First-Party Data

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.

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.

Predictive LTV (pLTV) is the use of statistical models and machine learning to forecast the future lifetime value of a user or cohort based on their early behavior signals. Rather than waiting months or years to observe a user's actual LTV, predictive models estimate what a user will spend over their entire lifecycle using data available within the first hours, days, or weeks after acquisition. Common input signals include acquisition source, geographic location, device type, onboarding completion, early engagement patterns, initial purchase behavior, and session frequency. For subscription apps, predictive LTV models typically forecast metrics like probability of trial-to-paid conversion, expected subscription duration, likelihood of plan upgrade, and projected total revenue. Predictive LTV is critical for real-time user acquisition optimization — by estimating the long-term value of users from a particular campaign early, growth teams can dynamically adjust bids, budgets, and targeting without waiting for actual revenue to materialize. This is especially important in the post-ATT landscape where campaign-level feedback loops are slower and less granular. Advanced implementations use predictive LTV signals to personalize paywall offers, onboarding flows, and re-engagement messaging in real time.

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.

Cohort analysis is a method of analyzing data that involves grouping data sets by shared characteristics or experiences, typically within a specific time frame. The purpose of cohort analysis is to track changes or patterns in behavior over time and to gain insights into the factors that influence those changes.