Challenge 01
Design Decided By Seniority
Changes ship because someone influential preferred them, and nobody measures the result.
SETTLE IT WITH DATA
A/B Testing & Data-Driven UX Optimisation Improvements you can prove, then keep.
A disciplined experimentation practice: hypotheses drawn from research, tests powered correctly, results read honestly, and every outcome documented.
A test that stops the moment the numbers look good is not a test. It is an expensive way to confirm what you already hoped.
Orka Digital has optimised websites for Australian businesses since 2009, and runs every experiment to a pre-agreed sample size, reporting losing tests alongside the wins, working from St Marys in Western Sydney.
PROJECT TIMELINE
Ongoing, 2–4 week cycles
Project stages
Step by step
How the work progresses
Start
Hypothesise
Design
Run
Learn
Illustrative project timeline (ongoing, 2–4 week cycles). Your scope sets the actual schedule.
Cycle
2–4 weeks
Per test
Pace
2-4
Tests a month
Called at
95%
Confidence
Library
Kept
Every result
The challenge
Signs You Need A/B Testing & Data-Driven UX Optimisation
Three signs this is the right conversation to be having.
Challenge 02
Tests Stopped Early
Experiments are called after a few days, so the wins do not survive contact with a full cycle.
Challenge 03
No Record Of Past Tests
The same idea is tested every eighteen months because nobody documented the previous answer.
What we deliver
What A/B Testing & Data-Driven UX Optimisation Delivers
The work itself, written down: what you receive and what each part includes.
01/04
Hypothesis Backlog
Research-derived ideas, scored and sequenced so the highest-value test always runs next.
What's included
- Ideas from data, research and testing
- Each written as a clear hypothesis
- Ranked by likely impact and effort
- Shared and reviewed with your team
Why Orka Digital
Our Edge
Three commitments that shape how we deliver this work.
Tests Run To Size
Every test runs to a sample size agreed in advance, so a lucky streak is never reported as a win.
Learn moreLosses Reported Too
Losing tests appear in your report alongside the winners, because they teach as much and stop bad ideas returning.
Learn moreWinners Built Properly
Winning variants are built into your site, not left running through a testing tool.
Learn moreOur Process, Step By Step
Each stage produces something you can review, so nothing moves forward without your agreement.
Phase 01
Hypothesise
Draw candidate tests from usability findings, analytics, and customer research.
Phase 02
Design
Calculate sample size and duration, and agree the stopping rule in advance.
Phase 03
Run
Build, QA, and run the experiment to completion without peeking-driven decisions.
Phase 04
Learn
Implement winners properly, document losers, and feed both back into the backlog.
Phase 05
Implement
Winning changes are built properly into the site, and the next test begins.
What we measure
Judged on what it changes.
We agree up front how the work will be judged, then report against it.
01
Conversion Rate
The primary goal measured for each variant against the original.
Reported: per test
02
Revenue Per Visitor
Guards against wins that trade order value for volume.
Reported: per test
03
Tests Concluded
Experiments run to full sample size and recorded.
Reported: monthly
04
Win Rate
Share of tests that beat the original, reported honestly.
Reported: quarterly
Client work
Work for Australian brands, since 2009.
A selection of the businesses we have supported with web design, development and digital marketing. Open any project to see what we delivered.
eCommerce design & development
eCommerce · Australia
Eco Care Group
An online store for an Australian-made hygiene brand.
Read case study
Web design, development & marketing
Health & NDIS · Australia
Embrace Ability
A custom website and NDIS marketing for a registered provider.
Read case study
Digital marketing
Professional Services · Sydney, NSW
SMG Group
Digital marketing for a Sydney accounting practice.
Read case studyPlans & pricing
A/B Testing & Data-Driven UX Optimisation / Options
Every engagement is scoped and quoted after a short discovery call, with a fixed price and timeline agreed before work starts. Call 1300 189 687 or email info@orkadigitals.com.au.
10 Hours
A steady testing rhythm for smaller sites.
10 Hours
per month
- Hypothesis backlog
- About one test a month
- Sample size planning
- Results library
- Custom variant builds
- Research inputs
Recommended
20 Hours
A full experimentation programme.
20 Hours
per month
- Hypothesis backlog
- 2-4 tests a month
- Variant design and build
- QA across devices
- Results library and reports
- Winner implementation
30 Hours
Testing plus research to feed it.
30 Hours
per month
- Everything in the 20 hour plan
- Usability research inputs
- Analytics deep dives
- Multi-page experiments
- Quarterly strategy review
- Team training on testing
Client Testimonials
Australian Businesses We Work With
Feedback from organisations that chose to plan and build properly.
“Highly professional and creative team. Anna and Robert are a delight to work with, extremely friendly and patient. They did an exceptional job for us. Thanks guys!”
Rustam Sandhu
Ivisapoint
“Excellent work quality and hard working team. Very happy with their service and fast delivery of the products.”
Abdur Rahman
Techno Point
“Looking forward to working again with this dynamic and hard working team.”
Ashraf
ECO Care Group
Trusted by Australian brands since 2009
Foodworks · IGA · Pizza Hut · Hills · SMG Group · Beyond Care Australia · Eco Care Group
Foodworks & IGA
National grocery groups
Pizza Hut
Hospitality brands
Beyond Care & Embrace Ability
NDIS & care providers
FAQ
Answering Your Questions
Straight answers to what clients ask before starting this work.
Have a different question?
Our experts are ready to audit your online presence and guide your path forward.
Get in TouchEnough for a few hundred conversions per variant per month. Below that, research-led redesign is the more honest approach.
Yes, through multivariate testing, but it needs considerably more traffic. Sequential A/B tests are usually the better trade.
Roughly a third, in a healthy programme. If nearly everything wins, the tests are probably being read too generously.
Enough to reach a result in a sensible time. We check this first, and if traffic is low we recommend research-led changes instead of testing.
That a difference this large would rarely happen by chance. We set it before the test and do not stop early when a result looks good.
It should not. We check performance and avoid the flicker that poorly set up tests can cause.
Ready to settle design
debates with
evidence?
Book a free testing review. We will check your traffic, tracking and the first ideas worth testing.
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Expert guidance
Tailored to your goals
Actionable insights
Real strategies that work