A/B Testing: What It Is and How to Do It Step by Step

Times have changed (and a lot). The digital environment is increasingly fast and unpredictable, and the old long-term formulas with stable results no longer work. Only companies willing to take risks and learn from mistakes will be agile and flexible enough to survive in the future.

That mindset of taking risks and testing does not always mean putting your entire digital business on the line. It is not all or nothing. Sometimes it is small bets, small tests where you can get your business to improve results step by step. And the best way to do it is with A/B tests.

In this article, we will explain what an A/B test is, how to do it step by step, and why it is the most effective method for optimizing your results without risking your business or completely changing your website.

What Is an A/B Test and What It Is NOT

Talking about an A/B test is not talking about any test where two different alternatives are offered. To truly understand it, you need to know that it is more than that. It is a methodology based on data and statistics.

An A/B test is a type of test carried out in a controlled, equal, and objective environment where two or more alternatives compete against each other, varying exclusively in a single variable with the aim of validating a specific hypothesis. It is a test that compares the different variants under equal conditions, obtaining statistically extrapolable results.

In other words: it is an experiment where part of a website's traffic sees variant A, while another proportional part sees variant B. There is only one variation between them and, if the data is statistically projectable, the winner is the one that generates an uplift in key metrics.

What an A/B Test Is NOT

To fully understand it, sometimes it is more helpful to understand what it is not. Because not every test can be considered an A/B test:

• A test with multiple variables -- There are many ideas and many hypotheses to validate, but you need to know how and when to do it. When several changes are made to different variables at the same time, it is not a valid test. There will be no way to know which change generated the impact.
• A test launched in any environment and conditions -- For it to be reliable, it needs certain characteristics. All of them serve the same goal: a clean test. The traffic sample, temporal conditions, and functionality must be completely equal.
• A test ended before it is significant -- It is not enough for the test to be active for a couple of hours and in that time the hypothesis is confirmed or denied. These tests reach a certain degree of significance as time passes and traffic increases. The tools used to run them provide the reliability data.

When Does an A/B Test Make Sense for Your Digital Business?

It is not enough to know what it is and how to do it. It is also important to know when and why. An A/B test is a useful methodology for certain business objectives, but not all:

• Improving specific metrics at friction points -- These tests allow you to apply changes and improvements at your website's friction points to get better results on certain metrics and know which element achieved those results.
• Validating specific versions of texts, designs, or structures -- In many cases, they serve to identify elements like messages, visuals, or logic that work better.
• Testing new versions before launching to the public -- It is also useful to employ this methodology as a filter before any change that will affect all users of your website.

How to Do an A/B Test Step by Step

The key to this process is following the methodology exactly. A methodology that has been proven to work and is divided into the following steps:

1. Define a Data-Based Hypothesis

The process of any A/B test starts with digital analytics. That is, reviewing your site or digital product data to identify where those friction areas are.

Data is the guide for any test. And it is thanks to them that a hypothesis is defined to be validated through the test. These hypotheses explore whether changing a certain variable will change any of the metrics or objectives.

2. Choose a Single Variable to Validate

In many cases, hypotheses can revolve around different variables: changing copy, an image, a button... Different options to achieve the same objective. But you must always choose one of them.

These tests are always carried out with a single variable. Only one change to measure its impact and draw firm, significant conclusions. Otherwise, if something works, it could be due to multiple factors.

3. Calculate the Required Sample Size and Traffic

When you have the variables defined, it is time to shape the test itself. It is essential to define the sample size and the percentage of traffic you want participating in it. The time it takes to draw representative conclusions depends on this.

Sometimes you cannot count on ideal traffic. Due to technical issues, business needs, or multiple tests running simultaneously... That does not mean the test is worthless; it will simply take longer.

4. Activate the Test in the Right Environment

The most important thing is to have good technical implementation of your A/B test. With it, you can collect data correctly, ensure the figures you handle are reliable, and that your conclusions are solid.

Something vitally important for success is having the right web traffic. You must always ensure that the volume of visits the test pages receive truly matches the initial design you made.

5. Wait for Statistical Significance

Finally, you must let the process work its magic. That is, activate it and wait to see how it evolves until you achieve results that are extrapolable to all your web traffic.

Sometimes tests do not reach the minimum significance to be conclusive. Whether due to traffic or other variables, these tests must be repeated in a different environment to get more solid data.

A/B Test vs Multivariate Test

Although they are often used interchangeably, an A/B test and a multivariate test (MVT) are different methodologies with different uses:

Feature	A/B test	Multivariate test
Variables that change	A single variable per test	Multiple variables simultaneously
Complexity	Low: control vs. variant	High: multiple combinations
Traffic needed	Moderate	Very high (grows exponentially with each variable)
Conclusions	Clear: you know exactly what caused the change	Complex: you identify interactions between variables
When to use it	Validating a specific hypothesis	Optimizing multiple elements at once on high-traffic sites

In practice, A/B testing is the right choice for most businesses. Multivariate testing only makes sense when you have enough traffic to feed all combinations and when you need to understand how several elements interact with each other. If in doubt, always start with A/B tests.

How Much Traffic Do You Need for an A/B Test

One of the most frequent questions is how much traffic an A/B test needs to be reliable. The answer depends on three factors: your current conversion rate, the minimum uplift you want to detect, and the level of statistical confidence you need.

As a reference, here are indicative sample sizes per variant to detect a 10% relative uplift with 95% confidence:

Current conversion rate	Visitors per variant	Estimated duration (1,000 visits/day)
1%	150,000	300 days
2%	75,000	150 days
3%	48,000	96 days
5%	28,000	56 days
10%	13,000	26 days

These numbers explain why many SMBs need to be strategic with their tests: with low traffic, each test must be well-grounded in data and aimed at changes with high potential impact. Sample size calculators like CXL's or Evan Miller's help you plan the duration before launching the test.

Real A/B Test Examples with Measurable Impact

Here are some real, measurable examples we have carried out at Boost with some of our clients. They show how, with just one A/B test and a single variant, we achieved completely compelling results:

• Visual redesign of payment methods for Catalonia Hotels & Resorts: We restructured the presentation of payment methods in the mobile checkout as selectable visual cards with recognizable icons. Result -- This single modification achieved an 11.26% increase in conversion rate on mobile devices, demonstrating the impact of transparency at the most critical point of the funnel.

• Sticky banner with free shipping and price for Goodies Nutrition: We added a fixed banner at the bottom of product pages on mobile with the message "Free next-day shipping," the discounted price, and the "Add to Cart" button. Result -- The Add to Cart rate doubled: from 7.17% to 13.67% (+91% improvement), with statistical significance at 99.91%.

• Greater visibility of elements and benefits for Vortex Labs: We made the main button more visible and clarified the benefit of the value proposition from the first moment. Result -- We achieved an additional +10.58% increase in conversion, demonstrating that when the user quickly understands what to do and has fewer doubts at the start, they progress further in the application.

Tools for A/B Testing

This type of testing is increasingly common in the digital environment. And as expected, there are many software options to carry them out. Each has different advantages and disadvantages, but regardless, the important thing is finding one that fits your needs:

• Complete and limited plans -- Each tool offers different plan options. Depending on your traffic volume and the number of tests you want to run, you should find a plan that offers good value for money.

• Direct connection with your web analytics -- As with other tools, sometimes data remains isolated and it is difficult to draw solid conclusions. For A/B tests, it is key to have a tool that truly connects with your data and lets you cross-reference it effectively.

• Ideation and forecasting -- Finally, there are more or less comprehensive tools that offer hypothesis ideation and test design services, as well as greater capacity for predicting trends and user behaviors.

At Boost, we use Mida, a comprehensive tool that allows us to run multiple A/B tests with different clients, connecting with their own analytics and offering a complete end-to-end service.

Start Your A/B Testing Strategy by Identifying Variables to Test

Now that you know what an A/B test is and how to do it step by step, we invite you to start thinking about what you want to focus on. To do this, it is essential to do a good analysis or audit of your website to know where to start.

And you are in luck. With our Scan&Boost tool you can do a complete digital audit of your website in less than 2 minutes and completely free. You just need to enter your website and we will send you the complete analysis by email.

With that, you can jump to a well-organized and executed strategy. At Boost, we can help you achieve it in a matter of days.