Lifetime Value (LTV)

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.

The LTV:CAC ratio measures the relationship between the lifetime value of a customer and the cost of acquiring that customer. It is one of the most important unit economics metrics for subscription app businesses. An LTV:CAC ratio of 3:1 or higher is generally considered healthy, meaning the revenue generated by a user over their lifetime is at least three times what it cost to acquire them. A ratio below 1:1 means the business is losing money on every customer acquired. This metric is critical for evaluating the sustainability of growth strategies — aggressive user acquisition spending only makes sense if LTV sufficiently exceeds CAC. For subscription apps, improving the LTV:CAC ratio can be achieved from both sides: increasing LTV through better retention, pricing optimization, and upselling; or decreasing CAC through more efficient ad spend, higher organic install share, and web-based checkout flows that avoid platform commissions. Investors scrutinize this ratio closely when evaluating subscription businesses, as it directly indicates whether the company can scale profitably.

The amount spent for each newly-acquired mobile app user over a given time period. Customer Acquisition Cost (CAC) for mobile apps refers to the cost that a business incurs to acquire a new user for their mobile application over a specific period of time. In other words, it is the amount of money spent to attract a user to download and install a mobile app on their device. Whether you are just embarking on your journey or navigating quite a while to create a successful mobile app, the Customer Acquisition Cost (CAC) must be at the forefront of your mind.

ARPU stands for Average Revenue per User, and it refers to the average amount of revenue an app generates from each active user. App growth teams that develop subscription or revenue-driven apps often include ARPU as a key performance indicator to measure their financial success. By calculating ARPU, you can determine the average amount of money you earn from each user. While ARPU takes into account the revenue earned from both paying and non-paying users, there is another similar metric used specifically for subscription-based apps. This metric is known as ARPPU (Average Revenue per Paying User), which only considers the revenue generated by users who have made a payment.

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.