PRIVACY-CENTRIC ATTRIBUTION
Combined deterministic and probabilistic attribution provide 100% coverage with privacy built in.
SDK FOR IOS 14
Unlock full iOS 14 readiness, supporting APIs, ATT framework, SKAdNetwork integration and more.
SKADNETWORK
Holistic SKAdNetwork management allows you to measure, visualize and optimize easily.
WEB-TO-APP UA
Tap into the web for new growth opportunities with measurable campaigns that offer seamless web-to-app journeys.
Don’t compromise on accuracy, coverage, or end-user privacy. Get the most out of the industry’s most advanced probabilistic algorithm, designed to deliver campaign-level data without infringing on user privacy. AppsFlyer’s probabilistic algorithm encompasses both extensive coverage and incomparable accuracy. Our model leverages our massive scale and engineering expertise, and is constantly improving based on industry-wide insights.
Advance to Advanced PrivacyAdvanced Privacy puts you in the driver’s seat with full control over your data, deciding exactly how it is collected, managed, and used by partners via data access permissions.
View simulations and visualizations of your critical performance KPIs. Explore advanced visualizations of performance KPIs, including ROI, CPI, ARPU, ROAS and more. With extensive insights, data trends and deep-dive metrics, you can make informed decisions about your iOS campaigns with confidence.
COMING SOON!
Take action quickly, overcoming the SKAdNetwork timer limitations. Based on early signs of user engagement, our predictive analytics engine lets you put mobile attribution on “autopilot”.
Make sure you’re getting real, accurate data on your campaign performance with data accuracy validation for SKAdNetwork. Protect your ad spend with 360-degree fraud coverage in the new iOS reality.
AppsFlyer continues to deliver seamless, self-serve integration with your chosen advertising partners. End-to-end cooperation ensures that postbacks, conversion value schemas and data are delivered to AppsFlyer and your chosen partners smoothly and easily.
“We’re excited to cooperate with AppsFlyer’s SKAdNetwork solution. We believe MMPs will remain a key solution for advertisers to keep their data actionable post-iOS 14.”
– Sheila Bhardwaj, Measurement Partnerships
Snap
“We’re excited to cooperate with AppsFlyer’s SKAdNetwork solution. MMPs continue to have an important role to play in the post-iOS 14 era, empowering advertisers to streamline implementation of SKAdNetwork and continue to access their data in an actionable way.”
– Yevgeny Peres, VP Growth
ironSource
“We’ve been working closely with AppsFlyer to deliver a fully operational plug-and-play SKAdNetwork solution in record time. The solution was designed together to validate all advertisers’ needs from SKAdNetwork are met: data trust, granularity, optimization and measurement. This is another exciting step for advertisers, delivering upon our promise of helping drive business success while maximizing end-user privacy.”
– Liwei Chen, Staff Product Manager
Twitter
Mobile websites are an excellent channel to educate, engage and drive growth among users, while maintaining full control over the user experience with the brand. Paid web campaigns that drive high-intent traffic to your website and convert to app installs can be efficiently measured with AppsFlyer, with full visibility into every step of the funnel.
A comprehensive guide for developing your first App Clip and elevating the iOS UX
SKAdNetwork doesn’t capture all non-organic attribution data. Our findings show that SKAdNetwork alone is not enough
How are AppsFlyer’s products impacted by iOS 14?
AppsFlyer’s Privacy-Centric Attribution provides full 360-degree visibility into campaign performance for all iOS versions by leveraging:
Deterministic attribution based on persistent device identifiers will still be available when the end-user accepts the ATT prompt on the source and target apps. In the absence of this identifier, AppsFlyer’s Probabilistic Modeling algorithm will provide data on campaign performance. In addition, AppsFlyer customers will be able to leverage SKAdNetwork, which is based on deterministic attribution. Together, AppsFlyer’s Privacy-Centric Attribution provides full 360-degree visibility into campaign performance for all iOS versions.
Remarketing post-iOS 14 is becoming more complex with various device identifiers being leveraged across different platforms and a newly introduced “remarketing opt-in” mechanism set forth by Apple. AppsFlyer Audiences fully supports additional device identifiers for audience creation beyond IDFA. This means that audiences can now be built and sent to partners based on any identifier advertisers wish to use. We have also added a new data governance layer to our Audiences tool, to allow advertisers to define exactly which identifiers should be sent to which partner, corresponding to the user’s most recent ATT opt-in status. We continue to provide a flexible audience management solution that is aligned with our partner’s targeting criteria and ensures a sustainable future for app advertisers.
There will be no impact to iOS 14 users deep linking from campaigns on ad networks and owned media. By default, AppsFlyer will use IDFA when available; when there is no IDFA, we will use Probabilistic Modeling. iOS 14 may affect deferred deep linking from ad network campaigns which rely on IDFA.
We advise customers seeking to implement deferred deep linking from ad networks and owned media to use AppsFlyer iOS SDK v.6.1.x to mitigate the overall impact, allow flexibility in ATT consent (if needed), and benefit from faster response times.
Customers running campaigns on SRNs should be aware that deep linking and deferred deep linking will not work when IDFA is unavailable. We advise customers seeking to retain attribution visibility with deep linking and deferred deep linking from SRN campaigns to use web-to-app flows.
We continue to offer seamless integration with 7,000+ ad networks, technology partners and analytics partners, while providing our customers with full transparency in regards to how their data is being used and shared by the integrated partners.
We are encouraging our partners to join our Advanced Privacy data sharing protocol to maximize end-user privacy. Partners that have enabled Advanced Privacy, as well as partners that are integrated with our SKAdNetwork solution are indicated in the Integrated Partners menu with dedicated badges. We recommend our customers minimize user-level integrations with partners, review data privacy practices and ensure that their partners are aligned with the updated Apple terms.
Other topics
SKAdNetwork alone is not enough; it is only one piece of the solution for iOS 14. SKAdNetwork still leaves many gaps (including misattribution of up to 32% of non-organic installs), as it only covers specific attribution flows within specific conditions. AppsFlyer’s complete iOS 14 solution, which encompasses SKAdNetwork, Privacy-Centric Attribution (Probabilistic Modeling and deterministic attribution) and Web-to-App solutions, provides full coverage for iOS attribution.
Fingerprinting:
Fingerprinting (often referred to as browser fingerprinting) is a term associated with the process of collecting a broad range of computer and browser information through a user’s web browser to uniquely identify a user and/or device. Fingerprints are used to identify a device when other persistent identifiers such as cookies cannot be read or stored by a website. A fingerprint is created by combining various data points from your device and/or browser that are accessible to every website when you visit their page. This could include the browser version, installed browser extensions, and plugins (including their versions), hardware properties, list of fonts, canvas and WebGL, HW benchmarking, language, time zones, OS version, screen characteristics, and menu bars.
Statistically, there is a very small chance that two or more devices will have identical set-ups and configurations, you can view how unique your configurations and setup are here. Fingerprints are especially strong given they can detect a device over an extended period even if certain parameters of the fingerprint are altered, with IP addresses changed, or even behind a VPN.
While fingerprinting techniques were initially created as a method for banks to detect fraud and prevent identity theft, today, they are used to track users across websites in order to compile long-term records of individuals’ browsing histories, and deliver targeted advertising or targeted exploits to users; thus, raising serious privacy concerns. For this reason browsers (Safari, Chrome, and Firefox to name a few) have recently begun making changes intended to limit the amount of data they expose to websites, and to make users look more alike, creating a kind of “herd immunity”.
The good news is that mobile browsers provide much less data, and already provide the aforementioned “herd immunity” against the type of fingerprinting common on desktops. iOS devices especially are much less fragmented, making them greatly immune to fingerprinting.
AppsFlyer’s Probabilistic Modeling:
Probabilistic modeling is a statistical technique to estimate campaign performance and can’t be used to uniquely identify a user and/or device. It leverages machine learning to estimate campaign performance without compromising on privacy. Unlike fingerprinting, which seeks to maximize captured data points from each user to create a unique identifier that can be used to track users over an extended period and across websites, AppsFlyer’s probabilistic modeling seeks to do the exact opposite; minimize the captured data points and prevent the ability to create a unique persistent or permanent identifier that can be used to identify a user and/or device.
Where fingerprints are used to create detailed profiles of users to enable precise targeting (at any point in time and on any site), probabilistic modeling is employed with the sole purpose of estimating campaign performance of paid and owned media (such as websites, social media platforms, emails, and user referrals).
Probabilistic modeling measures the app developers’ owned creative and campaign details, not data from the apps in which the ads are served. Moreover, in most cases, the app in which the ad was served is unknown.
Probabilistic modeling relies on very few data points that change frequently. For this reason, machine learning and statistical estimation techniques are used (as opposed to creating and matching unique ID’s) and why lookback windows cannot be defined.
As described above probabilistic modeling is a privacy-centric method to estimate ad campaign performance. It is very different from fingerprinting. It does not generate a unique ID that is persistent or permanent or that can uniquely identify any device across sites or apps over an extended period. It is not used for targeting or profiling. In fact, probabilistic modeling is one of the most privacy-friendly ways for attribution and estimating campaign performance.
The following table presents an easy way to view the significant differences between traditional browser fingerprinting and probabilistic modeling:
| Fingerprinting | AppsFlyer’s Probabilistic Modeling | |
|---|---|---|
| Equivalent to unique and/or persistent ID | Yes | No |
| Can be used to uniquely identify a device | Yes | No |
| Can be used to track users across sites/apps (cross-site tracking) | Yes | No |
| Relies on vast amounts of user-device and browser data | Yes | No |
| Can be used to create profiles | Yes | No |
| Can be used to target users | Yes | No |
| Can identify a device even when users hide behind VPNs or alternate IPs | Yes | No |
| Deterministic | Yes | No |
| Privacy | Can be leveraged in intrusive ways | Friendly |
AppsFlyer is a first-party software-as-a-service used by app developers and advertisers as an extension to their technology stack, similar to a CRM. AppsFlyer allows developers to manage, analyze, and secure their first-party end-user data, while complying with privacy regulations and platform policies, such as the ones recently introduced by Apple.
AppsFlyer’s probabilistic modeling is a statistical technique to estimate campaign performance and can’t be used to uniquely identify a user and/or device. It leverages machine learning to estimate campaign performance without compromising on privacy. Unlike fingerprinting, which seeks to maximize captured data points from each user to create a persistent or near-permanent unique identifier which can be used to track users over an extended period and across websites, AppsFlyer’s probabilistic modeling seeks to do the exact opposite; minimize the captured data points and prevent the ability to create a unique persistent or permanent identifier that can be used to track a user.
Probabilistic modeling is about answering a very simple question: did the consumers find value in my paid, earned, and owned media such as websites, social media platforms, emails, user referrals? Probabilistic modeling measures the app developers’ owned creative and campaign details, not data from the apps in which the ads are served. Moreover, in most cases, the app in which the ad was served is unknown. We believe that probabilistic modeling combined with our privacy-centric attribution, which enables aggregated attribution data integration with partners, is aligned with Apple’s iOS 14 guidance. With that said, we recommend our customers to review the Apple app developer agreement and guidelines, their partners’ integrations, and data collecting policies to ensure that their app is compliant with iOS 14 guidelines.
