What is A/B testing?

A/B testing is a method of comparing two versions of a web page, email newsletter, or other marketing element to determine which one performs better. Imagine you have two versions of your main button: red and blue. How do you know which one brings in more clicks? Simply show one half of your users the red one and the other half the blue one, and then compare the results!

This method helps you make data-driven decisions instead of guessing. Marketers, UX designers and website owners use A/B testing to improve user experience, increase conversions and make businesses more profitable.

By the way, the first A/B tests appeared at the beginning of the 20th century in the field of medicine! Back then, researchers compared the effectiveness of different medicines by dividing patients into two groups and giving them different drugs. Now this principle works in the digital sphere as well.

Examples of tests for websites

A/B testing can be done on almost anything. Here are a few examples:

• Headings and texts - different wording can affect user engagement. For example, BuzzFeed is known for testing up to 25 headline variations for a single article!
• Button colour and text - Even a single phrase or nuance can increase conversions.
• Location of elements - testing page structure helps to improve navigation.
• Application forms - reducing form fields can increase the number of requests. For example, Expedia removed one field from a booking form and made $12 million in additional revenue!
• Images and videos - different media files can influence user behaviour in different ways.
• Prices and discounts - experimenting with different discount levels helps to find the optimal sales strategy.
• Content personalisation - testing different product recommendations can increase the average cheque.
• Length of pages - For example, long landing pages may work better for some products and short ones for others.
• Calls to action - changing the wording of the CTA ("Buy now" vs. "Get a discount") can affect conversion rates.

What data should I report?

In order for testing to be accurate, it is important to collect and analyse data correctly. Pay attention to:

• Conversions - how many users performed the targeted action.
• Time on site - whether visitors are staying longer.
• Clicks and scrolls - which elements attract more attention.
• Refusals - whether the proportion of people leaving the site too quickly has increased.
• Cross-platform - how different the results are for mobile and desktop users.

Errors in conducting tests(h4)

Even in such an accurate method, there are pitfalls. Here are the main pitfalls:

• Insufficient traffic - If the sample is small, the results may not be representative.
• Simultaneous testing of several elements - by changing several parameters at once, it is difficult to understand what exactly influenced the result.
• Testing too short - if the test is a couple of days old, the data could be random.
• Ignoring seasonality - results may depend on external factors (holidays, promotions).
• Failure to account for mobile users - Often tests are conducted for desktop only, forgetting about mobile visitors.
• Neglect of statistical significance - if the differences between the options are small, testing may not yield useful conclusions.

Interesting cases of A/B testing(h5)

• Google - the company once tested 41 shades of blue for links in search results to determine which option brought more clicks. This test generated an additional $200 million in revenue for Google.
• Amazon - testing different versions of the "Buy" button helped to increase sales through optimal colour and text.
• Netflix - the company experimented with preview images of shows to see which covers attracted more viewers.
• Airbnb - testing of accommodation photos has shown that professional shots significantly increase bookings.
• Facebook - platform tested the placement of the Like button, which helped improve user interaction with posts.
• Dropbox - Changing the registration form from a long list of fields to a minimalistic version helped increase conversions on 10%.

Unexpected example: A/B testing in politics (subheading)

Businesses aren't the only ones using A/B testing. During Barack Obama's presidential campaign, his team tested different versions of donation appeals. Bottom line: The version with less formal text and a friendly message increased the number of donations by 60%.

And there was also a case when one politician tested two versions of a slogan for an election campaign, and the simpler version won by a huge margin. This proves that even one word can change the course of history!

A/B testing is a powerful tool for increasing conversions, improving UX and increasing profits. The main thing is to use it correctly and draw conclusions based on real data!

So if you're not already testing buttons, headers and forms, now's the time to start - who knows, it might save you millions like Expedia!