A low conversion rate is a symptom, not a diagnosis. Device mix, traffic quality, catalogue structure, stock, pricing and checkout friction can all affect the result. We combine event checks, funnel analysis and customer evidence to choose a useful first intervention.
Conversion rate optimisation uses behavioural evidence and customer research to improve the journey from landing page to purchase. We investigate product discovery, product-page decisions, cart and checkout before selecting changes. Where traffic supports a reliable experiment, we test; where it does not, we use research and clearly label the limits of before-and-after measurement.
You have traffic but do not know why shoppers fail to buy.
Event validation, device and funnel analysis, journey review and a prioritised hypothesis backlog.
Evidence for each problem and a clear next step for design, development or further research.
You have identified friction in navigation, product pages, cart or subscriptions.
Approved UX changes, implementation, critical purchase QA and a baseline measurement plan.
A release record showing what changed, why, and how its performance will be reviewed.
Your traffic and conversion volume can support meaningful controlled tests.
Hypotheses, experiment design, sample-size planning, QA and results review.
A decision log covering evidence, uncertainty and whether to keep, revise or reject the change.
Audit scope, number of journeys, implementation effort and testing infrastructure determine the proposal. Testing software and development work are identified separately. We agree research and release milestones first; experiment duration depends on traffic and the statistical plan rather than a promised uplift.
A low conversion rate is a symptom, not a diagnosis. Device mix, traffic quality, catalogue structure, stock, pricing and checkout friction can all affect the result. We combine event checks, funnel analysis and customer evidence to choose a useful first intervention.
Identify friction in product discovery, pricing, trust, cart, and checkout before it becomes lost revenue.
Use customer behaviour, analytics, and qualitative evidence to decide what deserves testing first.
Build a prioritised optimisation backlog your team can keep improving after the first wins.
Fuel Your Body2.3x subscriber growth in 5 monthsRedesigned the Shopify meal-subscription journey, simplified plan selection and cut mobile checkout friction.Read the case study ↗
French Factor20 premium product pages on custom ShopifyLuxury perfume ecommerce with refined interactions and product discovery that supports premium positioning.Read the case study ↗What would a better conversion rate be worth? Enter your sessions, conversion rate and order value to see the extra revenue.
Try the CRO calculatorEvery recommendation explains the friction, evidence, proposed change and success measure. Testing plans account for traffic, seasonality and the cost of a wrong decision.
Verify product views, add-to-cart, checkout and purchase events. Segment by device and acquisition source, and check stock, discounts and payment failures. Measurement errors and poor traffic quality should not be mistaken for a design problem.
Review navigation, search, product information, delivery promises, pricing, trust and subscription choices. Use available customer feedback and behavioural evidence to identify the questions the storefront leaves unanswered.
Scope page designs, theme work, app dependencies and performance implications. Test product variants, discounts, carts, subscriptions and payment journeys across agreed devices before releasing changes.
Use controlled experiments when sample size and infrastructure permit. For smaller stores, document the baseline, release date, traffic changes and seasonal effects. A before-and-after increase is not by itself proof that a redesign caused the result.
Agree funnel definitions, validate events and review traffic quality and device performance.
Combine journey research and customer evidence into hypotheses ranked by impact and effort.
Implement approved changes and test purchase flows, variants, subscriptions and tracking.
Review experiment evidence or clearly qualified before-and-after results before choosing the next change.
A fixed-scope review of what is holding growth back, with a prioritised plan you can act on with us or on your own.
A defined scope with milestones, sign-off points and a clear launch date for new builds, redesigns and migrations.
An ongoing team for optimisation, campaigns and support, reviewed monthly against the numbers that matter to you.
Yes, but a conventional A/B test may not produce a reliable answer quickly. We can validate tracking, review customer journeys and prioritise research-backed fixes. We state the limits of measurement and avoid calling a small before-and-after change a proven experiment.
Both are possible. An audit produces an evidence-led backlog; an implementation scope covers agreed UX designs, development and purchase-flow QA. The proposal names the owner of each task and distinguishes research, build work and ongoing testing.
Depending on the goal, we review product discovery, add-to-cart, checkout completion, revenue per visitor and subscription outcomes. Metrics are segmented by device and acquisition source so changes in traffic mix do not hide the actual customer journey.
No. We can commit to research, implementation quality and a sound measurement plan. Customer behaviour, traffic quality and trading conditions affect results. Published client outcomes are examples with their own context, not a promise for another store.
Share your store, traffic sources and the step where customers drop out. We will scope the research and measurement needed before recommending a redesign.
Share the essentials and the right TNM specialist will follow up with a useful next step.
