Stop Redesigning Pages Based on a Hunch
We diagnose with real visitor data before we test anything, so every change is backed by evidence, not opinion.
Conversion rate optimisation is the systematic process of diagnosing where and why visitors fail to convert, then testing specific, evidence-backed hypotheses instead of redesigning on instinct.
What is Conversion Rate Optimization?
Conversion rate optimisation is the systematic process of diagnosing where and why visitors fail to convert, then testing specific, evidence-backed hypotheses instead of redesigning on instinct.
Skipping the diagnosis costs more than a wasted redesign: teams under pressure to move a number reach for whatever change feels obvious - a new hero image, a reworded headline - and ship it without confirming that was ever the thing stopping people from converting, treating every drop-off as the same problem when a confusing form, a slow page, and a mismatched offer each need a different fix.
It's different from A/B testing alone, which is the execution step once a hypothesis exists, and from a full redesign, which changes everything at once and makes it impossible to know which change actually moved the number.
Common problems we solve
Changes made on a hunch
A new hero image or headline ships because it "feels obvious," not because evidence pointed there.
Every drop-off treated the same
A confusing form, a slow page, and a mismatched offer all get the same generic fix.
No evidence behind the decision
Redesigns happen without heatmaps, recordings, or funnel data confirming where visitors actually leave.
Tests called too early
A number that looks good on day two ends the test before it reaches real significance.
No institutional memory
Each redesign starts from scratch with no documented record of what was already tried.
Everything covered in this service
Diagnostic Research
Heatmap and scroll-depth analysis, session recording review, and form or field-level drop-off tracking.
Hypothesis Prioritisation
Impact-versus-effort scoring and backlog management across multiple pages.
Test Design & Execution
A/B and multivariate test builds with cross-device and cross-browser QA.
Statistical Validation
Sample size calculated before launch, plus significance and guardrail metric checks.
Rollout & Documentation
Winning variant rollout and a searchable history of test results and learnings.
Our optimization framework
Diagnose
Heatmaps, session recordings, and funnel data establish where visitors are actually dropping off before anything is proposed.
Hypothesis
Each finding gets turned into a specific, testable hypothesis about why the drop-off is happening.
Prioritisation
Hypotheses are scored by expected impact against effort, so the highest-leverage tests run first.
Design
Sample size and minimum run time are calculated before a single line of the test is built.
Execution
The test runs to its planned duration - we do not call it early because an early number looks good.
Rollout
Winning variants get rolled out, and the result - win, loss, or inconclusive - is documented for the next test.
What actually changes
| Before | After |
|---|---|
| Page redesigned on internal opinion | Change backed by heatmap and session recording evidence |
| Every drop-off gets the same generic fix | Each hypothesis addresses a specific diagnosed cause |
| Test stopped early because a number looked good | Test runs to a pre-calculated sample size and duration |
| Each project starts over with no record | A documented backlog of wins, losses, and learnings |
What we report on
Conversion rate movement
Tracked against your baseline from the diagnostic phase, not projected before testing starts.
Tests run to significance
Reported as the share of tests reaching proper statistical significance before a call is made.
Diagnosed drop-off points fixed
Tracked by how many identified friction points have a shipped, tested fix.
Documented learnings
Reported as the growing backlog of validated wins, losses, and inconclusive results.
Tools & platforms
Industries we optimize for
Built for teams like yours
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
Related Conversion Optimization services
One goal per page, executed properly
Landing page structure, copy, and design optimised against a single conversion goal.
Fix the step that's actually losing people
Full-funnel analysis to find and fix the specific stage where prospects drop off.
Stop Trusting Test Results That Were Never Statistically Real
We calculate sample size and run time before launch, so you get a result you can trust, not a number that looked good on day two.
Lead Conversion OptimizationMore leads that actually turn into customers
Optimisation focused specifically on lead-to-customer conversion, not just form-fill rate.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
A specific, evidence-backed statement of why a change should affect behaviour - not just "try a new headline." A good hypothesis names the observed problem, the proposed fix, and the expected effect, which makes the result interpretable whether the test wins or loses.
It helps but is not required - if tracking is missing or broken, that gets fixed as part of the diagnostic phase, since testing on top of unreliable data just produces confident wrong answers faster.
Enough to keep the backlog moving without running tests on overlapping traffic or the same page at the same time, which muddies which change caused which result. The right number depends entirely on traffic volume.
It gets documented as a real result, not treated as a failure. An inconclusive test tells us the variable tested probably was not the actual constraint, which redirects the next hypothesis rather than wasting a repeat test.
No - anyone promising a fixed percentage before running a diagnostic is guessing. What we commit to is a disciplined process: real hypotheses, properly powered tests, and an honest read of what the data actually shows.
Not sure if a redesign will actually fix anything?
We'll run a diagnostic before recommending a single change.
Ready to get started?
We usually reply within 24 hours.
Conversion Rate Optimization, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Diagnostic Audit | Heatmap and session recording review plus a funnel analysis report identifying where visitors actually drop off |
| Testing Roadmap | Prioritised hypothesis backlog scored by expected impact and effort to implement |
| Test Execution | Build, QA, and launch of each test with a pre-calculated sample size |
| Reporting | Statistical readout at test end with a clear rollout, iterate, or kill recommendation |
How this compares
| Diagnostic-Led CRO | Redesign by Instinct |
|---|---|
| Every test starts from a specific, evidence-backed hypothesis | Changes are made based on internal preference or a competitor trend |
| Sample size is calculated before a test launches | Tests are called early because a number looked good |
| Wins and losses are documented and compound over time | Each redesign starts over with no institutional memory |
This does not mean a full redesign is never the right call - sometimes the diagnostic points that way. It means the decision follows the evidence rather than preceding it.