A structured analysis of a website’s conversion performance — identifying where visitors are leaving, where they are failing to take desired actions and what specific improvements to content, design, copy or user flow would increase the percentage of visitors who complete the target conversion goals.
Conversion rate is the percentage of visitors who complete a defined goal. Benchmarks vary significantly by industry, traffic source and conversion type. A B2B services website generating enquiries might aim for 1–3% of all visitors. An e-commerce site might target 2–4%. The most useful benchmark is your own historical performance trend, not an industry average.
Google Analytics 4 (goal completion rates, funnel drop-off, traffic source performance), heatmaps (click patterns, scroll depth), session recordings (individual user behaviour), form analytics (field-level abandonment), A/B test results (where available) and customer or user interview insights (qualitative context for quantitative patterns).
A systematic review of each key conversion pathway on the website — identifying entry points (where visitors arrive), the intended journey to conversion and the points at which the largest proportions of visitors exit. Prioritised recommendations are produced based on potential impact (volume of lost conversions) and implementation effort.
Unclear headline value proposition (visitors don’t immediately understand what the business does and for whom), weak or missing social proof, calls to action that are vague or not sufficiently prominent, form friction (too many fields or unclear purpose), slow page load times and a confusing navigation structure that prevents visitors from finding relevant service content.
Conversion optimisation improves specific elements of an existing website based on evidence — testing and iterating toward better performance without a full rebuild. A redesign replaces the site structure and visual design comprehensively. Optimisation is data-driven, lower-risk and delivers incremental gains; redesign is appropriate when the architecture or brand has fundamentally changed.
A/B tests require sufficient traffic to reach statistical significance within a reasonable time frame. A page receiving fewer than 500 conversions per month per variant cannot generate statistically significant results quickly. For lower-traffic sites, qualitative methods (heatmaps, session recordings, user testing, expert review) provide more actionable insight than running A/B tests.
Using an impact-effort framework: prioritise improvements with the highest estimated conversion impact on the most commercially important pages, achievable with the lowest implementation effort. Quick wins (changing CTA copy, improving a headline) should be implemented immediately; more complex changes (page restructure, new components) are planned for subsequent development cycles.
A one-off review produces a prioritised recommendations list. An ongoing CRO programme continuously tests, implements and measures improvements in cycles — building a compounding body of evidence about what works for the specific audience. Sustained CRO programmes deliver significantly greater long-term conversion improvement than isolated reviews.
When the improved variant shows a statistically significant increase in the conversion metric being targeted, with adequate sample size, over a sufficient test period to account for day-of-week and seasonal variation. Results should be measured against the specific goal metric (form submissions, purchases, click-throughs) not proxy metrics like time on page.