Conversion Rate Optimization
Your traffic is fine.
Your funnel
isn’t.
Most businesses losing conversions aren’t losing them to bad products or bad traffic. They’re losing them to friction nobody noticed. I find it, fix it, and prove it worked.
CRO works when the conditions are right
It’s not the right tool for every business at every stage. Here’s an honest picture of who gets the most out of this kind of work.
You have steady traffic
A/B testing needs volume to reach statistical significance. Roughly 5.000+ monthly sessions on target pages is a workable starting point.
Conversions are flat or falling
Traffic is there but people aren’t completing purchases, signing up, or enquiring. You know something is wrong but not exactly what.
You want ongoing improvement
CRO compounds over time. Each experiment informs the next. A one-off audit helps, but a sustained engagement gets real results.
You’re open to being wrong
Good CRO challenges assumptions. The best clients are curious, not defensive, when data contradicts what they believed about their users.
Not the right fit if…
Your site is brand new and has very little traffic, or you’re looking for a one-time quick fix without any ongoing involvement. CRO requires data, time, and iteration to work properly. If you’re at an earlier stage, a UX audit or a well-structured website build might be a better starting point.
A structured process, not a bag of tricks
Every engagement follows the same rigorous framework. Not random changes pushed to production and no vanity metrics.
Identify friction points
I start by building a clear picture of where users are dropping off and why. This uses a combination of surveys, heatmaps, clickmaps, funnel analysis, and current conversion rate benchmarks. The goal isn’t to collect data for its own sake, it’s to find patterns that point to real problems.
Surveys · Heatmaps · Funnel analysis · Clickmaps
UX / Product Design
Build an Opportunity Solution Tree
Rather than treating each friction point in isolation, I map them onto an Opportunity Solution Tree to find the behavioral hypotheses underneath. Often several experiments can be linked to the same root cause, which means fixing one thing can lift multiple metrics at once.
Impact analysis and MDE calculation
Before running a single experiment, I calculate the Minimum Detectable Effect for each target page. This tells us how large a change needs to be to produce a measurable result given the traffic volume. Under 5% MDE means standard A/B testing works well. Between 5 and 10% requires a more substantial design change. Above 10% points toward user testing or preference testing in tools like Lyssna instead.
Run the experiment
The right validation method is chosen based on the impact analysis. A/B test, multivariate test, user test, or preference test. Each experiment has a clearly defined hypothesis, a success metric, and a minimum run time before analysis begins. No peeking at results early.
Analyse and conclude
After the test period, I analyse results and draw a clear conclusion: was the behavioral hypothesis in the experiment true or false? Either outcome is valuable. A win gets rolled out. A loss gets documented and feeds into the next hypothesis.
Meta analysis and compounding
Over time, I run a meta-analysis across all completed experiments to identify overarching themes. For example: users consistently respond well to reduced perception of shipping costs, or urgency cues backfire with this particular audience. These themes shape the next round of hypotheses and make each iteration smarter than the last.
Conversion audit
A structured review of your funnel using analytics, heatmaps, and session recordings. You get a prioritised list of friction points and opportunities.
Impact analysis
As a data-driven UX designer, I rely on heatmaps, analytics, testing and tweak until the design works.
A/B testing
Hypothesis-driven experiments with statistical rigour. I design, run, and analyse tests so you know exactly what’s working and why.
Ongoing optimisation
Monthly retainer to keep testing, iterating, and compounding results. Includes reporting, strategy calls, and experiment roadmap.