A/B testing

Test, learn, and keep what wins.

Opinion-based marketing decisions are expensive. Yavun runs structured A/B tests across email, WhatsApp, SMS and social - with proper statistical controls - so every change you make to a campaign is backed by evidence, not instinct.

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A/B test · subject line

Variant A (control)

“Your exclusive offer is inside”

42.1% open rate · 2,400 sends

Variant B (winner)

“Sarah, this one's for your bakery”

51.4% open rate · 2,400 sends

95% confidence · +9.3 pt lift · winner rolling out

95% confidence

Before rollout

12% avg lift

Per test (sample)

Illustration · sample data

Most campaign 'improvements' are guesses with better presentation.

A new subject line, a different call to action, a changed send time - each feels like an improvement when you write it. Without a controlled test, you will never know if the change made a difference or if results shifted for unrelated reasons.

12%
average lift per test
typical open rate improvement from winning subject line variants
2 weeks
to statistical significance
average time for email tests to reach 95% confidence
4 variables
testable per campaign
subject line, send time, audience segment, and offer
3x
more tests run
Yavun automates test setup vs. manual split testing in most email tools

Sample data · illustrative

Managed service

We design and run your testing program

Our team identifies the highest-value test opportunities in your campaigns, designs the variants, runs the tests with proper statistical controls, and implements the winners - so you get the benefit of a structured testing program without managing it yourself.

Test roadmap built from your campaign history
Variant design for subject lines, CTAs, offers and send times
Statistical significance monitoring with clear pass/fail criteria
Winner implementation and result documentation

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Self-serve platform

Run A/B tests in Yavun

Yavun's built-in A/B testing tools let you split any campaign across two or more variants, set your significance threshold, and have the winning variant automatically rolled out once the test concludes.

Split testing on any campaign across all four channels
Configurable test size and significance threshold
Automatic winner detection and rollout
Test history and results stored per campaign

Explore the platform

1Test design

Tests that answer the question you actually need to ask.

A badly designed test produces a result you cannot act on. Yavun's test design process starts with the specific question you need to answer - does a benefit-led subject line outperform a curiosity-led one for this audience? - and works backward to the variant design, sample size and success metric.

Hypothesis-first design: question before variant
Sample size calculation to reach statistical significance
Single-variable tests: one change per test for clean results
Control group held back to establish baseline
Success metric defined before the test runs

See campaign reporting

A/B test · subject line

Variant A (control)

“Your exclusive offer is inside”

42.1% open rate · 2,400 sends

Variant B (winner)

“Sarah, this one's for your bakery”

51.4% open rate · 2,400 sends

95% confidence · +9.3 pt lift · winner rolling out

Illustration · sample data

2Multi-channel testing

What works in email may not work in WhatsApp.

Channel audiences behave differently. An offer that converts well in an email newsletter may fall flat in a WhatsApp broadcast - because the context, the relationship and the expected content are different. Yavun runs channel-specific tests that treat each channel as a distinct communication environment.

Email: subject lines, preview text, CTAs and layout variations
WhatsApp: message length, tone and offer framing
SMS: copy length and call-to-action wording
Social: headline, image and targeting angle variations
Cross-channel: test whether email or WhatsApp converts better for the same offer

See email marketing

Test roadmap

3 active

Email - Subject line
Winner found
+9.3%
WhatsApp - Message length
Running
48h left
Email - Send time
Planned
Next week

Sample data - illustrative only

Illustration · sample data

3Test-and-learn system

Build a learning library that compounds over time.

A single A/B test answers one question. A structured testing program builds institutional knowledge about what works for your specific audience - subject line patterns, offer structures, send time windows and audience segments that consistently outperform. Yavun documents every test result so each one builds on the last.

Test results logged with hypothesis, variant and outcome
Winning patterns identified across multiple tests
Test roadmap updated based on what questions remain open
Onboarding playbook built from your own test history
Quarterly learning review with strategic implications

See marketing analytics

Test result library

Subject: benefit vs. curiosity
Benefit-led+9.3%97% confidence
Send time: Tue 10am vs. Thu 9am
Thu 9am+6.1%95% confidence
CTA: button text variation
"Get my offer"+4.8%96% confidence

Sample data - illustrative only

Illustration · sample data

A testing program that actually changes your campaigns.

Design, run, analyze, and build on results - across all four channels.

Test design

Hypothesis builderStart with the question; work backward to the variant.
Sample size calculatorRun tests large enough to reach statistical significance.
Single-variable testingOne change per test for results you can act on.
Control groupBaseline group held back in every test for clean comparison.
Multi-variable testingAdvanced factorial tests for higher-volume campaigns.

Channel testing

Email subject linesTest length, tone, benefit vs. curiosity, and personalization.
Email CTAsButton text, placement and urgency framing tested systematically.
WhatsApp message variantsTone, length and offer framing in WhatsApp contexts.
SMS copy testsShort copy variations that test different calls to action.
Send time testsCompare morning vs. afternoon, weekday vs. weekend sends.
Audience segment testsDoes the same message land differently with different segments?

Analysis

Statistical significanceResults held until they meet your confidence threshold.
Winner detectionAutomatic rollout to the remaining audience once a winner emerges.
Lift measurementPercentage improvement from the winning variant over control.
Confidence intervalsRange of likely true effects shown alongside point estimates.

Learning system

Test result libraryEvery test logged with hypothesis, variants, results and actions.
Winning patternsSubject line and offer patterns that consistently outperform.
Test roadmapPrioritized list of tests to run based on open questions.
Quarterly reviewStrategic implications from a quarter of test results in one session.

First test live within one week.

1
Campaign audit
We review your recent campaigns to identify the variables with the most improvement potential.
2
Test roadmap
A prioritized test plan is built: which variable to test first, what the hypothesis is, and what a meaningful result looks like.
3
Variant build
Test variants are written, reviewed and loaded into Yavun alongside the control group.
4
Run and monitor
Test runs until statistical significance is reached; results are monitored daily.
5
Implement winner
The winning variant is rolled out to the remaining audience; results are documented for the learning library.

Pairs well with:Campaign Reporting Marketing Analytics Email Marketing Conversion Optimization AI Marketing Automation

Questions, answered.

Still unsure? Talk to our team.

How large does my audience need to be for A/B testing?

It depends on the expected effect size and your significance threshold. For email subject line tests, most campaigns need at least 1,000 recipients per variant to detect a meaningful difference at 95% confidence. Smaller audiences can still benefit from testing, but the results take longer to reach significance and should be interpreted with caution.

How long does a typical test take?

Email and SMS tests usually reach statistical significance within 7-14 days if the audience is large enough. WhatsApp tests often conclude faster because of higher open rates. Social tests vary widely depending on campaign budget and audience size.

Can I test more than one thing at a time?

You can run multiple tests simultaneously as long as they are on different campaigns or different audience segments. Running two tests on the same audience and campaign at the same time makes it impossible to isolate which change caused which result.

What is statistical significance and why does it matter?

Statistical significance tells you how confident you can be that the difference between two variants is real and not a result of random variation. A 95% significance threshold means there is a 1-in-20 chance the result is a false positive. Without it, you risk implementing changes that had no real effect.

What variables can be tested?

Subject lines, preview text, CTAs, offer amounts, message length, send times, audience segments, and landing page headlines. For social, you can also test image, headline and targeting parameters. We prioritize tests based on which variables are most likely to move the metric that matters most to your business.

What happens after a test concludes?

The winning variant is rolled out to the remaining audience, the result is logged in the test library, and the test roadmap is updated to reflect what the result tells us about the next question to test. Over time, the test library becomes a reference for what works with your specific audience.

Ready to replace campaign guesswork with evidence?

Book a free call and we will review your last three campaigns, identify the variables most worth testing, and build a test roadmap that gives your team a clear path to better performance.

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