Shopify A/B Testing that grows revenue per visitor
For AU DTC brands with enough traffic to test. We design, build and analyse experiments on your Shopify store, so decisions are backed by data, not opinions.
Guesswork is expensive
Redesigns and hunches ship changes nobody has validated. A/B testing tells you what actually moves revenue.
Opinions win arguments, not revenue
The loudest voice decides what changes. Testing settles it with real customer behaviour.
Redesigns bet the whole store
Big-bang rebuilds risk tanking conversion overnight. Controlled experiments de-risk every change.
Wins never get isolated
Ship five things at once and you never learn which one worked, or which one hurt.
Tests get called too early
Ending on day three because it looks good is how false winners ship. Significance takes discipline.
Traffic goes to waste
Every untested visitor is a data point you paid for and never used to improve.
Speed suffers
Most testing tools flicker and slow the page. Done wrong, the test itself hurts conversion.
What we run, what stays with you
Clear lines so nothing falls through the gaps.
| We handle | Your side | Out of scope | |
|---|---|---|---|
| Hypotheses | Research, score and prioritise the backlog | Share context, goals and past learnings | Brand strategy overhaul |
| Build | Variant build, QA and flicker control | Theme and app access | New feature development unrelated to the test |
| Measurement | Tracking, significance and analysis | Confirm the primary business metric | Ongoing BI dashboards |
| Rollout | Ship winners cleanly to live | Sign off on the winning variant | Paid media management |
Is A/B testing right for you?
A good fit if you
- Get enough traffic and orders to reach significance in weeks, not months
- Have a backlog of ideas and disagreements you want settled with data
- Run on Shopify or Shopify Plus and want testing done properly
- Care about revenue per visitor, not just click-through vanity metrics
- Want a repeatable experiment programme, not one-off tweaks
Not a fit if you
- Have very low traffic, where tests would run for months without a clear result
- Need a full redesign or rebuild first, before testing makes sense
- Want guaranteed wins, no honest programme can promise every test succeeds
- Are chasing a single quick fix rather than a testing habit
What A/B testing with Nexly includes
Hypothesis to result, run end to end so you can trust the call.
What's included
- Hypothesis backlog scored by impact, confidence and effort
- Test design with clear primary metric and guardrails
- Build and QA of each variant, mobile and desktop
- Significance monitoring and an honest read of the result
- Documented winner, loser or inconclusive with next step
Technical deliverables
- Flicker-free variants that hold your Lighthouse score
- Server-side or theme-level implementation where it counts
- Event and revenue tracking wired to your analytics
- QA across breakpoints, browsers and key devices
- Clean rollout of winners into the live theme
Tools & integrations
- Testing platform selection to suit your traffic and budget
- GA4 and Shopify analytics for revenue-level reporting
- Consent-aware tracking that respects AU privacy expectations
- Segment or CDP hookups where you already run them
How a testing programme runs
A repeatable loop, not a one-off. Typical first cycle runs 4–6 weeks.
Discovery
We review analytics, session data and your goals to find where testing pays off.
Hypothesis backlog
Ideas scored by impact, confidence and effort, so we test what matters first.
Test design
One primary metric, guardrails and a sample-size plan set before anything ships.
Build & QA
Variants built flicker-free and checked across devices, browsers and breakpoints.
Run & monitor
The test runs to plan. No peeking, no early calls, guardrails watched throughout.
Analyse & decide
An honest read: winner, loser or inconclusive, with the reasoning documented.
Roll out & repeat
Winners ship to live, learnings feed the backlog, and the next test begins.
How we keep results trustworthy
Statistical rigour, not wishful thinking.
- Sample size and duration agreed before launch
- 95% confidence threshold before any winner is called
- Guardrail metrics watched so a win in one place is not a loss elsewhere
- Full test cycles to avoid weekday and weekend skew
- Flicker control so the test never slows the page
- Every result documented, including the ones that fail
Investment
Typical AUD bands, scoped after a discovery call. Pricing depends on traffic, test complexity and cadence.
Single experiment
One hypothesis designed, built, run and analysed end to end.
Testing sprint
A batch of prioritised tests over 6–8 weeks with reporting.
Ongoing programme
Continuous backlog, build and analysis as a retained loop.
A/B testing FAQs
Related services
Stop guessing. Start testing.
Book a call and we will review your traffic, find your highest-value hypotheses, and map a testing programme that grows revenue per visitor.
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