Analytics

Funnel Analysis 101: Find and Fix Drop-Off Points

September 17, 2026 12 min read

What is Funnel Analysis?

Funnel analysis follows a defined sequence of user actions—such as link visit, install, sign-up, and purchase—to show where progress slows or stops. It helps you ask better questions; it does not, by itself, explain why people leave.

Illustrative e-commerce funnel

Hypothetical counts to show how a funnel is read; not an industry benchmark.

1. Click on Ad 10,000 users (100%)
100%
2. Install App 4,000 users (40%)
40%
60% did not reach the next step in this example.
3. Complete Onboarding 2,800 users (28%)
28%
30% did not reach the next step in this example.
4. First Purchase 840 users (8.4%)
8.4%

Common Drop-Off Points

1. Link visit to install

Causes:

  • Slow App Store loading
  • Poor app rating/reviews
  • Large app size
  • Mismatch between ad and app description

Fixes:

  • Optimize app store listing
  • Reduce app size
  • Align ad messaging with app value prop

2. Install to onboarding complete

Causes:

  • Lengthy onboarding flow
  • Required account creation
  • The destination promised by the link is not preserved after install

Fixes:

  • Skip onboarding with deep links
  • Delay account creation
  • Progressive onboarding

Build a funnel you can act on

  1. Name each step using events your product actually records.
  2. Choose whether the funnel is open or requires steps in a fixed order, and document the conversion window.
  3. Break results down only by useful dimensions such as campaign, platform, or app version.
  4. Investigate the largest meaningful drop-off, then test one change and compare against a baseline.

Use the Funnel Analysis page to review funnel performance in Lativiq. Confirm that the events and date range match your question before acting on the results.

Turn a drop-off into a test

If users leave between install and viewing shared content, test whether preserving the intended destination improves that step. Check the link-routing experience and measure the same event before and after; don’t assume correlation alone proves the change caused the difference.

Conclusion

Funnel analysis identifies where to investigate next. Pair the observed drop-off with user feedback and controlled tests, then track whether the change improves the outcome without harming later steps.

Frequently Asked Questions

A conversion funnel in mobile apps is a sequence of steps users must complete to reach a desired outcome — such as: click ad → install app → complete onboarding → make first purchase. Funnel analysis shows how many users enter each step and what percentage drop off before completing it, revealing the biggest bottlenecks in your user journey.

There is no conversion-rate target that applies to every app. Results depend on the product, purchase model, audience, event definition, and measurement window. Compare the same funnel definition over equivalent periods, then investigate changes rather than treating a generic benchmark as a goal.

Instrument the events your funnel needs using the tracking methods supported by your app and analytics setup. Then use Lativiq’s Funnel Analysis page to choose the steps and date range available to your account. Verify event names and data coverage before interpreting the conversion rates.

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