PostHog in Practice: Where Users Drop Off and What Works

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PostHog delivers the most value when you’re making app decisions on gut feeling and nobody knows what users actually do. We’ve collected use cases that turn guesswork into concrete numbers and recordings, from registration funnels to safely shipping new features.

For how to install PostHog and build your first funnel, see our PostHog tutorial.

Where to Start with PostHog

The best first use case is one funnel for the most important process in your app: registration, purchase, or first use of a key feature. One well-built funnel and a dozen watched session recordings of people who dropped off give you more than twenty charts nobody looks at.

Feature flags are worth introducing with your next major deployment. Gradually rolling out one feature convinces the team of this approach better than any presentation.

Where People Drop Off During Registration

The most common first use case. A funnel from landing on the registration page to account activation shows which step loses the most people. Instead of guessing whether the problem is the form, pricing, or confirmation email, you see numbers. Then you filter session recordings of people who dropped off at that step and watch what they did.

Session Recordings Instead of “Something’s Broken” Reports

A customer writes that something isn’t working but can’t describe what. A recording of their session shows exactly where they clicked and what they saw. Filters for frustrated repeated clicks and JavaScript errors let you find problems before anyone reports them.

Gradual Feature Rollouts

A new feature goes to a few percent of users first. If something goes wrong, you turn off the flag without a new deployment, and most users won’t notice anything. PostHog documentation recommends starting at 5 to 10 percent and gradually increasing.

A/B Tests on the Same Data

Two pricing page variants, two button texts, two form layouts. Experiments in PostHog use the same events as analytics, so you don’t need a separate tool. The metric can be funnel conversion, average value, or a custom indicator.

Which Features Are Actually Used

Teams often develop features almost nobody uses. A trend chart shows how often each feature is used, and retention analysis shows whether people come back to it. This is data that changes your priority list.

Analytics for AI-Built Apps

PostHog’s installation wizard works with AI assistants like Cursor. An app built with AI help gets analytics in minutes, which is especially useful for prototypes where you want to quickly check if anyone’s using them.

Shared Data for Product and Marketing

Marketing sees where users come from, and product sees what they do after arriving. In PostHog it’s one dataset, so conversations about which campaigns bring people who stay are based on the same numbers.

Example: New Pricing Without Risk

An invoicing app wants to test a new pricing page layout. The team creates an experiment with two variants, split 50/50, with a metric of progression from pricing to purchase. After two weeks the new layout has a clearly better result, but recordings show some people don’t understand one item. The team fixes the description, rolls out the new pricing gradually through a flag, and watches the funnel to see if the result holds.

This example shows that experiments and recordings work best together. The test result alone said the new pricing was better, but only recordings showed what could still be improved. Gradual rollout through a flag provided safety: if the result didn’t hold after deploying to everyone, reverting the change would take one click, without a new app release.

What to Watch Out For

• Session recordings and privacy: sensitive data, like password fields, should be masked.
• Too many events without a plan gives you lots of data and few answers.
• Conclusions from small samples in experiments can be misleading.

How to Tell If It’s Worth It

PostHog pays off when data changes decisions. After a month, write down which decisions you made based on funnels, recordings, or experiments, and what their effect was - for example, improved registration conversion. If there isn’t a single such decision, the problem isn’t the tool, it’s that nobody regularly looks at the data.

A good practice is a regular, short review once a week: one funnel, a few recordings, one insight.

Finally, a note on privacy, important for all these use cases. Session recordings and user behavior data are subject to data protection regulations, so it’s worth masking fields with personal data, describing analytics in your privacy policy, and collecting only what you actually need to answer your questions.

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