Intermediate 12 min· 5 steps

Running your first A/B test

Pick a link, create a variant, split traffic, and let the data decide which title wins. With statistical-significance basics and the mistakes everyone makes the first time.

1Choose the right link to testStep 1 2Create the experimentStep 2 3Let the test run (without peeking obsessively)Step 3 4Read the results — and ignore raw clicksStep 4 5After the test: turning learnings into a patternStep 5

Before you begin

Pro plan or above
An existing link with some traffic — A/B testing on a link that gets 5 clicks a week is statistically meaningless
At least 50 clicks/week on the link you're testing (more is much better)
Patience — A/B tests pay off over weeks, not hours. Don't start a test on a Monday and check it at lunch.

Step 1 of 5

· 3 min

Choose the right link to test

A/B testing works best on links that already receive meaningful traffic. The most common rookie mistake is running a test on a link with 10 clicks a week and stopping after 4 days — you'll see noise and call it a result.

Statistical reliability requires at least 100 clicks per variant. With a 50/50 traffic split, that means 200 total clicks before you can even start interpreting numbers. If your link gets 50 clicks a week, that's about 4 weeks per test — and that's if you wait properly.

How to find your testing candidate

Go to your link manager. Sort by weekly clicks. The top 1–3 links are your candidates. Pick whichever one has the most strategic upside — usually your hero offer, your top product, or your main CTA.

What to test, in order of impact

Link title — the most impactful, easiest to iterate on. Change a "Watch my latest video" to "How I record vocals at 3am" and you might double CTR overnight.
Cover image / thumbnail — second-biggest lever. Tested less often because creators worry about brand consistency.
Description text (subtitle below the title) — meaningful but smaller swings.
Link order on the page — affects every link, not just one. Run carefully and only on big audiences.
Button colour or shape — almost always within noise for normal-sized audiences. Don't bother unless you have 10,000+ weekly visitors.

Avoid testing during seasonal traffic spikes (Black Friday, holidays, your album launch week) — the abnormal traffic mix biases the results. Test on regular weeks for clean signal.

Test ONE thing at a time. If you change both the title AND the destination URL, you've turned the test into a guessing game and any result is uninterpretable.

Next: Create the experiment

Step 2 of 5

· 4 min

Create the experiment

Click the three-dot menu on your chosen link and select "Create A/B test". The current link becomes Variant A automatically — that's your control. You'll then enter the alternative version (Variant B), and that's your hypothesis.

1Enter the Variant B title — the alternative you want to test
2Set the traffic split (default 50/50, recommended)
3Optionally add a hypothesis note — a sentence describing why you think B might win
4Click "Start test"

Writing a strong Variant B

If your two variants are too similar, the test will produce a tie no matter how long you wait. Make Variant B meaningfully different from A. Aim for 'these are clearly different framings' rather than 'one has a comma the other doesn't'.

Examples of meaningful differences:

A: 'Watch my latest YouTube video' B: 'Behind the scenes of my album recording' (descriptive vs curiosity-driven)
A: 'Get my course' B: 'Join 800+ photographers learning Lightroom' (generic vs social proof)
A: 'Free preset pack' B: 'The presets I use on every paid shoot' (offer vs personal)
A: 'Buy now' B: 'Drop closes Friday' (cta vs urgency)

Each visitor is consistently assigned to the same variant for the duration of the test using a cookie. They never see different titles on different visits — that would muddy your data and confuse the visitor.

text

Variant A: "Watch my latest YouTube video"
Variant B: "Behind the scenes of my album recording 🎵"

Hypothesis: Specific, curiosity-driven titles outperform generic
            ones for our music audience.

Always write down your hypothesis. Future-you will thank present-you when you're trying to remember why you started a test 14 days ago.

Next: Let the test run (without peeking obsessively)

Step 3 of 5

· 7–14 days

Let the test run (without peeking obsessively)

Once started, the test runs automatically. Your job now is to wait. Statistical significance requires a minimum sample size — and waiting through a full week captures normal day-of-week variation.

What "enough data" actually means

Minimum recommended runtime: 7 days. Always. Even if you're 95% confident on day three.
Minimum sample: 100 clicks per variant before reading results — 200 if you can manage it.
Confidence target: 95% or higher before declaring a winner. Below 90% is essentially a coin flip.
If neither variant reaches significance after 30 days, declare the test inconclusive — the variants perform equally for your audience.

Stopping a test early is the most common A/B testing mistake. Early results are almost always misleading — a variant that looks winning on day 2 often loses by day 10. The phenomenon has a name: regression to the mean.

Why a full week matters

Visitor behaviour varies systematically by day of the week. Saturday-afternoon traffic is qualitatively different from Wednesday-lunch traffic. A test that runs Sunday-Wednesday over-weights weekend behaviour and under-weights weekday. One full week (Monday-to-Monday or any 7-day stretch) gives you a representative sample.

Set a calendar reminder for 7 days from now to check results. The most common outcome of starting an A/B test is forgetting to ever check it. Don't be that creator.

Next: Read the results — and ignore raw clicks

Step 4 of 5

· 4 min

Read the results — and ignore raw clicks

Go to Analytics → A/B Tests to view your running and completed experiments. Each test card shows clicks, impressions, click-through rate, uplift, and confidence.

Understanding the metrics

Impressions — how many visitors saw each variant. With 50/50 split, these should be roughly equal.
Clicks — total clicks per variant. Useful as a sanity check; not the metric you optimise.
CTR — click-through rate (clicks ÷ impressions). THIS is the metric you care about.
Uplift — percentage improvement of B over A in CTR terms. +20% means B converts 20% better than A.
Confidence — statistical certainty of the result. Aim for 95%+ before declaring a winner. Below 90% is too noisy to trust.

Worked example

Variant A got 150 clicks from 400 visitors (37.5% CTR). Variant B got 120 clicks from 350 visitors (34.3% CTR). Variant A wins — even though B had fewer raw clicks. The traffic split was slightly uneven, and per-visitor performance is what matters.

If Variant B reaches 95% confidence with a positive uplift, click 'Declare winner'. LinkStacked replaces the original link with the winning version and archives the test data for future reference.

If neither variant wins after 30 days with adequate sample, the test is inconclusive. That's a result, not a failure — it tells you those two variables perform equally for your audience. Try a more dramatic change next time. Subtle wording shifts are hard for stats to detect.

Next: After the test: turning learnings into a pattern

Step 5 of 5

· 3 min

After the test: turning learnings into a pattern

The single test that ran in isolation isn't the real value of A/B testing. The real value is the pattern that emerges over months of testing — a body of evidence about what works for your specific audience.

Document everything

After every completed test, write down: what you tested, what won, by how much, and your one-line interpretation of why. After 5–10 tests, patterns emerge. Maybe your audience always responds to specific over generic. Maybe urgency consistently wins. Maybe social proof barely matters. You'll never see this pattern from a single test, but you can't miss it after a quarter of testing.

Pick the next test

After declaring a winner, immediately queue the next test. The hardest part of an A/B testing programme is keeping it going — most creators run their first test, get a result, and never run another. Calendar your next test before closing the dashboard.

Run continuous testing on your top 3 highest-traffic links. While one is mid-test, plan the next variant for the same link. After 6 months, your link page will have been A/B tuned through dozens of variants — and your CTR will look unrecognisable compared to today.

Compounding matters: a 5% lift every two weeks is roughly 70% improvement over six months. Most creators will dismiss small wins as 'not worth the effort' and miss the multiplier.

Guide complete!

You've finished all 5 steps of "Running your first A/B test". Ready for what's next?