Fingerprinting (Probabilistic Attribution)

Mobile attribution is the process of connecting app installs and in-app actions to specific marketing campaigns, ads, or channels that drove them. It enables marketers to understand which advertising efforts deliver results, optimize ad spend across channels, and make data-driven decisions about user acquisition strategies.

App Tracking Transparency (ATT) is Apple's privacy framework, introduced with iOS 14.5 in April 2021, that requires apps to request explicit user permission before tracking their activity across other companies' apps and websites. When an app wants to access a user's IDFA (Identifier for Advertisers) for targeted advertising or cross-app attribution, it must display a system prompt asking the user to opt in. Industry-wide opt-in rates have hovered around 20–35%, meaning the majority of iOS users are now untrackable via traditional deterministic methods. ATT fundamentally disrupted mobile attribution and user acquisition by limiting the data available for campaign optimization, audience targeting, and performance measurement. This shift forced the industry to adopt new attribution frameworks like SKAdNetwork, invest in first-party data strategies, explore probabilistic modeling, and seek alternative monetization channels — such as web-based funnels — that operate outside the ATT-restricted ecosystem. For subscription apps, ATT's impact on attribution accuracy has made it significantly harder to measure true LTV by acquisition channel, increasing the importance of predictive modeling and server-side analytics.

IDFA, short for Identifier for Advertisers, is a distinct and unpredictable alphanumeric code that is assigned to each iOS device by Apple. Advertisers use this identifier to deliver targeted ads and measure the effectiveness of their advertising campaigns. With the changes to Apple's privacy policy, app developers are now required to explicitly ask for user permission to track their IDFA. Users can choose to opt out of IDFA tracking by turning on the "Limit Ad Tracking" (LAT) option in their device's settings.

SKAdNetwork (SKAN) is Apple's privacy-preserving attribution framework for measuring the effectiveness of advertising campaigns that drive app installs on iOS. Introduced as an alternative to IDFA-based tracking after ATT severely limited user-level tracking, SKAN provides aggregated, anonymized attribution data directly from Apple's servers — without revealing any information about individual users. Under SKAN, when a user clicks an ad and installs an app, Apple validates the install and sends an attribution postback to the ad network after a delay, containing limited information: the ad network ID, campaign ID, and a conversion value set by the developer. The conversion value (initially 6 bits allowing 64 possible values, expanded in SKAN 4.0 with coarse and fine values) is the developer's only mechanism for encoding post-install user behavior into the attribution signal. This means developers must carefully decide which events to encode — such as whether the user started a trial, made a purchase, or reached an engagement threshold — within a constrained time window. SKAN's design prioritizes user privacy through delayed, aggregated reporting with built-in noise, which makes campaign-level optimization significantly more challenging than traditional attribution methods.

A lookback window (also called an attribution window) is the defined time period during which an ad click or impression can be credited for a subsequent app install or conversion event. If a user clicks an ad but doesn't install the app until after the lookback window has expired, the install is classified as organic rather than attributed to the campaign. Standard lookback windows vary by platform and ad network: click-through attribution windows are typically 7–30 days, while view-through (impression-based) attribution windows are usually 1–24 hours. The length of the lookback window significantly affects how marketing performance is measured. Shorter windows produce more conservative attribution (fewer attributed installs) but higher confidence that the ad actually influenced the decision. Longer windows capture more potential influence but increase the risk of over-crediting ads for installs that would have happened organically. In the SKAdNetwork framework, Apple uses fixed attribution windows — initially 24 hours for click-through and not supported for view-through — which is significantly more restrictive than the industry standard and has forced marketers to rethink how they measure campaign performance on iOS.