Funnel Analysis

Funnel drop-off rate measures the percentage of users who abandon a conversion process at each stage before reaching the final desired action. It is the inverse of stage-to-stage conversion rate. For example, if 1,000 users view a web landing page and only 300 click through to the checkout, the drop-off rate at that stage is 70%. Identifying stages with the highest drop-off rates reveals the biggest optimization opportunities. In web-to-app subscription flows, common high-drop-off points include: the transition from landing page to checkout (often caused by poor value proposition communication or unexpected pricing), the payment form itself (friction from too many fields or lack of trusted payment options), and the post-purchase app activation step (where users must download and open the app to access what they paid for). Reducing funnel drop-off by even a few percentage points at critical stages can have an outsized impact on overall conversion rates and revenue, since improvements compound across the funnel.

Mobile app conversion rate (CR or CVR) is the percentage of users who achieve a specific goal within the app, such as making a purchase, creating an account, subscribing to a service, or completing a survey. Conversion rates are an important metric for mobile app developers and marketers as they indicate the effectiveness of the app in driving user engagement and achieving business goals. A higher conversion rate typically means that the app is successfully guiding users towards the desired action, while a lower conversion rate may indicate that there are issues with the user experience or the app's marketing strategy.

A web funnel is a structured, multi-step web-based experience designed to guide users from initial awareness to a conversion action — typically a subscription purchase, trial start, or app download. In mobile app marketing, web funnels have become a critical acquisition and monetization strategy, particularly for subscription apps looking to process payments outside the App Store or Google Play to avoid platform commissions of 15–30%. A typical web funnel for a mobile subscription app follows a sequence like: ad click → landing page → value proposition/quiz/personalization flow → pricing/plan selection → web checkout → payment confirmation → app download prompt → in-app activation. By processing the subscription on the web before directing the user to the app, developers retain significantly more revenue per subscriber. Web funnels also offer advantages beyond commission savings: they provide a larger canvas for communicating value (compared to a constrained in-app paywall), enable more sophisticated A/B testing of pricing and messaging, allow for richer attribution data (since web tracking isn't subject to ATT restrictions in the same way), and support more flexible offer structures (downsells, upsells, bundles) that aren't possible within native app store billing. The main challenge of web funnels is the additional friction of requiring users to complete a purchase flow on the web and then separately download the app — making seamless handoff and activation critical to overall funnel performance.

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.

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.