> ## Documentation Index
> Fetch the complete documentation index at: https://documentation.qonversion.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> A/B testing to validate your monetization hypothesis for every paywall element from UI to pricing

Qonversion Experiments are designed for revenue-focused A/B testing for subscription apps to quickly discover if **new pricing, paywall UI, or even new onboarding flow positively impacts your app's revenue**. Qonversion Experiments can be used with your existing subscription payments flow. You can start running advanced subscription app experiments to validate your growth hypothesis in under 30 minutes.

<img src="https://mintcdn.com/qonversion/eOsiYIQAgYr1cnnF/images/docs/8f69580-image.png?fit=max&auto=format&n=eOsiYIQAgYr1cnnF&q=85&s=68fb6e395e4ec29d8ab4330a44a577aa" alt="" width="1739" height="1245" data-path="images/docs/8f69580-image.png" />

## 1. Flexible variants configuration

Our goal is to provide as flexible variants configuration as possible:

* Experiments' in-app values allow you **to set up any appearance of your app's paywall** and even the onboarding steps before the paywall if required.
* Variant weights allow you to decide what share of your users are exposed to each variant. **You are not limited to a default evenly split strategy**.

Learn more about [variant configuration](launch-experiments#2-configure-and-test-variants).

<img src="https://mintcdn.com/qonversion/i4lAeIKmC47lf8K-/images/docs/0c064f3-image.png?fit=max&auto=format&n=i4lAeIKmC47lf8K-&q=85&s=a631a59be821fcfbc2a68dc6e14d6fa1" alt="" width="1554" height="1221" data-path="images/docs/0c064f3-image.png" />

## 2. Target the right user segments

Qonversion A/B testing offers flexible user segmentation:

* Test hypotheses for **specific countries** to find the most effective pricing strategy.
* Target either **new users** or **particular subscription owners** to determine which introductory offers and upgrades work best.
* Run A/B testing for a specific share of your active users to be able to run **several experiments simultaneously**.
* And [other options](launch-experiments#segment-your-users).

## 3. Analyze experiment results

We believe each experiment should be analyzed in two ways:

* With **a strict focus on the primary metric** you aim to improve. Avoiding data peeking and reducing correlated metrics noise are crucial to making accurate decisions during data collection.
* With convenient access to all the data including all relevant subscription metrics. So you can be 100% sure when you accept or reject the hypothesis.

<img src="https://mintcdn.com/qonversion/5c527iOH0vIMjiW3/images/docs/c8e79e9-image.png?fit=max&auto=format&n=5c527iOH0vIMjiW3&q=85&s=4e79d2f0b6debfa657eafdf6ffcd32f7" alt="" width="1481" height="867" data-path="images/docs/c8e79e9-image.png" />

Learn more about how we help [analyse experiments](analyse-experiment).

***

What’s Next

* [Launch experiments](launch-experiments)