Test changes with statistical significance
boosted engagement by 40%
"Y Combinator uses PostHog's experimentation suite to try new ideas, some of which have led to significant improvements."
increased registrations by 30%
"This experiment cuts that in half to a 30% drop-off – a 50% improvement without a single user complaining!"
unthrottled event ingestion from a previous analytics provider, leading to better insights
"PostHog, which can do both experiments and analytics in one, was clearly the winner."
Conversion funnels or trends, secondary metrics, and range for statistical significance
Targeting & exclusion rules
Set criteria for user location, user property, cohort, or group
Automatic suggestions for duration, sample size, and confidence threshold in a winning variant
Built on Feature FlagsAll the benefits of feature flags with added functionality around stat-sig experiments
JSON payloadsModify website content per-variant without additional deployments
Split testingAutomatically split traffic between variants
Multivariate testingTest up to 9 variants against a control
Dynamic cohort supportAdd new users to an experiment automatically by setting a user property
Answer all of these questions (and more) with PostHog A/B Testing.
Use A/B testing free. Or enter a credit card for advanced features.
Either way, your first 1,000,000 requests are free – every month.
No credit card required
All features, no limitations
Boolean feature flags
Multivariate feature flags & experiments
Persist flags across authentication
Multiple release conditions
Release condition overrides
Flag targeting by groups
Local evaluation & bootstrapping
Flag usage stats
Funnel & trend experiments
Secondary experiment metrics
First 1 million requests
50 million +
So, what's best for you?
Reasons a competitor might be better for you (for now...)
- No-code experiments or CMS capabilities
- You'll still need a designer/engineer to create experiments
- No integration with Google Ads
- PostHog can't run ad experiments, or target users into an experiment based on an ad variant engagement.
Reasons to choose
- Integration with other PostHog products
- Attach surveys to experiments or view replays for a test group. Analyze results beyond your initial hypothesis or goal metric.
- Automated recommendations for sample sizes and runtime
- Automatic significance calculator – to help you figure out the winning variant as quickly as possible
- Robust targeting and exclusion options, including cohorts and location
- Anything you monitor in analytics, you can target in an experiment
Visit the tutorials section for more.
Running experiments on new users
Optimizing the initial experience of new users is critical for turning them into existing users. Products have a limited amount of time and attention from new users before they leave and churn.
How to set up A/B/n testing
A/B/n testing is like an A/B test where you compare multiple (n) variants instead of just two. It can be especially useful for small but impactful changes where many options are available like copy, styles, or pages.
How to run holdout testing
Holdout testing is a type of A/B testing that measures the long term effects of product changes. In holdout testing, a small group of users is not shown your changes for a long period of time, typically weeks or months after your experiment ends.
How to do A/A testing
An A/A test is the same as an A/B test except both groups receive the same code or components. Teams run A/A tests to ensure their A/B test service, functionality, and implementation work as expected and provides accurate results.
Install & customize
Here are some ways you can fine tune how you implement A/B testing.
Explore the docs
Get a more technical overview of how everything works in our docs.
Meet the team
PostHog works in small teams. The Feature Success team is responsible for building A/B testing.
Roadmap & changelog
Here’s what the team is up to.
Open-ended choice added for single-choice surveys
We've added a new option to user surveys so that you can give users an open-ended 'Other' field when selecting responses in a single-choice survey.
Open-ended choices are especially helpful for capturing detail on edge-cases, for example when a user can't give a response that covered by another available option.
Feature Success Analysis
Bringing together different parts of PostHog (flags, replay, surveys) to allow users to better analyse the success of a new feature.
Users & recordings linked to feature flags
We want to make it easier for those who use feature flags to get information on users attached to a particular feature flag, and gather more information on those users' experience through session recordings.
See more questions (or ask your own!) in our community forums.
- Question / TopicRepliesCreated
PostHog products are natively designed to be interoperable using Product OS.
Run analysis based on the value of a test, or build a cohort of users from a test variant
Watch recordings of users in a variant to discover nuances in why they did or didn’t complete the goal
Make changes to the feature flag the experiment uses - including JSON payload for each variant
This is the call to action.
If nothing else has sold you on PostHog, hopefully these classic marketing tactics will.