CRO sprints that lift revenue per session.

Conversion Rate Optimisation is the discipline of systematically increasing the percentage of visitors who take a desired action — purchase, signup, booking — without spending more on traffic. The common failure mode is running CRO as a series of disconnected "let's try a different button colour" tests with no underlying hypothesis; the version that actually moves revenue starts from quantitative data (analytics, heatmaps, session recordings) and qualitative research (user testing, support-ticket themes) to identify where and why visitors are actually dropping off, then designs experiments to fix that specific friction.

We run CRO as an ongoing experimentation programme rather than a one-off redesign, because the highest-leverage opportunities change as traffic sources, offers and the competitive landscape shift. Every experiment is built with statistical rigor (proper sample sizing, significance testing) and every winning variant becomes a reusable pattern applied elsewhere in your funnel — so the gains compound across pages and quarters instead of resetting with every new test.

The most common CRO mistake is calling a test winner too early, based on a result that looks good after a few days but has not reached statistical significance — this is how teams end up implementing "winning" changes that actually have no real effect, or worse, quietly hurt conversion, because the early sample was simply not large enough to be trusted. We also see teams run a full site redesign based purely on opinion or design trends, with no underlying data on where visitors were actually struggling, and discover post-launch that conversion dropped because the redesign removed something that was quietly working. The discipline that prevents both mistakes is the same: start from real behavioural data (where do people actually drop off, what do session recordings show them hesitating over), form a specific hypothesis about why, and only then design an experiment — changing one meaningful thing at a time so you can actually attribute the result, rather than redesigning five elements simultaneously and having no idea which change drove the outcome.

Rigorous A/B testing needs real traffic volume to reach statistical significance in a reasonable timeframe — a page getting a few hundred visitors a month will take a very long time to produce a trustworthy result from a traditional split test. For lower-traffic pages, we shift toward heuristic and qualitative-driven optimisation (expert review, user testing, session-recording analysis) rather than running underpowered experiments that would take months to reach significance, and we are upfront about which approach fits a given page's traffic level before proposing a testing roadmap.