What we build
Every layer of the storefront — instrumented, tested, shipped.
Each capability replaces the "let's try it and see" cycle with a falsifiable, server-side test that ships to 100% only when it clears the LTV guardrail. Weekly cadence, not quarterly redesigns.
Server-side A/B test infrastructure
Flag-driven experimentation at PDP, cart, and checkout — sticky bucketing, no UI flicker, and guardrail metrics wired before a test ever reaches 1% of traffic.
LTV-aware ranking, not just CVR
Every experiment evaluated on 30-day LTV alongside conversion rate. Winners on CVR that lose on repeat-order economics get caught before they ship to 100%.
Personalised merchandising + recs
Recommendation engine with social-proof layering — reviews, scarcity signals, cross-sell by affinity. Ranking learned per cohort, not a vendor default.
Exit-intent, tuned per segment
Exit triggers calibrated per traffic source, device, and basket size. No blanket offers — every intervention is matched to the cohort it's likely to recover.
Mobile-first optimisation
Every experiment runs on mobile-first — checkout, address entry, one-tap payment, and wallet-aware flows. Desktop parity checked, not assumed.
Funnel analytics + drop-off heatmaps
Session replay, funnel drop-off, and scroll/tap heatmaps feeding the hypothesis backlog — so the next test targets the biggest revenue-moving friction.
Use cases we optimise
The surfaces where a 0.3pp lift pays for the year.
Every experiment we run is tied to a specific surface, a specific hypothesis, and a specific metric. Shipping a +0.3pp CVR lift on a checkout step sounds small — but compounded across traffic, AOV, and LTV, it pays for the year of CRO work.
PDP variant testing · hero + PDP layout
Hero image, gallery order, trust badges, reviews placement, swatches — tested against CVR, AOV, and 30-day LTV to find the layout that earns its pixels.
Checkout + cart flow experiments
Shipping-step friction, address autocomplete, wallet buttons, guest checkout, trust signals — server-side tests that compound into real completion-rate lift.
Exit-intent, per segment
Different offers, social proof, or help prompts per visitor cohort — mobile vs desktop, new vs returning, cart-size banded — with LTV guardrails baked in.
Recommendation + cross-sell ranking
PDP cross-sells, cart upsells, and post-purchase bundles ranked by predicted LTV contribution — not just next-click likelihood or vendor defaults.
Session-replay-driven roadmap
FullStory and Hotjar feeds converted into prioritised hypothesis backlog — so every sprint targets a known friction, not a gut-feel redesign.
Mobile-first wallet + one-tap flows
Apple Pay, Google Pay, Shop Pay, and local wallet integration tested for real uplift — especially the abandonment drop-off at the address-entry step.
Model families we deploy
The intelligence behind every experiment decision.
No single model ships a winner. Significance, ranking, exit classification, and drop-off analysis combine — so the experiment backlog, evaluation, and rollout are all honest, not stitched together from dashboards.
Computes statistical significance, minimum detectable effect, and time-to-signal per experiment — with peeking protection and early-stopping safeguards.
Ranks products per visitor using recency, affinity, and predicted 30-day LTV — not just click probability. Keeps margin-eroding items from being over-recommended.
Predicts which exiting visitors are recoverable and which are not — so offers, social proof, and help prompts go to the cohorts that actually convert on them.
Segments drop-off by device, traffic source, and browser to surface the specific friction driving a funnel dip — not just the headline number.
Data sources wired into every experiment
Every signal that moves conversion — integrated.
Pulled in real time, normalised into a single event schema, versioned alongside the experiment that consumes them.
Honest attribution, not just lift numbers
A CVR lift alone doesn't mean the experiment worked.
Every experiment is evaluated on CVR, AOV, and 30-day LTV — in that order. A variant that lifts CVR but drops LTV typically fails to ship. The rollout record captures the decision, the guardrails, and the learning — whether the variant shipped or got archived.
- Primary + guardrail metrics logged per experiment
- 30-day LTV check before any 100% rollout
- Segment-parity audit before promotion
- Hypothesis + result archived for future learning
Frameworks we align to
Why Axccelerate for CRO
Not a testing tool.
A growth operating system.
A testing tool runs whatever you queue. We run a hypothesis backlog — prioritised by revenue moveability, evaluated on LTV, and archived with an honest learning record.
Pricing
Priced to the cadence, not the test count.
CRO deployments are custom — we scope against your traffic, surface count, and engineering capacity before quoting.
Glossary
The vocabulary behind every experiment.
A quick reference for the terms that show up in experimentation — the language your CRO, analytics, and engineering teams will all use.
- A/B test
- Two-arm controlled experiment
A head-to-head split — control versus a single variant — with traffic randomly assigned and a primary metric chosen before launch. The default for testing a single, isolated change.
- Multivariate
- Multi-factor experiment
A test with multiple simultaneous changes — e.g. hero image × headline × CTA — evaluated for both individual and interaction effects. Costlier in sample size; useful for combined PDP changes.
- Control
- Baseline variant
The unchanged experience against which every variant is measured. Everything about the experiment is relative to the control's performance during the same window.
- Variant
- Challenger experience
The candidate change being tested — a new PDP layout, a new CTA, a different checkout step. Variant B, C, etc. when more than one challenger is running at once.
- Statistical significance
- Confidence the lift is real
The probability that the observed difference between control and variant is not down to chance. Retail CRO typically ships at 95% (p < 0.05), sometimes 90% for lower-risk tests.
- p-value
- Probability under the null
The probability of seeing the observed lift (or more extreme) if there were actually no difference. Lower p-value = stronger evidence. 0.05 is the conventional threshold.
- MDE
- Minimum detectable effect
The smallest true lift the experiment can reliably detect at its sample size. If you need +0.3pp but only have MDE of +1pp, the test can't tell you anything useful.
- Exit intent
- Leaving-behaviour signal
Behavioural cues that a visitor is about to abandon — cursor leaving the viewport on desktop, tab-blur on mobile. A trigger point for recovery interventions.
- CVR
- Conversion rate
Orders divided by sessions (or another denominator). The headline metric for most CRO — but not the only one. A winner on CVR that loses on LTV is a loser.
- AOV
- Average order value
Revenue per order. CVR × AOV × sessions = revenue — so lifting AOV by 10% is equivalent to lifting CVR by 10%, without any checkout change needed.
- LTV
- Customer lifetime value
The predicted revenue a customer will generate across their full relationship. The guardrail metric for every experiment — especially anything with a discount.
- Funnel
- Step-by-step conversion path
The ordered sequence from first touch to completed order — typically session, PDP view, add-to-cart, checkout start, payment, order complete. Drop-off between steps is the prize.
- Drop-off
- Step-to-step abandonment
The share of visitors who leave between two funnel steps. Concentrating optimisation on the largest drop-off step is the fastest path to revenue lift.
- Server-side experiment
- Backend-decided variant
Variant assignment and rendering decided on the server — no client-side flash, no UI flicker, sticky bucketing by user id. The default for anything beyond cosmetic copy tests.
Your growth cadence, engineered.
30-minute scoping with a senior engineer and a growth-systems operator. You'll leave with a test cadence plan, integration sketch, and realistic timeline — not a sales pitch.