Our Conversion Research tool reads your GA4, heatmaps, form analytics, surveys, and calls, then hands you a list of ranked issues with potential fixes and the source evidence behind each, so you can verify before you test, build, or implement.
4 sample findings · scroll to see all columns
| Status | Result | Issue | URL/Page | Type | Ease | 5s | Traffic | Pain | +/- El | Bug Fix | Est. Impact | Score | PIE | Source | Action Bucket | Problem Category |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Backlog | — | Shipping cost only revealed on the final checkout step — 38% of carts abandon at the shipping step on mobile. | /checkout | Below fold | 7 | — | — | +$14,200 / mo | 4 | 8.4 | Heuristic | Test it | Friction | |||
Backlog | — | Hero headline doesn't name the product category — first-time visitors can't tell what you sell within the first 5 seconds. | / | Above fold | 9 | — | — | +$8,600 / mo | 4 | 8.1 | Buyer Panel | Test it | Comprehension | |||
Backlog | — | Two competing CTAs above the fold ('Start free trial' and 'Book a demo') split the click — primary CTA conversion is half what it should be. | / | Above fold | 8 | — | — | +$5,400 / mo | 3 | 7.6 | Heuristic | Test it | Focus | |||
Backlog | — | No SSL / security indicator near the credit card field on the payment form — Skeptic persona stalls here in 4 of 5 walkthroughs. | /checkout/payment | Above fold | 9 | — | — | +$3,100 / mo | 3 | 7.2 | Buyer Panel | Just do it | Trust |
Companies spend about $92 acquiring customers for every $1 they spend converting them (VWO, CRO statistics 2024). So when budget for conversion rate optimization finally arrives, it lands on top of a stack of tools that already produce more data than anyone has the time to read.
You probably have GA4. You probably have a heatmap tool. You're running A/B tests. None of them are telling you why people aren't converting, and the tests you do run come back inconclusive (only about 1 in 8 produces a real winner — Conversion Sciences).
Three voices from a recent r/CRO thread of growth leads asking the same question:
"We use Hotjar for heatmaps and session recordings, but most of the time you're just watching people bounce and still don't know why. You can watch 100 sessions and still be guessing."
"The biggest shift lately isn't more tests, it's clearer diagnosis. Heatmaps and recordings are useful, but they get noisy fast if you don't tie them to a specific question."
"There are so many CRO tools out there that will give you all the data you need. But if you don't have actionable insights, that information is worthless."
The data already exists, scattered across five tools. Nobody has time to turn it into a prioritised list of things worth testing. That's what Conversion Research does.
Every finding ships with a PIE score (potential, importance, ease: 1–10 each with a one-line rationale per axis), a PXL score, a Nielsen severity rating, and a revenue impact estimate calculated from your actual traffic and conversion rate. The list arrives sorted by what will actually move the number, not by what's loudest in the data.
Each finding is tagged with one of five action buckets (test, just_do_it, instrument, hypothesize, or investigate) so you know whether to run an A/B test, ship the obvious fix, or collect more data before you commit.
The common objection: "someone still has to do the work." True. But this list tells you exactly which work to do first, with the evidence behind it, so you're not debating priorities in a Slack thread.
| # | Finding | PIE |
|---|---|---|
| 1 | Shipping cost hidden until payment step /checkout Heatmap | 8.4 |
| 2 | Hero headline doesn't name the product category / Buyer Panel | 8.1 |
| 3 | Two competing CTAs split the click above the fold / Heuristic | 7.6 |
| 4 | No security badge near the credit card field /checkout/payment Buyer Panel | 7.2 |
Every finding is written in Problem / Context / Fix format. The problem names what's breaking. The context explains the mechanism. The fix is specific and testable: not "improve the hero" but "surface shipping cost on the cart page with an add-$X-for-free-shipping nudge."
Each finding links back to the exact evidence that triggered it: the GA4 report, heatmap clip, survey quote, form drop-off number, or page screenshot. So you can verify it before you build the test. No AI-generated claims floating without a source under them.
If the evidence doesn't hold up, you don't ship the test. Every claim has a source. Run a free single-page audit to see an example finding with its full evidence chain.
Mobile shoppers reach the final step before seeing shipping cost, then bounce when the fee appears.
Surface shipping cost on the cart page and product-page sticky bar with an 'add $X for free shipping' nudge.
"47 verbatim mentions of 'shipping cost' as the abandon reason." — Exit-intent survey, last 30 days
One SDK tag powers your click heatmaps, scroll depth, rage-click and dead-click detection, and per-field form analytics: time to complete, drop-off rate, and refill patterns on every input. All from the same script. See how heatmaps work →
The AI reads heatmap data and form analytics together with GA4, so a finding like "44% of users abandon the phone field" arrives with the corroborating session count and a suggested fix, not a raw number you have to cross-reference yourself.
For sites running paid traffic, the scent and message-match lens compares the promise on your ad sources to what the landing page delivers, surfacing the gaps that explain why ad-clickers bounce where organic visitors don't.
Phone field: 44% drop-off and 22s avg. Likely friction or anxiety. Consider making optional.
The AI analyst reads your GA4, heatmaps, surveys, and form drop-offs side by side in a 30-step pipeline. If the first read is inconclusive, it can request more data and re-analyse for up to four rounds. Like a junior analyst saying "wait, I need to see the device split before I'm sure."
Statistical-significance gates mean low-traffic pages don't muddy the analysis: 50 sessions minimum per page for GA4 findings, 100 conversions per region for geo findings. The AI knows when it doesn't have enough data and says so.
Each run also reads your private team knowledge base (past winning experiments, internal docs, custom hypotheses) so its recommendations get sharper over time instead of staying generic. On-site surveys collected via the SDK feed directly into this qualitative layer.
Promote any finding to a draft A/B test in one click. The hypothesis, target page, primary metric, and variant idea are pre-filled from the finding. No copy-pasting back and forth between a research doc and a test brief.
From there, the AI Test Coder writes the variant code. The variant goes through a seven-item pre-flight validity checklist (sample size, full weeks, instrumentation, external events, selection bias, flicker, cross-device) before anything ships site-wide.
When the test concludes, the result writes back to the originating finding and to the team knowledge base. The next run learns what's worked on your site, not just generic patterns.
If we surface shipping cost on the cart page with an 'add $X for free shipping' nudge, mobile checkout completion will increase because we remove the cost-surprise that currently drives 38% cart abandonment.
/cart
Mobile checkout rate
Cart page sticky footer: shipping cost display + "Add $12 for free shipping" progress bar when under threshold.
Ready to write the variant code from the hypothesis above. The code goes for review before anything ships site-wide.
The CRO Dashboard tracks test velocity, win rate, revenue impact, and revenue protected across the whole team. Whoever reports to the exec team has the numbers. No spreadsheet rebuilt every quarter.
Win/loss results feed back into the originating finding and the team knowledge base, so each run compounds with the last. Schedule re-runs weekly, monthly, or on demand. Each one picks up fresh GA4 data, heatmap sessions, and survey responses since the last one.
A finding only matters if you know what to do with it. Conversion Research connects to every step that takes a finding from "interesting" to "shipped and measured". Each run builds on the last.
Any finding becomes a draft experiment with the hypothesis, target page, and variant idea already filled in. No copy-pasting between a research doc and a test brief.
Once the test is drafted, the AI Test Coder writes the variant code. The variant gets reviewed before it goes live. Nothing ships site-wide by accident.
Before launch, each test gets a seven-item checklist: sample size, full weeks, instrumentation, external events, selection bias, flicker, cross-device. Auto-passed where the data confirms it, manually signed off where you have to make the call.
When a test concludes, the result writes back to the originating finding and to the team knowledge base. The next run reads what has worked on your site, not just generic patterns.
Test velocity, win rate, revenue impact, and revenue protected in one view. Whoever reports to the exec team has the numbers without rebuilding a spreadsheet each quarter.
A curated library of proven patterns ships with the product. Your team's past wins, internal docs, and hypotheses layer on top, private to your team, and feed every future run.
Schedule weekly, monthly, or on demand. Each run picks up fresh GA4 data, heatmap sessions, and survey responses since the last one. The diagnosis doesn't go stale.
Paste any URL from your site and get three to five prioritised findings on that page in about a minute. No signup, no install. The same analysis, scoped to one page.
The analysis adapts to what matters in your funnel. The lenses, scoring, and finding format are the same — what they surface depends on your business model.
DTC ecommerce stores
Mobile PDP friction, add-to-cart drop-off, checkout abandonment, shipping cost reveal — the issues that kill DTC conversion rates.
See how it works for DTC ecommerce →
SaaS self-serve products
Pricing page confusion, trial-to-paid drop-off, activation blockers, upgrade friction — the leaks that keep MRR growth flat.
See how it works for SaaS self-serve →
Lead generation sites
Form field drop-off, trust-signal gaps, landing page message-match for paid traffic — the barriers between a click and a booked call.
See how it works for lead generation →
GA4 tells you what's happening. Heatmaps and survey responses tell you why. They're read in one pass, so every finding ships with both the number and the customer quote behind it. No flipping between an analytics tab and a recordings tab trying to correlate them manually.
Most analytics tools tell you what happened. Not what to do about it. You get a prioritised list of issues with the next test for each one. Nothing to interpret, nothing to assemble.
Every finding maps to a hypothesis, target page, and variant idea. One click drafts the A/B test. The variant gets reviewed before it goes live. Nothing ships site-wide by accident.
Each finding links to the exact GA4 report, heatmap clip, survey quote, form-drop number, or page screenshot that triggered it. You can verify before you build the test. No claims without a source under them.
If the first read is inconclusive, the AI requests more data and re-analyses, up to four rounds. The same way a real analyst would say "hold on, let me pull the device split before I commit to this." That's the difference between a test you trust and one you ship and quietly regret.
Every run reads a curated library of test case studies and CRO frameworks. Your team's past wins, internal docs, and hypotheses layer on top, private to your team, and the AI uses them on every run.
Paste any page from your site. In about a minute you'll get three to five prioritised findings on that page. Same analysis as a full run, scoped to one page, no install and no signup.
Two integrations from the setup screen. The SDK powers heatmaps, on-site surveys, and form analytics from one script tag. No other code changes.
One click. The AI analyst pulls your data, runs the analysis, and emails you when the prioritised list is ready. Pick the top finding and promote it to a test.
Findings in Problem / Context / Fix format, sorted by estimated revenue impact
Two scores per finding: PIE (potential, importance, ease, 1-10 each with rationale) and PXL (the heavier-evidence ICE successor that demands proof of impact), plus a 0-4 Nielsen severity score so a five-minute CSS fix is never confused with a redesign
Each finding tagged with one of five action buckets so you know whether to test it, instrument it, hypothesize from it, investigate further, or just ship the obvious fix
Each finding tagged with the Eisenberg persona it most affects (Methodical, Competitive, Humanist, Spontaneous), so you can see whose journey breaks where
Source evidence under every finding: the GA4 report, heatmap clip, survey quote, form-drop number, or page screenshot that triggered it
A test-ready hypothesis per finding: the change to make, the page to make it on, the metric to measure, and the reason it should move
Sample size and days-to-significance estimate per test, calculated from your real conversion rate and traffic, so you know what's even worth running
A seven-item pre-flight validity checklist on every test (sample size, full weeks, instrumentation, external events, selection bias, flicker, cross-device)
A draft A/B test in one click. The variant code goes from finding to live test without leaving the page.
Re-runs on your cadence, weekly, monthly, or on demand, each one picking up new data since the last
Conversion friction shows up differently depending on your business model. See how Conversion Research applies to yours.
DTC Ecommerce
Cart abandonment, product page friction, checkout drop-off — and what to do about each.
See use caseSaaS Self-Serve
Trial-to-paid conversion, activation friction, and feature adoption without a sales team.
See use caseLead Generation
Form completions, landing page message-match, and improving lead hand-off quality.
See use caseQuestions worth answering before your first run.