Cohort Analysis

App retention refers to the capacity of an application to sustain users' engagement and activity for a specified duration after they have downloaded and installed it on their devices. It gauges the proportion of users who revisit the app after their initial download and reuse it. Essentially, app retention is an indicator of user loyalty and demonstrates the app's ability to deliver a positive user experience and satisfy user requirements over time. Retention rates are typically measured on Day 1, Day 7, and Day 30. High app retention rates are critical for the success of an app because they suggest that users perceive value in the app and are inclined to continue using it in the future.

The percentage of users who stop using your mobile app over a specific time period. Churn rate is the inverse of retention rate and serves as a critical health indicator for any app business. For subscription apps, tracking churn is essential since even small reductions in churn can significantly impact revenue and customer lifetime value (LTV). Churn rate, also known as attrition rate, measures the percentage of users who stop using your mobile app over a specific period. In the context of mobile applications, churn represents users who disengage from your app—whether they uninstall it completely, cancel their subscription, or simply stop opening and interacting with it.

LTV meaning, Lifetime Value (LTV), is a performance indicator used to evaluate the total earnings generated by a customer throughout their entire tenure of using a mobile application. Historical data on user retention rates is often used to estimate the expected duration of user engagement. Having knowledge of what is LTV and the average LTV of your customers is crucial for executing successful marketing strategies. LTV in marketing for mobile apps is normally used to optimize revenue streams such as subscriptions, in-app advertising, and in-app purchases by determining the amount of money that can be spent on user acquisition while still being profitable.

Funnel analysis is the process of tracking and measuring how users progress through a defined sequence of steps toward a desired outcome, such as installing an app, starting a free trial, or completing a subscription purchase. Each step in the funnel represents a conversion point where some users proceed and others drop off. For mobile subscription apps, common funnel stages include: ad impression → ad click → landing page view → app install or web checkout → onboarding completion → paywall view → trial start → paid conversion. By measuring the conversion rate between each stage, product and growth teams can identify where the largest drop-offs occur and prioritize optimization efforts accordingly. Funnel analysis is essential for both in-app flows (onboarding to paywall to purchase) and web-to-app flows (ad click to landing page to web checkout to app activation). Advanced funnel analysis segments users by acquisition source, geography, device type, or cohort to identify which segments convert best and where each segment encounters friction.

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.