Funnel analysis
A map of the path from first visit to enquiry, with drop-off at each step. It shows exactly where people are lost.
If two out of a hundred visitors leave an enquiry and you want more enquiries, there are two routes: buy more traffic, or make it three out of a hundred. The second is cheaper and its effect carries across every channel you already use.
Conversion optimisation is not about button colour. It is about where people drop out and why: a form with too many fields, missing pricing information, no clarity on what happens after submitting, slow loading on a phone. Those things are visible in data, not guessed at.
So we always start with measurement. If there is no data on where people drop out, our first job is to make it visible.
What it includes
Behavioural data
Heatmaps, session recordings and form analysis. They show what people do rather than what we assume.
Landing pages
Pages built for a specific campaign and intent, instead of sending ad traffic to the homepage.
A/B testing
Comparing variants where volume is sufficient for a reliable conclusion. On low traffic we use qualitative methods instead of tests.
Form optimisation
Fewer fields, clearer error messages, splitting long forms into steps. Usually the fastest win on a site.
How it runs
01 — Measurement
We set up tracking on every funnel step, if it does not already exist.
02 — Diagnosis
Quantitative and behavioural data together: where they drop and what they do just before.
03 — Hypotheses
Ranked by expected effect and cost to implement. The cheap, high-impact changes go first.
04 — Implement and verify
The change ships and is measured. If it does not help, it is reverted — that is a result too.
What We Do
01
Funnel analysis
02
A/B testing
03
Heatmaps & recordings
04
Landing page optimization
05
Checkout optimization
Ideal For
·E-commerce
·SaaS
·Lead generation
·High-traffic websites
FAQ
There is no universal figure — it depends on the sector, the price point and the traffic source. The comparison that matters is with your own previous state. If someone quotes an "industry average" without knowing where your traffic comes from, the number means nothing.
A/B tests are not meaningful at low volume, because the result will not be statistically reliable. But qualitative work is: session recordings, form analysis and conversations with customers reveal problems even with few visitors. Tests come later, once there is volume.
Some changes — simplifying a form, speeding up a page — take effect the moment they ship. Tests need enough visitors to reach reliability, which depends entirely on your traffic. We give a realistic estimate once we have seen the volume.