating more revenue than they cost. It is a critical metric for gauging the overall effectiveness of your marketing strategies.
- Return On Ad Spend (ROAS) is a metric that quantifies the effectiveness of advertising campaigns by measuring the revenue generated in relation to the amount spent on those campaigns and helps understand the direct impact of advertising efforts on the app's revenue. A positive ROAS value indicates that your advertising campaigns are generating more revenue than they cost.
