# What is A/B Testing?

## What is an A/B Test?

It is an effective way to improve your product through data-driven decision making.

When launching a new feature or changing a UX flow, you can apply the current version versus the new version to real users over the same period and accurately measure the impact of those changes.

* You can make decisions based on real user data rather than intuition or experience.
* You can statistically validate the impact of changes on key metrics.

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**With Hackle's A/B Test, you can form hypotheses, design experiments, and improve your product based on statistically significant results.**
{% endhint %}

## Who A/B Tests Are For

A/B Tests are not only for developers.

* Planners/PMs/POs who want to validate whether new features or UI changes actually work
* Marketers who want to improve conversion rates for promotions, landing pages, CTA copy, and more
* Designers who want to determine which design provides a better user experience
* Developers who want to check the impact of technical changes (algorithms, infrastructure, etc.) on service metrics

In addition, anyone who wants to make data-driven decisions can leverage A/B Tests.

## A/B Test Use Cases

### Conversion Rate Improvement

You can compare which designs or copy deliver higher conversion rates at key conversion points such as sign-up forms, checkout pages, and landing pages.

The advantages are:

* You can choose the optimal UI/UX based on real data rather than intuition.
* You can discover opportunities where small changes can make a big difference in conversion rates.

### New Feature Validation

Before releasing a new feature or service to all users, you can validate its effectiveness in advance with a subset of users.

The advantages are:

* You can reduce release risk by verifying a feature's effectiveness before a full rollout.
* You can statistically validate whether the feature has a positive impact on key metrics (retention, revenue, etc.).

### Recommendation Algorithm Optimization

When changing algorithms for product recommendations, content recommendations, search result sorting, etc., you can compare performance against the existing algorithm.

The advantages are:

* You can accurately measure the impact of algorithm changes on real business metrics such as click-through rate and purchase conversion rate.
* You can compare multiple algorithms simultaneously and choose the optimal one.

### Pricing and Promotion Strategy

By comparing the effectiveness of various promotion conditions such as discount rates, coupon amounts, and free shipping thresholds, you can establish the optimal strategy.

The advantages are:

* You can validate with data which promotion strategies are most effective for revenue and profit.
* You can reduce unnecessary discount costs and maximize ROI.

{% hint style="success" %}
Want to try running an A/B Test yourself?

* [Explore the Hackle demo](https://dashboard.hackle.io/demo)
* [Contact us about Hackle](https://hackle.io/ko/?inquiry=true)
  {% endhint %}


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