A/B Testing

A/B testing (also called split testing) is an experimental method that pits two variants of the same element against each other, the current version (control, A) and a modified version (variant, B), splitting traffic randomly between them to measure which one performs better on a target metric (clicks, conversions, opens). Instead of debating which email subject line or which button color works best, you decide with real data from your own audience.

It applies to almost any digital touchpoint: email subject lines, calls to action, landing page headlines, segments of an automation flow or ad creatives. It is one of the core practices of marketing optimization and of the campaign flows that Revio automates, because it turns every send into an opportunity to learn what resonates best with your market.

The key concept: an A/B test is only valid if the two audiences are statistically comparable and if you change only one variable at a time. If you change three things at once, you may win the test, but you won't know what actually worked, and that learning can't be repeated.

How it works, step by step

A well-run A/B test follows an orderly sequence. Skipping a step is usually the reason a result doesn't hold up later.

1. State a concrete hypothesis: not "let's try something different," but "if I add the customer's name to the subject line, the open rate goes up." A hypothesis gives you a clear variable and an expected outcome.
2. Define ONE primary metric: choose in advance which number decides the winner (for example, conversion rate, not "several things"). Looking at ten metrics at once leads you to find false differences by pure chance.
3. Change only one variable: if you are testing the subject line, everything else (sender, send time, content, segment) stays the same. That way the result can be attributed to that change.
4. Split the audience at random: the split between A and B must be random and evenly sized, so the two samples are comparable.
5. Calculate the sample size before you start: define how many contacts per variant you need to detect a real difference. With small audiences, almost no result will be reliable.
6. Run the test long enough: let it run for at least one full behavior cycle (in Argentine B2B, typically a full business week) so you don't bias the result by day or time.
7. Evaluate statistical significance: only when the result clears the confidence threshold (usually 95%, p < 0.05) can you declare a winner. If it doesn't, the test was inconclusive, which is also a valid learning.

Why it matters in B2B marketing

In a market like Argentina's, where lists tend to be smaller than those of a mass-market e-commerce business, every copy decision carries weight. A/B testing replaces the opinion of whoever shouts loudest in the meeting with evidence, and it builds cumulative learning: each test leaves a reusable conclusion about what resonates with your ICP. That is the difference between "we think it works" and "we know it works and why."

A concrete example

A B2B software company in Buenos Aires is torn between two subject lines for its monthly newsletter to 4,000 contacts. The hypothesis: a subject line with a direct question generates more opens than a descriptive one. It splits the list 50/50 at random, keeps a single variable (the subject line), runs the test for a full week and only then compares the open rate. If the difference clears 95% confidence, it adopts the winner for all future sends and records the learning. If not, it changes nothing and tests another hypothesis.

Common mistakes that invalidate a test

• Stopping the test too early: seeing B ahead on the first day and declaring it the winner. The first hours are dominated by noise; the lead almost always shrinks as more data comes in.
• Ignoring statistical significance: taking a "3-point" difference at face value without checking whether it is statistically real. With small samples, that gap can be pure chance.
• Changing more than one variable: testing the subject line and the send time at once. If B wins, you don't know which of the two changes did it, and you can't replicate it.
• Non-comparable audiences: sending A to active customers and B to inactive ones. Then you are measuring the segment, not the variant.
• Looking at many metrics and picking the one that came out well: if you check enough numbers, one of them will show a difference by coincidence. The primary metric is chosen before, not after.

A/B testing vs. multivariate testing

When more than one element is in play, the question arises of whether a classic A/B test or a multivariate test (MVT) is the better fit. The difference is practical:

For most B2B companies in LATAM, with limited volumes, simple A/B testing is the right tool: faster to read, less traffic needed and clearer learnings.