What is A/B Testing?

A/B Testing is an experimentation method where you compare two versions of something — A and B — to see which one performs better.

In mobile apps, this could mean testing two onboarding flows, two paywall designs, or two ad creatives. The goal is to let data, not intuition, guide decisions.

For example:

Group A sees a “Start Free Trial” button.
Group B sees “Unlock Premium”.
Whichever group converts more effectively helps you choose the better variant.

How does it work?

A/B tests work by randomly splitting users into groups:

• The control group sees version A (the current baseline)
  
  

• The variant group sees version B (the new idea)
  
  

You track a primary outcome, like click rate, conversion, retention, or revenue.

Key components of a valid A/B test:

• Random assignment
  
  

• Statistically significant sample size
  
  

• A clearly defined success metric
  
  

• Only one variable changed per test
  
  

Many growth and product teams use tools like Firebase, Split, or custom in-house platforms to run experiments safely and consistently.

Why it matters
A/B Testing is essential for data-driven decision making. It lets you:

• Improve user experience without guessing
  
  

• Optimize funnels and monetization
  
  

• Reduce the risk of rolling out harmful changes
  
  

• Build a culture of iteration and learning
  
  

Especially in performance marketing and product growth, small wins from A/B tests compound into huge gains over time.