Get ready for iOS 14 with AppsFlyer

Glossary & FAQ
Clear answers to your iOS 14 related questions

Glossary

IDFA

The identifier for advertisers or IDFA is unique to each iOS device. IDFA is the standard that Apple adopted that allows mobile advertising networks to track users and serve them targeted ads. With iOS 14, both a publisher and a destination app must separately receive permission from the user to track them, in order to read the IDFA, making it an exclusively opt-in feature.

IDFV

The identifier for vendors or IDFV is an alphanumeric string that uniquely identifies a device to the app’s vendor. An IDFV is assigned and shared by all apps from the same company.

SKAdNetwork

SKAdNetwork is an attribution solution developed by Apple two years ago as a part of Apple’s StoreKit framework, designed to deliver aggregate, campaign-level insights to ad networks and advertisers. SKadNetwork allows only pre-registered ad networks to attribute installs by “flagging” the conversion without actually sharing the IDFA.
With the announcement of iOS 14 beta, Apple introduced a few updates to SKadNetwork as well. The following diagram describes the current path of an install validation. App A is the source app that displays an ad. App B is the advertised app that the user installs.

AppTrackingTransparency (ATT)

AppTrackingTransparency framework (ATT) is Apple’s IDFA opt-in mechanism. This mechanism will request user authorization to access app-related data via a prompt dialog.

FAQ

What are the key points introduced by Apple around iOS 14?

Among many exciting new features released in iOS 14, a key part of Apple’s announcement is privacy and making sure that a user cannot be tracked. One of the elements is that device ID (IDFA) usage will become opt-in with iOS 14 at the app-level. That means that once the privacy guidelines with iOS 14 are rolled out, app developers are required to ask for users’ permission before they can use the IDFA for measurement purposes.

How is AppsFlyer responding to these changes?

With the changes introduced by Apple in iOS 14, AppsFlyer has taken extensive measures to align the platform with Apple’s new guidelines. Various solutions have been introduced to align with Apple’s new privacy guidelines – AppsFlyer’s Privacy-Centric Attribution, SKAdNetwork support, Web-campaigns-to-app features, and more (see above for more info) placing advertisers in the “driver’s seat” by having full control on which data they want to share with integrated partners.

What are the additional capabilities delivered by AppsFlyer on top of SKAdNetwork?

The main highlights of our solution are:
Conversion events: Server side, dynamic, and flexible in-app event to conversion value configuration.
Data aggregation: Collecting all SKAdNetwork information from each ad network, on behalf of the advertiser.
Data validation: Ensuring all postbacks are signed by Apple and aren’t manipulated in transit.
Data enrichment: Matching SKAdNetwork information with other data points, such as impressions, clicks, cost, organic traffic, and more, for complete ROI analysis.
Data enablement: Facilitating SKAdNetwork data for convenient consumption by the advertiser, through dedicated dashboards and APIs.
Seamless integration: Full encapsulation, requiring close to zero effort from the advertiser, including for future changes in the SKAdNetwork protocol

Is AppsFlyer’s Probabilistic Modeling aligned with Apple iOS 14 guidelines?

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.

What is the difference between Fingerprinting and Probabilistic Modeling?

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:

	핑거프린팅	AppsFlyer’s Probabilistic Modeling
Equivalent to unique and/or persistent ID	예	아니요
Can be used to uniquely identify a device	예	아니요
Can be used to track users across sites/apps (cross-site tracking)	예	아니요
Relies on vast amounts of user-device and browser data	예	아니요
Can be used to create profiles	예	아니요
Can be used to target users	예	아니요
Can identify a device even when users hide behind VPNs or alternate IPs	예	아니요
Deterministic	예	아니요
프라이버시	Can be leveraged in intrusive ways	Friendly