01
Hypothesis Backlog
Every idea ranked by expected impact, confidence and effort, so testing effort goes where the return is highest.
CRO & Experimentation
A testing programme that compounds every month.
Full-funnel conversion rate optimisation and structured experimentation: hypothesis backlog, A/B and multivariate testing, landing page and form experiments, offer and pricing tests, and a results library that turns wins into repeatable patterns.
32%
Average conversion lift
48
Tests run per year
95%
Tests run to significance
100%
Results recorded, wins and losses
Direct answer
CRO and experimentation is the disciplined practice of improving conversion through structured testing rather than opinion. It covers a prioritised hypothesis backlog, A/B and multivariate testing across landing pages, forms, offers and checkout, statistical analysis, qualitative research through session recordings and surveys, and a results library that records both winners and losers. AKESTECH runs this as a monthly programme, so improvements compound instead of being rediscovered each quarter.
What you get
Everything included, end to end.
02
A/B and Multivariate Testing
Experiments designed, implemented and analysed properly, with sample size calculated before launch rather than after.
03
Qualitative Research
Session recordings, heatmaps, surveys and user testing that explain why behaviour happens, not just that it did.
04
Offer and Pricing Tests
Structured experiments on bundles, thresholds, guarantees and price presentation, which usually move revenue more than layout.
05
Form and Checkout Experiments
Friction reduction, field ordering, payment options and trust signals tested where the money is actually lost.
06
Results Library
A documented record of every test, its hypothesis and its outcome, so learning accumulates instead of resetting with each new hire.
Research
Research before random testing
Testing random ideas produces random learning. Our programs start with quantitative and qualitative research — funnels, heatmaps, recordings, surveys and support tickets — so the backlog aims at proven problems rather than opinions about button colors.
- Quant first — Funnels and segments revealing where value actually leaks.
- Qual for why — Recordings, heatmaps and surveys explaining the behavior behind numbers.
- Voice of customer — Reviews and tickets supplying the exact language buyers use.
- ICE scoring — Impact, confidence and effort ranking every hypothesis objectively.
Rigor
Statistics taken seriously
Most testing programs quietly lie to themselves: peeking at results early, stopping at the first green number, ignoring sample size. We calculate power before launch, commit to durations and report confidence honestly — because a false win shipped permanently costs more than any single test.
- Pre-set sample sizes — Power calculations before launch, not rationalization after.
- Fixed durations — Full business cycles covered; no peeking, no early calls.
- Segmented reads — Device, source and new-vs-returning checked before shipping.
- Guardrail metrics — Refunds, AOV and margin watched so conversion wins stay profitable.
Compounding
Learning that accumulates
A test's value is not just its lift — it is the reusable insight about your buyers. The results library turns scattered experiments into institutional knowledge: every future page, offer and campaign starts from evidence instead of a blank page.
- Results library — Every test, hypothesis and outcome documented and searchable.
- Pattern extraction — Repeated wins distilled into design and messaging principles.
- Rollout discipline — Winners shipped everywhere they apply, not left on one page.
- Roadmap evolution — Each quarter's tests chosen from last quarter's learnings.
How we work
From audit to compounding results.
01
Baseline
We establish clean conversion, funnel and revenue baselines and confirm the tracking can support reliable testing.
02
Research
Quantitative and qualitative research identifies where value leaks and generates a ranked set of hypotheses.
03
Prioritise
Ideas scored on impact, confidence and effort, and sequenced into a roadmap that balances quick wins with larger bets.
04
Experiment
Tests built, QA'd and run to statistical significance, with no peeking and no early calls on partial data.
05
Compound
Winners shipped permanently, losers documented, and the next round designed around what the data revealed.
FAQ
Questions clients ask us.
Shopify CRO focuses on the store itself: product pages, cart, checkout, merchandising and platform speed. This service covers the entire acquisition funnel including landing pages, lead forms, offer and pricing tests, and ad-to-page experience across every channel. Many clients run both, with Shopify CRO owning the store and this programme owning everything upstream of it.
Reliable A/B testing generally needs a few thousand sessions per variant, depending on your baseline conversion rate and the size of effect you are trying to detect. Below that, tests take too long to reach significance, so we shift to higher-confidence sequential improvements and qualitative research instead of reporting unreliable results.
Most tests run two to four weeks, depending on traffic and effect size. We calculate required sample size before launch and commit to the duration, because ending a test early when it looks good is the most common way programmes produce false positives that quietly cost money later.
A losing test is a successful test, because it removes a hypothesis permanently and usually reveals something about your buyers. We document losses in the results library alongside winners, so the same idea does not get rebuilt and retested in twelve months.
The highest-impact, highest-confidence items, which are usually offer, message match and friction rather than button colours. Layout and micro-copy matter far less than whether the offer is compelling and whether the page answers the objection that is actually stopping the purchase.
Yes, though carefully. We test price presentation, bundling, thresholds, anchoring and payment terms rather than simply raising or lowering prices, and we monitor contribution margin and refund rates alongside conversion so a conversion win does not become a profit loss.
For most tests, no. We can run experiments through tag-based tools without touching your codebase. Larger structural tests or checkout changes need development support, which we can provide or coordinate with your own developers.
Monthly reporting covers tests run, results, statistical confidence, revenue impact of shipped winners and what is queued next. We report business outcomes rather than test counts, because a programme that ran forty tests and shipped nothing useful is not a success.
Replace opinions with evidence.
Get a free experimentation roadmap with your highest-value tests ranked.