Media Mix Modeling (MMM)

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.

Incremental lift testing (also called incrementality testing or hold-out testing) is a measurement methodology that determines the true incremental impact of a marketing campaign by comparing the behavior of a group exposed to ads against a control group that was not exposed. Unlike standard attribution, which can overcount conversions by crediting campaigns for users who would have converted organically, incrementality testing isolates the causal effect of advertising spend. In mobile app marketing, this typically involves withholding ads from a randomly selected subset of the target audience and measuring the difference in install rates, subscription conversions, or revenue between the exposed and holdout groups. The difference represents the true "lift" — the additional conversions directly caused by the campaign. In the post-ATT era, where traditional last-click attribution has become unreliable for iOS campaigns, incremental lift testing has gained importance as a privacy-safe way to measure campaign effectiveness. It answers the fundamental question: "Would these users have converted anyway without seeing my ad?"

Return on Ad Spend (ROAS) measures the revenue generated for every dollar spent on advertising. It is calculated by dividing total revenue attributed to a campaign by the total ad spend on that campaign. A ROAS of 2.0 means the campaign generated $2 in revenue for every $1 spent. ROAS is the most widely used profitability metric in mobile user acquisition because it directly connects marketing investment to revenue outcomes. For subscription apps, ROAS calculations must account for the time lag between ad spend and revenue realization — a user acquired today may not generate their first payment for 7 days (if they start with a free trial) and may generate revenue over months or years of subscription renewals. This makes "Day 0 ROAS" (revenue from immediate purchases) an incomplete picture. Growth teams track ROAS at multiple horizons — Day 7, Day 30, Day 90, Day 365 — to understand the full return curve. Target ROAS thresholds depend heavily on a company's margin structure: apps routing payments through app store billing (with 15–30% commissions) need higher gross ROAS than apps processing through web checkout to achieve the same profitability.

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.

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.