Predictive LTV

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.

Cohort analysis is a method of analyzing data that involves grouping data sets by shared characteristics or experiences, typically within a specific time frame. The purpose of cohort analysis is to track changes or patterns in behavior over time and to gain insights into the factors that influence those changes.

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.

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.

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.