In marketing, UI/UX design, and product development, ads, creatives, and landing pages do matter. But business decisions can’t rely on “looks good” alone — you need hard numbers: conversion rate, lead volume, revenue, and other measurable outcomes. It’s only by relying on numbers that you can accurately evaluate a business's success and determine whether it’s ready to grow and scale. This is exactly where A/B testing becomes useful. It’s a practical way to check ideas with real users and then choose the next step based on evidence — not “intuition.” More about what A/B testing is, how it works in practice, features of split testing, why A/B testing of the website is worth doing, and how to analyze results — in this article by Tech4you.io.
What is A/B testing?
A/B testing (often called split testing) is a method used in marketing and product work to validate hypotheses by comparing two variants — typically version A and version B — of a specific element (a headline, button, price, layout, etc.) You measure how each variant affects a chosen metric, such as conversion rate, registrations, or CTR, and then determine which option performs better in a cause-and-effect way.
In simple terms, A/B testing is an experiment where you divide your audience into separate groups. Each group is shown a different version of the same experience — for example, a different headline or CTA — and you compare how those groups behave.
When you’re conducting A/B testing properly, you can see how users react to each variant and which one drives stronger outcomes. Analyzing this data helps you build a more confident marketing strategy, launch changes, and make business decisions based on numbers — not just guesses or “what looks nicer.”
Why use A/B testing?
In most cases, split testing is used to improve one key metric while keeping risk under control.
For example, you have a website that already receives traffic and generates leads. You decide to increase the landing page CR and come up with a change: show a banner after 30 seconds on the homepage.
It may sound like a good idea — but how do you know whether it will help, do nothing, or even hurt performance? The most reliable way to confirm or disprove the idea is A/B testing of the website.
A/B test are also used to:
- Improve key metrics consistently: CR, revenue, CTR, average order value, etc.
- Separate the real impact from randomness — because digital performance naturally fluctuates, and testing provides a controlled comparison.
- Don’t waste time and bidget on weak ideas: things that look “nice” but don’t work.
- Understand your audience better and refine the user experience based on behavior.
- Build more effective strategies over time.
What gets tested most often: websites, page layouts, creatives, feedback forms, buttons, CTA, discounts, widgets, subscription offers, email campaigns, and other touchpoints across the funnel.
In that sense, A/B testing is like a “compass”: it helps you choose changes that are worth scaling — and avoid changes that don’t pay off.
How is A/B testing conducted?
A/B testing of the website — as well as experiments for ads, headlines, emails, and other assets — typically follows the same pattern: introduce one clear change, split users randomly, measure the impact, then interpret the results.
These features of split testing make it possible to compare variants fairly under the same conditions.
Step by step:
Step 1. Define the goal and the hypothesis
At the first stage, you make an assumption: if you replace element A with element B, metric X should change.
For example, if you make the first-screen CTA more specific (changing “Sign up” to “Sign up for a consultation”), the lead conversion rate may increase.
The features of split testing are designed to keep cause and effect clear, so it’s best to test one meaningful change per run.
Step 2. Define the metric
Choose the main (primary) metric you’ll use to judge the test’s “success.” This can be:
- Lead conversion rate
- Purchase conversion rate
- Revenue per user
- CTR on the key CTA
- LTV (customer lifetime value)
- Engagement, etc.
Step 3. Build the variants you’ll compare
For example, you’re testing a creative. So, option A — the control group: keep the button green. Option B — the test group: make the button red.
Step 4. Conducting A/B testing
At this stage, it’s important to split traffic randomly and evenly: 50% of users go to group A and 50% to group B.
It’s commonly considered optimal for an A/B test to run for 10–14 days.
Trusted services for A/B testing of the website: Google Analytics, Google Optimize, VWO, Optimizely, Changeagain, Convert.
Step 5. Review results and decide what to do next
Once the test ends, compare outcomes: which version performed better on the target metric. Did you achieve the goal? Was the hypothesis supported by the data?
Now let’s break down how to analyze the results.
Analysis of split testing results
Split-test review typically focuses on three areas.
- Data cleanliness
First, confirm the experiment was set up and tracked correctly: random and even traffic allocation; similarity of groups by GEO, devices, and traffic sources; absence of bots and internal traffic; and stable tracking for both versions throughout the entire test period.
- A real performance difference between the two versions — not randomness
Carefully compare the target metric in variant A vs variant B: in raw numbers, in percentages, and over the exact test period.
One of the key features of split-testing is that results can sometimes be “random”. Check whether seasonality, holidays, or other external factors could have influenced outcomes; whether there were enough users to conclude; and whether the result is statistically significant.
If there is a difference, ask yourself: does this uplift actually matter for the business? Will it pay back the development effort and the risk?
- A check for “hidden harm”
Always verify whether the version that looks “more effective” on the main metric causes losses in secondary and guardrail metrics.
For example, an A/B test may show that variant B increased lead conversion rate, but at the same time lead quality dropped significantly, page load speed decreased, and the bounce rate increased. That’s “hidden harm.”
Important: make rollout decisions for a broader audience only after analyzing these three points.
What is multivariate testing?
Multivariate Testing (MVT) is a more complex method where you test several page elements at the same time, as well as their combinations.
This approach helps you understand:
- Which element has the greatest impact on the metric
- Which combination of elements produces the best result
- Whether there are interactions between elements (some work better together)
For example, you want to test:
- Two headlines: A1 and A2
- Two buttons: B1 and B2
- Two images: C1 and C2
In that case, you test 8 (2×2×2) combinations (A1B1C1, A1B1C2, …, A2B2C2). Traffic is distributed across all 8 variants, and you compare them by the metric.
MVT requires more traffic, but it can help you find an optimal combination faster, detect interactions between elements, and identify the single element with the strongest influence on the metric.
It’s better not to run multivariate testing if you have little traffic, few conversions, or you need to quickly validate just one hypothesis. In those cases, conducting A/B testing is the better choice.
Statistically significant sample size in A/B testing
A statistically significant sample size in A/B testing is the minimum number of users (or conversions/leads) in each variant that you need to distinguish a real effect from randomness.
If the sample is small, the difference between variants A and B can be “accidentally the same” or “accidentally different”. If the sample is large enough, you can say: “The probability that this result is random is low”, meaning the result is statistically significant.
For example, for the first 100 users conversion rate might be 12%, but with the next hundreds, it could drop to 4% and stay there.
Tip: you can calculate the required number of users for conducting A/B testing with a dedicated calculator — for example, ABTestGuide.
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