Service · Optimise & Scale
Shopify CRO
We change what the data justifies, test it properly against a sample size that can actually resolve, and report results including the tests that did nothing.
- GA4
- Shopify Analytics
- Server-side events
- A/B tooling
- Group
- Optimise & Scale
- Starts with
- Technical audit
- Delivery
- Git + staging theme
- Handover
- Written documentation
01Overview
Shopify cro done properly starts with instrumentation, not opinion. Before any test ships, funnel events, product analytics and session review are in place so every hypothesis is grounded in what customers are actually doing on your store, not what a best-practice article assumes.
The discipline that's usually missing is honesty about statistical power. A lot of CRO work on Shopify runs tests that traffic can never resolve and calls a coin flip a win. We do the sample-size maths first and are explicit when a test can't conclude in a sensible timeframe.
Where a test can't run, changes are still prioritised and shipped based on the strongest available evidence — funnel data, recordings, and a clear mechanism for why the change should help — and reported the same way either way.
02The problem
Most CRO programmes are a queue of opinions dressed up as a roadmap. Without instrumentation and enough traffic per variant, the reports are decoration, and the same redesign gets relitigated every quarter.
03You will recognise this if
- Traffic is healthy, conversion is flat
- Past tests were never conclusive
- Checkout or PDP drop-off is unexplained
- CRO decisions are made from opinion, not data
- Winning tests never make it into production properly
04How we work on it
- 01
Instrument first
Funnel events, product-level analytics and session review are set up so decisions have an evidential basis rather than a hunch.
- 02
Diagnose the drop-off
Session recordings, heatmaps and funnel data are read together to find where and why customers actually leave, not where we assume they do.
- 03
Prioritised hypotheses
Each idea is framed as a hypothesis with an expected mechanism and the metric it should move, then ranked by expected impact and effort.
- 04
Tests that can conclude
Sample-size maths is done up front. If your traffic cannot resolve a test in a reasonable window, we say so and ship a considered change instead of running theatre.
- 05
Build and QA
Winning changes and test variants are built to production standard, not throwaway script hacks, so they survive beyond the test.
- 06
Honest reporting
Wins, losses and inconclusive results are all reported with the underlying data and reasoning, so the backlog reflects what's actually true.
04What you get
Deliverables, not decks.
- Analytics and funnel instrumentation
- Session recording and behavioural audit
- Prioritised hypothesis backlog
- Sample-size and test-duration modelling
- Test implementation and QA
- Results readouts with raw data
- Roadmap of validated changes
- Production-grade implementation of winning variants
1// field-data first — never lab scores alone2const budget = { lcp: 1800, tbt: 120, cls: 0.05 };3 4onCLS(send); onINP(send); onLCP(send);5// regressions fail the deploy, not the quarterDelivery pipeline
- Audit
- Scope
- Build
- QA
- Measure
05What changes
- Instrumented funnel
- Full visibility into where customers drop off, replacing guesswork with an evidence trail.
- Validated backlog
- A roadmap of changes that have either been tested and won, or are backed by strong qualitative evidence.
- Honest test history
- A record of what was tried, what worked, what didn't, and why — so the same idea doesn't get relitigated every year.
06How an engagement runs
- 01
Audit
We review your current analytics setup, funnel data and past test history to find the gaps and the highest-value opportunities.
- 02
Scope
A prioritised hypothesis backlog and instrumentation plan are agreed, with sample-size modelling done for anything proposed as a formal test.
- 03
Delivery
Tests and changes are built, QA'd and shipped to production standard, with results reported transparently including inconclusive outcomes.
07Stack
- GA4
- Shopify Analytics
- Server-side events
- A/B tooling
- Liquid
- TypeScript
08Related work
09Questions
More in Optimise & Scale
Shopify CRO
Send us the store and the symptom.
A senior engineer reads it, tells you what is actually going on, and scopes only what the evidence supports.