Conversion funnel audit
I walk the path from landing to order and find the places where people drop out.
- Funnel analysis in the data (GA4)
- Where and why people back out of buying
- Priorities ranked by impact on revenue
Paying for traffic that does not turn into orders? Conversion work starts in the data – heatmaps and session recordings show why people leave, and I fix it straight away. No PDF for the drawer.
No commitment · a written estimate within 24 hours
For three years I chased one number on my own Shopify store: how many people from the ads actually bought. Conversion by tenths of a percent, because it was my money. I know where orders get lost – I patched those holes myself. Optimisation is not theory to me; it is a craft learned in operation.
The same approach goes into every store I build: numbers first, pixels second.
I do not stop at charts. I find where you are losing people, design the fix and ship the important ones into the store myself.
I walk the path from landing to order and find the places where people drop out.
Numbers say where people leave. Recordings say why. I set up Microsoft Clarity and watch real sessions.
I do not change things on a hunch. I write a hypothesis, test it and let the data decide.
The most common place a purchase breaks. Structure, content, trust and the path to the cart.
I remove the friction between “add to cart” and “ordered”. On standard plans that means the cart and the path into checkout; deeper checkout work is a Shopify Plus capability.
Most customers now buy from a phone – and when the site is slow or awkward on mobile, orders walk away.
CRO is neither cosmetics nor guessing what might work. This is how I approach it.
I set up analytics and behaviour recordings and only then change anything. Every adjustment has a reason in the data.
A report is half the job. The other half – shipping it into the store in Liquid – I do too. Audit and change in one place.
I do not chase a hundred small things. I take what actually moves revenue first, not what is most visible.
Measure, hypothesise, test, evaluate – and again. Each round builds on what the last one taught.
I set up analytics and Microsoft Clarity, go through the data and find where and why orders are escaping.
I rank the opportunities by impact on revenue and by effort. I start with whatever moves the number most.
Where there is enough traffic, I A/B test the change. Where the problem is obvious, I simply fix it.
What demonstrably worked stays. What did not gets thrown away. Then the next round.
At Aniball I did not touch the product page until the data showed me where people hesitated and where they left.

A Czech lovebrand trusted by more than 130,000 customers, selling across Europe. I did not start with design but with the question of where on the page people hesitate and where they back out – and the new section order came out of that. And here is the difference: the analysis turned straight into shipped code, not into a presentation.
The logic is simple: more orders from traffic you are already paying for. You can start with a one-off audit, or I run continuous optimisation sprints – either way the price is agreed upfront.
A one-off CRO audit is clearly bounded – I go through the store, find the highest-impact priorities and ship the quickest ones immediately. You do not leave with a list for the drawer, but with changes already live. I price it fixed upfront; it is a fraction of a month of ad spend, so it is a small first step on which you can test me. If you want to keep the gains coming, we run continuous sprints – but that is a choice, not a condition.
An indicative estimate in writing within 24 hours · no commitment
No, and anyone promising it is guessing. Conversion is affected by plenty of things outside my control – the product, the price, the season, the quality of the traffic. What I guarantee is the method: I measure, I test and I let the data decide. What demonstrably worked stays; what did not gets thrown away. I do not make changes I cannot defend with a number.
Both, and that is my main difference. Most CRO agencies send you a report and that is where it ends – the rest is your problem or your developer’s. I write the changes into Liquid myself, so there is no second supplier standing between “we know what to improve” and “it is live”.
Yes, but differently. A/B testing needs volume – without it a test will never reach significance and you would be reading noise. With lower traffic I work from recordings, heatmaps and plain usability problems, which are usually obvious enough not to need a test. I will tell you honestly which regime you are in.
Partly, and it is worth being precise. Deeper changes to the Shopify checkout itself are a Plus capability. On standard plans I work with everything up to it – the product page, the cart, the path into checkout and the thank-you page – which is where most of the losses happen anyway.
GA4 for the funnel and Microsoft Clarity for heatmaps and session recordings. Clarity uses cookies, so it runs behind the consent banner – anyone telling you it is cookieless is wrong. If you already have your own stack, I work with that.
They bring traffic and read numbers. I change the store. Those are different jobs, and the handover between them is where most CRO dies – a recommendation nobody ships. I take the finding and turn it into code.
Obvious usability fixes show up within days of shipping. A genuine A/B test needs a few weeks to reach significance, depending on your traffic. Anyone promising a number in a fortnight is not measuring.
Partly, and only where there is a reason. CRO is not a redesign – I am not changing the look for its own sake. If it turns out the store needs a redesign rather than tuning, I will say so rather than selling you sprints.
Write a few lines about the store and the traffic. Within 24 hours you will have where I see the biggest opportunities and how I would approach them, in writing. No call, no commitment.
Would rather not fill anything in? Write straight to ahoj@hynekkraus.cz