A/B testing sounds technical — and expensive — but at its core it's just comparing two things to see which performs better. When it comes to links, you can run a perfectly valid A/B test with nothing more than two short links and a basic spreadsheet. No paid tools required.
What you're actually testing
Before creating any links, decide what variable you're testing. A/B testing only works when you change one thing at a time. Common link A/B tests include:
- Two different landing pages — same traffic source, different destination
- Two different calls to action — same page, different button or headline
- Two different offers — a discount vs a free trial, for example
- Two different channels — the same link shared on Instagram vs Twitter
- Two different times — the same post published at different hours
Pick one variable. Keep everything else the same. That's the core rule.
The method: two tracked short links
Here's how to run an A/B test using only TheLinkSpot and a spreadsheet:
Step 1 — Create two short links
Go to TheLinkSpot and create a short link for Version A — your control, the thing you're already doing. Then create a second short link for Version B — the variation you want to test. Give each a custom slug that makes it easy to tell apart, like /offer-a and /offer-b.
Step 2 — Split your audience
This is the key step. You need to show each version to roughly equal groups. How you split depends on your channel:
- Email list: Export your list, split it in half randomly, send Version A to one half and Version B to the other.
- Social media: Post Version A at a chosen time, then Version B at the same time the following day or week. Not perfect, but workable.
- Paid ads: Run two identical ads pointing to different links. Ad platforms split traffic automatically.
- Two audiences: If you have two similar Facebook groups or subreddits you post in, share each version in one.
Step 3 — Run the test long enough
The most common mistake with A/B testing is stopping too early. If you check results after 100 clicks and declare a winner, you're likely acting on noise. A useful rule of thumb: run until each version has at least 200–300 clicks, or at least one full week of data — whichever comes later.
Step 4 — Record your results
Open a simple spreadsheet with these columns:
| Version | Link | Sends | Clicks | CTR | Conversions | Conv. Rate |
|---|---|---|---|---|---|---|
| A (control) | /offer-a | 1,000 | 47 | 4.7% | 9 | 19.1% |
| B (variation) | /offer-b | 1,000 | 63 | 6.3% | 8 | 12.7% |
In the example above, Version B got more clicks (higher CTR) but Version A converted better. That tells you Version B had a more compelling hook, but Version A's landing page or offer was stronger. Which matters more depends on your goal.
Step 5 — Decide on a winner
A winner isn't always the one with more clicks. If your goal is sign-ups, the version that produced more sign-ups wins — even if it had fewer clicks. Define your success metric before the test starts, not after.
Common A/B test ideas you can run today
| What to test | Version A | Version B | What the data tells you |
|---|---|---|---|
| Landing page headline | "Get 20% off today" | "Save on your first order" | Which framing resonates more |
| Email subject line | Direct benefit | Curiosity-driven | What motivates your list to open |
| Social post format | Link with no image | Link with image | Visual vs text engagement on your platform |
| Offer type | 10% discount | Free shipping | What your audience values more |
| CTA wording | "Buy now" | "See the deal" | Hard sell vs soft prompt performance |
How to read your click data
Every short link on TheLinkSpot has a stats page you can access at any time. Check both links at the end of your test period and record the numbers. For a cleaner comparison, always look at click rate (clicks divided by the number of people who saw the link), not raw click count. Raw numbers are misleading if one version was sent to a larger group.
Limitations to be aware of
This no-tools method has real value, but it also has limits. Without advanced software you can't automatically randomise who sees each version, track conversions beyond the click, or calculate statistical significance automatically. For serious testing at scale, dedicated tools add value. But for most small businesses, freelancers, and content creators, two tracked short links and a spreadsheet is more than enough to start making better decisions.
What to do with your results
Once you have a winner, implement it as your default. Then design the next test based on what you learned. If Version B won because of the headline, your next test might compare two different image styles on the same page. This iterative loop is how marketing gets genuinely better over time — not through guesswork but through evidence.
You can also look at your click data more deeply to spot patterns across multiple campaigns and get a fuller picture of what's driving performance.
Frequently asked questions
Do I need a large audience to A/B test?
Smaller audiences need longer test periods to get reliable data. With a list of 200 people, you'd need to run several test cycles to see meaningful patterns. With 2,000+, a single well-run test gives you actionable results. If your audience is small, focus on learning directionally rather than expecting statistical certainty.
Can I A/B test on social media without paid promotion?
Yes, though organic social testing is noisier. Algorithm timing, day-of-week effects, and random virality all affect results. Post Version A and Version B at the same time of day on different days, and run for at least a week each. Treat the results as directional, not definitive.
What if both versions perform about the same?
A flat result is still a result. It tells you that the variable you tested probably isn't the key driver of performance. Move on to testing a more impactful variable — usually the offer itself or the audience targeting.
Start testing today
The biggest barrier to A/B testing isn't tools or budget — it's starting. Pick one thing to test this week, create two short links on TheLinkSpot, split your audience, and check back in a week. That single test will give you more useful marketing insight than months of guessing.