IDFV

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.

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.

First-party data is information collected directly by an app developer from their own users through interactions on owned channels — including the app itself, websites, web funnels, email communications, and customer support touchpoints. This data includes behavioral signals (screens viewed, features used, session duration), purchase history (subscriptions, transactions, plan changes), declared preferences (onboarding survey responses, settings), and device-level signals (OS, device model, app version). In the post-ATT privacy landscape, first-party data has become the most valuable data asset for mobile app businesses. Unlike third-party data, which is collected by external entities and is increasingly restricted by platform privacy policies, first-party data is consented, reliable, and privacy-compliant by nature. Subscription apps that build robust first-party data strategies can use this information to personalize onboarding experiences, optimize paywall presentations, build predictive LTV models, power targeted re-engagement campaigns, and create lookalike audiences for acquisition — all without depending on third-party cookies or mobile advertising identifiers that are being deprecated across the ecosystem.

A Mobile Measurement Partner (MMP) is a third-party analytics platform that provides independent attribution, analytics, and measurement services for mobile app marketing campaigns. MMPs track which ad clicks and impressions lead to app installs and post-install events, providing a unified view of marketing performance across multiple ad networks and channels. Major MMPs in the mobile ecosystem include AppsFlyer, Adjust, Branch, Singular, and Kochava. MMPs play a neutral intermediary role — since they are independent from both the advertiser and the ad networks, their attribution data is considered unbiased. Core MMP capabilities include last-touch attribution, multi-touch attribution, fraud detection, deep linking, audience segmentation, SKAdNetwork management, and cohort-based analytics. In the post-ATT landscape, MMPs have evolved to offer probabilistic modeling, predictive analytics, and aggregated measurement solutions that work within Apple's privacy constraints. For subscription apps, MMPs provide critical insights into which channels and campaigns deliver subscribers with the highest LTV, enabling marketers to shift budget toward the most profitable traffic sources.