Incremental Lift Testing

A/B testing (also called split testing) is a method of comparing two or more variations of a product experience — such as a paywall design, landing page layout, pricing structure, or onboarding flow — to determine which version performs better against a defined metric. In mobile app monetization, A/B testing is used extensively to optimize conversion rates, trial starts, subscription sign-ups, and revenue per user. Traffic is randomly split between variants, and statistical analysis determines whether differences in performance are meaningful or due to chance. Effective A/B testing requires a clear hypothesis, a single variable change per test, sufficient sample size, and patience to reach statistical significance before drawing conclusions.

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.

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.

Media Mix Modeling (MMM) is a statistical analysis technique that measures the impact of various marketing channels and activities on business outcomes — typically installs, revenue, or subscriptions — using aggregated historical data rather than user-level tracking. MMM uses regression analysis to estimate how much each marketing channel (paid social, search, TV, influencer, organic) contributes to overall results, while controlling for external factors like seasonality, competitive activity, and macroeconomic conditions. In the mobile app industry, MMM has experienced a significant revival driven by the privacy changes introduced by ATT. As deterministic user-level attribution has become increasingly limited on iOS, MMM offers a privacy-compliant alternative that doesn't require any user-level identifiers or consent. It works with aggregated spend and performance data, making it immune to tracking restrictions. While MMM has historically been associated with large enterprises and offline media planning, modern implementations using Bayesian statistical methods and tools like Meta's Robyn or Google's Meridian have made it accessible to growth-stage app companies. The main limitation of MMM is its reliance on historical data variance — it needs sufficient changes in spend levels across channels over time to produce reliable estimates.

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.