Conversion Rate Optimisation Agency in Auckland

Quantitative analysis uses Google Analytics 4 to map the conversion funnel in detail — where traffic enters, which pages generate the most exits, where drop-off occurs in multi-step flows, which traffic sources and audience segments convert best, and which device types have the largest conversion rate gaps. We use Hotjar or Microsoft Clarity for heatmaps (showing where visitors click and scroll) and session recordings (showing exactly how individual real users navigate your site). These tools reveal patterns invisible in aggregate analytics — such as a form field that confuses users, a CTA button that is below the fold on mobile, or a pricing page that causes visitors to exit rather than progress.

Qualitative research adds the 'why' behind the numbers. We analyse on-site survey responses (asking visitors who are about to exit why they are leaving), customer interview transcripts, sales call recordings, and live chat transcripts to understand the objections, confusions, and anxieties that prevent conversion. This qualitative data generates hypotheses that are both specific and testable. For example, if 40% of survey respondents say they left without converting because they were unsure about delivery timelines, the hypothesis is clear: adding prominent delivery timeline information to product pages should improve conversion rate.

Every test we run starts with a documented hypothesis derived from research insights, a clear success metric, a minimum detectable effect calculation to determine the traffic required for significance, and a pre-set test duration (minimum two weeks to account for day-of-week variation). We use Google Optimize or VWO for A/B testing, with a pre-set significance threshold of 95% statistical confidence before declaring a winner. All tests are monitored for novelty effect — the tendency for new experiences to perform well briefly before reverting to baseline — and we validate winner performance over a post-test monitoring period before full implementation.

Our testing prioritisation framework evaluates each test opportunity across three dimensions: expected impact (how much could this test move the conversion needle if the hypothesis is correct), confidence (how strong is the research evidence behind this hypothesis), and ease of implementation (how much development effort is required). This PIE framework ensures our testing roadmap maximises expected improvement per unit of development effort, producing the fastest path to measurable conversion gains.