Conversion optimization
Find the problems quietly costing you signups
Conversion problems are often not about copy or button color. A validation error on mobile, a slow pricing page or a button that does nothing can stop people cold. ROOKON connects funnel drop-offs to the errors and behavior that caused them.
The problem
An A/B test can't fix a broken button
CRO tools are built to test variations. Yet many drop-offs come from defects nobody noticed: a form that rejects valid input, a third-party script that blocks checkout, a layout that hides the call to action on some screens.
- Funnel reports show where people leave, not why
- Signup bugs go unnoticed because nothing visibly crashes
- The evidence is spread across analytics, replay and engineering
- Small products rarely have the traffic for long A/B tests
Live demo 1 of 3
Explore a simulated upgrade funnel
Simulated funnel. Select a step to see its drop-off and the signals ROOKON found there, then open the diagnosis.
- Select “Selected a plan”
- Press “Diagnose with AI”
rookon · funnel · pricing → upgraded · last 7 days
Simulated dataSelected a plan
/billing
- Continue to “Started checkout”
- 51%
- Visitors lost at this step
- 1,126
What ROOKON found
- Error“Upgrade to Pro” throws an error
TypeError in PlanSelector for customers whose saved interval is “yearly”.
212 sessions - Rage clicksRage clicks on the Upgrade button
Customers click 3–6 times when nothing happens, then leave.
187 sessions
How ROOKON helps
What you get
Funnels from real journeys
Define a funnel from pages or events (visit, pricing, signup, activation, upgrade) and ROOKON tracks how many people complete each step.
Root causes at every step
Each step lists the errors, slow requests and frustration signals concentrated there, so a drop-off comes with suspects, not guesses.
Evidence you can watch
Jump from a finding to the sessions that show it and see the problem the way your visitors did.
Prioritized by impact
Findings are ranked by how many sessions they affected at that step, so the first thing you fix is the thing that matters most.
From finding to change
Recommendations explain what to change and the expected benefit. For code-level problems, a proposed diff can follow for your review.
Live demo 2 of 3
Watch a visitor drop out of the funnel
A simulated session from the failing step: the upgrade click that does nothing, the rage clicks, and the exit.
- Press play
rookon · session ses_4821 · desktop · Safari · DE
Simulated data2,914 recordings stored. Size depends on page complexity and session length. Storage grows with your plan, and extra GB is available if you want to keep more.
How ROOKON investigates a drop-off
- 01
Detect
A step's completion rate moves away from its usual range, or a new problem appears on a critical path.
- 02
Correlate
Errors, failed requests, slow responses and rage or dead clicks at that step are gathered as evidence.
- 03
Explain
A recommendation answers what happened, why it matters, who is affected, what to change, the expected benefit and what happens if you approve.
- 04
Propose and measure
Approve a change, ship it your usual way and compare the step before and after the release.
Live demo 3 of 3
Prove the fix moved the number
A simulated impact report: conversion and bounce rate in equal windows before and after each change, with significance tests.
rookon · acme-cloud · impact of changes
Simulated dataEach change: 14 days before vs. 14 days after.
12 NovLive
Conversion rate · pages ROOKON changed
4.67% → 5.85%
+1.2 pp since the first ROOKON change
Bounce rate · same pages
41.4% → 38.5%
−2.9 pp since the first ROOKON change
ROOKON changes
2 improved of 3 measured
1 no significant change · 0 worse · 1 collecting
Conversion rate · /billing · 7-day rolling
| Time | conversion rate |
|---|---|
| 14 Sept | 3.1% |
| 15 Sept | 3.1% |
| 16 Sept | 2.7% |
| 17 Sept | 2.5% |
| 18 Sept | 2.5% |
| 19 Sept | 2.4% |
| 20 Sept | 2.5% |
| 21 Sept | 2.8% |
| 22 Sept | 2.7% |
| 23 Sept | 2.7% |
| 24 Sept | 2.9% |
| 25 Sept | 2.9% |
| 26 Sept | 2.9% |
| 27 Sept | 3.0% |
| 28 Sept | 2.9% |
| 29 Sept | 3.2% |
| 30 Sept | 3.5% |
| 1 Oct | 3.4% |
| 2 Oct | 3.6% |
| 3 Oct | 3.7% |
| 4 Oct | 3.5% |
| 5 Oct | 3.6% |
| 6 Oct | 3.5% |
| 7 Oct | 3.4% |
| 8 Oct | 3.7% |
| 9 Oct | 3.3% |
| 10 Oct | 3.1% |
| 11 Oct | 3.5% |
| 12 Oct | 3.8% |
| 13 Oct | 3.9% |
| 14 Oct | 3.7% |
| 15 Oct | 3.2% |
| 16 Oct | 3.6% |
| 17 Oct | 3.9% |
| 18 Oct | 3.9% |
| 19 Oct | 3.7% |
| 20 Oct | 3.7% |
| 21 Oct | 3.8% |
| 22 Oct | 4.4% |
| 23 Oct | 4.6% |
| 24 Oct | 4.5% |
| 25 Oct | 4.3% |
| 26 Oct | 4.3% |
| 27 Oct | 4.3% |
| 28 Oct | 4.4% |
| 29 Oct | 3.9% |
| 30 Oct | 3.7% |
| 31 Oct | 3.6% |
| 1 Nov | 3.7% |
| 2 Nov | 3.8% |
| 3 Nov | 3.7% |
| 4 Nov | 3.7% |
| 5 Nov | 3.8% |
| 6 Nov | 3.7% |
| 7 Nov | 3.7% |
| 8 Nov | 3.6% |
| 9 Nov | 3.7% |
| 10 Nov | 3.6% |
| 11 Nov | 3.7% |
| 12 Nov | 4.1% |
Shaded: baseline and after windows of the selected change.
It was better before
“Release v2.15.0: new onboarding checklist”
Bounce rate +2.6 pp (32.9% → 35.5%)
p < 0.001 (Holm-adjusted) · 14 days before vs. after
Either way, ROOKON won’t propose this exact change again. A different approach to the same page is still allowed.
Overlaps with “Release v2.15.0: new onboarding checklist”, so part of the difference may come from that change.
Observed changes between equal windows before and after each change, tested for significance (two-proportion z-test for rates, Welch’s test for durations, Holm-adjusted, p < 0.05). This shows association, not proof of cause: seasonality, campaigns and other releases can contribute, so overlapping changes are flagged.
Try: Open the first ROOKON change · Switch the window to 7 days and see the verdicts change
FAQ
Common questions
Is ROOKON an A/B testing tool?
No. ROOKON finds defects and friction that hurt conversion and proposes fixes. If you run experiments, it complements your testing tool by telling you what is worth testing, or what simply needs fixing.
Which conversions can I track?
Any step you can describe as a page view or an event: signup, email verification, first project created, plan upgrade, checkout completed. You define the funnel; ROOKON measures and investigates it.
How does ROOKON know a drop-off is a problem?
It compares each step with its own recent history and looks for concentrated evidence, for example a validation error that appears in many sessions that abandoned the form. Findings always show the evidence so you can judge for yourself.
Is it useful for products with modest traffic?
Yes. Many of the problems it looks for, such as a failing form submission, are visible in a small number of sessions, so you don't need the traffic required for statistically significant experiments.
Can I measure whether a fix worked?
Yes. Tag your releases and compare the step's completion before and after the change, alongside whether the original error or signal has stopped.
Stop losing signups to problems you can't see
Sign up and map your first funnel on day one.