Conversion Research Sprint
Funnel analysis, session replays, heatmaps, on-site polls and user tests across your top revenue templates. Output is a ranked list of friction points with revenue attached.
Sydney, NSW
Conversion Rate Optimisation in Sydney
A structured test programme for Sydney stores, built around the two things that actually lose Australian orders: what delivery costs and whether it will fit.
Delivered remotely for brands across Sydney and Australia.
Grow · Sydney
Why Sydney brands come to us for this
- Delivery cost treated as the primary conversion lever: threshold, estimate and method choice tested where the doubt actually starts
- Fit and return-risk interventions built for swim and activewear, where the return rate justifies the work more than another ad dollar
- Sample sizes calculated in advance for Australian traffic volumes, so the programme runs fewer, better-powered tests instead of a pile of inconclusive ones
- Testing calendar runs February to October, with peak reserved for harvesting research at the highest volume of the year
- Returns and refund copy written to match Australian Consumer Law guarantees, which is both a compliance and a conversion issue
Australian abandonment has a dominant cause and it is not trust or page speed. It is the delivery line appearing later than the customer expected, at a number they did not price in. That is a research problem before it is a test problem, so the first sprint goes through your own session recordings, site-search logs and support inbox looking for where the delivery question gets asked and where it goes unanswered. Then we test the answers: threshold placement, cart progress messaging, estimate-by-postcode on the product page, method choice at the cart rather than after the address form.
The second leak is fit, and in this city it is concentrated in the categories that sell best. Swimwear and activewear carry return rates that make a fit intervention worth more than an extra ad dollar, and the levers are testable — size guidance in the buyer's own terms, reviews filtered by body type and by size taken, exchange framed as easy before purchase rather than after. For homewares and outdoor living the equivalent is scale and material: dimensions in context, freight expectations set on the page, and enough detail that a customer does not need to email before buying.
There is also a hard statistical constraint here that agencies rarely say out loud. A store selling into a domestic market of 27 million usually has less traffic per template than an equivalent US brand, so a programme of many small tests will spend the year producing inconclusive results. We run fewer, larger tests with pre-calculated sample sizes and a stated minimum detectable effect, we hold the checkout tests for the periods with enough traffic to power them, and every result gets reported in full — including the ones that lost, which are frequently the most useful thing we learn all quarter.
Local context
Peak is for harvesting, not for experimenting
The Sydney testing calendar is shaped by a six-week concentration that no other part of the year resembles. Traffic from Boxing Day into January is high enough to power tests quickly and completely unrepresentative — different intent, different device mix, heavy discount exposure, gift buyers who are not your customer. A winner declared in that window frequently fails to hold in March. So we treat the season as a period for locking down what we already know works, running the peak checklist and collecting research: recordings, search logs, support tickets and abandonment reasons at the highest volume of the year. The test programme itself runs hard in the shoulder — February through October — where the sample is representative and a result means something in twelve months. The end-of-financial-year period in June is its own smaller test window for anything B2B or business-purchase related, since Australian buying behaviour genuinely shifts before 30 June.
Scope
What Conversion Optimisation includes
The same standard of work we run for every client — applied to a Sydney brand’s realities.
Full service detailPrioritised Test Roadmap
Every hypothesis scored on impact, confidence and effort, with the projected revenue and required sample size stated up front. You always know what runs next and why.
Experiment Build & QA
Tests built and QA'd across devices, browsers and your app stack, with flicker-free rendering and no measurable hit to Core Web Vitals.
Statistical Analysis
Pre-declared sample sizes, sequential-testing guardrails and segment-level readouts by device, traffic source and new versus returning. No peeking, no calling a test at 80%.
Checkout & Cart Optimisation
Cart drawer, shipping thresholds, payment options, express checkout placement and post-purchase upsell, tested against AOV and revenue per session rather than clicks.
Monthly Programme Report
Tests run, results in full, cumulative revenue impact and what the results taught us about your customers. Losers are reported in the same detail as winners.
Scoped and quoted for your Sydney store
We do not work off a rate card. Every Sydney engagement starts with a fixed statement of work — named deliverables, named dates, one number — written after we have looked at your store, not before. If a smaller first step would serve you better, we will say so.
Get this scopedHow it runs
From kickoff to results
Measure
We instrument the funnel properly first. Most stores have broken or double-counted events, and you cannot optimise against numbers you cannot trust.
Research
Quant tells us where visitors leave. Qual tells us why. We combine analytics, replays and direct customer input before writing a single hypothesis.
Prioritise
Hypotheses are scored and sequenced so the highest-value, lowest-effort tests run first. The roadmap is shared and you can reorder it.
Test
Two to four concurrent experiments depending on traffic, each run to a pre-declared sample size. No test gets stopped early because it looks good on day three.
Implement & Compound
Winners get hard-coded into the theme, losers get documented, and every result feeds the next round of hypotheses. The programme gets smarter each month.
Proof
Conversion Optimisation results
Anonymised under NDA. Figures pulled from the client’s own analytics.
Premium Skincare (DTC)
8-figure DTC brand, ~140 SKUs, US + CA · Shopify Plus (migrated from Magento 2)
Magento 2 cost $9k a month in hosting, extensions and emergency dev before a single feature shipped. Mobile LCP sat at 5.4 seconds and mobile converted at less than half the desktop rate across 68% of sessions. The named constraint: 11,412 indexed URLs and a top-3 organic position on their hero category, so a migration that lost rankings would cost more than the platform ever saved.
+41%mobile conversion rate1.62% to 2.28% over the first 90 days post-launch
5.4s → 1.8smobile LCP75th-percentile CrUX field data, not lab
+27%organic sessionssix months post-migration vs. pre-migration baseline, branded queries excluded
$108k/yrplatform cost removedhosting, extension licences and the standing emergency dev retainer
Performance Apparel (DTC)
~$6M/yr DTC, 900+ SKUs across size and colour variants, US · Shopify Plus
Returns ran at 31% and refund cost consumed the entire paid media margin. One size chart image served 40 different fits, and 62% of add-to-carts started on a collection page that never showed variant availability. The named constraint: no new product photography budget, so every fix had to come out of the existing asset library and the review corpus.
+29%sitewide conversion rate1.71% to 2.21%, five-month average
+14%average order value$84 to $96 once the threshold bar and cross-sell shipped
31% → 22%return ratenine-point drop, roughly $310k/yr in recovered margin
1.9x → 2.4xblended MERwith paid spend held flat throughout
In their words
Clients on this work
CVR 1.9% → 2.7%
“We'd been running our own tests for a year and calling every 3% swing a win. The first thing they did was tell us our sample sizes were garbage, which was not a fun call to sit through. Six months later we're getting wins that actually hold up when you re-measure them. Sitewide CVR went from 1.9% to 2.7%.”
Founder & CEOApparel DTC brand, ~$12M/yr · Los Angeles, CA
Verified client, 2025FAQ
Conversion Optimisation in Sydney — your questions
Yes, but the design has to change rather than the ambition. Fewer tests, bigger swings, longer run times and honest pre-calculated power — a redesigned product page tested as one unit rather than six micro-variations tested separately. Where a page genuinely cannot reach significance, we say so and use qualitative research and sequential rollout with careful before-and-after measurement instead of pretending a test was conclusive.
We would rather not, and the reason is contamination rather than caution. That traffic behaves nothing like your March traffic: heavier discount exposure, more gift buyers, a different device and channel mix. A winner from that window often reverses when normal demand returns. It is the best research period of the year though, so we run recordings, exit surveys and search-log analysis at full volume and let it feed the first-quarter roadmap.
It affects both wording and conversion. Australian consumers have statutory guarantees on goods that a store policy cannot remove, and blanket phrases like 'no refunds' are the kind of thing that creates real problems. The practical CRO angle is that a clearly written policy which explains the guarantees plus whatever change-of-mind window you voluntarily offer reduces pre-purchase hesitation, particularly in apparel. We test placement and wording; we do not test misleading claims.
Results land as a written readout with the data, the call and the recommendation already in it, ready when your day starts. We do not hold a test open waiting for a meeting — the stopping rule is set before launch, so the decision is arithmetic rather than a discussion. A monthly live session, early in your morning, covers the roadmap and anything contested.
Around 40,000 sessions and 800 orders a month is where a testing programme becomes statistically viable. Below that, tests take months to reach significance and you are better served by research-led redesign work and analytics fixes. We will tell you honestly which bucket you are in.
The first test goes live in week three, after research and instrumentation. Meaningful cumulative impact typically shows around month four, once six to ten tests have run. CRO is a compounding programme, not a one-month fix, and anyone promising otherwise is selling best practices.
Some do, and that is normal. A loser is still information: it rules out a hypothesis and sharpens the next one. We report losses in the same detail as wins because a programme that only ever produces winners is one that is not being measured properly.
Next step
Conversion Optimisation for your Sydney brand.
Thirty minutes with the strategist who would actually run your account. We screen-share your store, read your data live, and tell you the three highest-value things we can see from the outside.
Shopify or Shopify Plus stores doing $150k/mo or moreFounder, CEO or eCommerce lead on the callNo deck and no pitch — we open your store instead
Prefer to write it out? [email protected] gets a real reply the same business day, Mon-Fri, 9am-6pm MT.