Your form converts at 3%. Your paid traffic converts at half that. You're not sure which field to remove, whether the CTA is the problem, or why visitors scroll past your proof and leave without submitting. Conversion Research reads your GA4, heatmaps, form analytics, and surveys together — then tells you what to fix and what to test.
Most lead-gen teams already run GA4 for traffic and funnel data, a paid acquisition platform (Google Ads, Meta), and some combination of heatmaps and surveys. The tools exist. The problem is that each one answers a different question, and none of them synthesise across the others.
Conversion Research reads them together. GA4 surfaces the funnel drop-off and device gap. Heatmaps show the scroll pattern before the CTA. Form analytics surfaces the specific field where visitors exit. Surveys capture why. The platform combines all four into a ranked list of findings, each with a hypothesis and the source evidence attached.
See the full Conversion Research platform for how it works, how heatmaps and survey analysis feed into findings, and how findings connect to A/B Testing.
Tools in your lead-gen stack
Conversion Research
Ranked list of what to fix and test on your lead-gen pages
Form analytics gives you the answer to that question at the field level. Not "your form has a 68% abandonment rate" — that number is useless on its own. The useful number is "44% of visitors who start filling your form abandon at the company size field."
That's a finding. The finding has a hypothesis attached: remove or make optional the fields that drive the highest drop-off and measure whether completion rate increases without reducing lead quality.
Most lead-gen teams don't have field-level form analytics. They have a total form completion rate in GA4 and a gut feeling about which fields are too long. Form Analytics captures field interactions — time spent, hesitation, where users exit — and surfaces the specific friction points, not the aggregate.
% of visitors who abandon at each field
Removing the phone number and company size fields is the finding. The test writes itself.
For lead-gen sites, findings cluster across four zones. Each one links to the source data — no fabricated benchmarks, no generic heuristics.
Form completion friction
Field-level drop-off, required field overreach, phone number hesitation, and the form length versus lead-quality tradeoff — surfaced with evidence, not opinion.
CTA positioning and copy
Where visitors stall before the CTA, whether the button label matches what the visitor wants (a demo, a quote, a conversation), and whether the CTA is visible above the fold on mobile.
Trust and credibility gaps
Heatmap scroll patterns that show visitors looking for proof before they commit — testimonials, client logos, case study links — and whether that proof is positioned where the intent is.
Paid traffic message mismatch
The gap between what your ad promises and what the landing page delivers. A visitor who clicks an ad for 'free competitive analysis' and lands on a generic homepage bounces — not because the page is bad, but because the scent broke.
Company size field is the top drop-off point on the demo request form
Field analytics show abandonment spikes at the company size dropdown. Visitors who skip it and reach submit don't — they leave. The field is required; the form treats it as qualification, the visitor treats it as friction.
Paid traffic converts at half the rate of organic — same page
GA4 segments show a sharp paid/organic conversion gap. Survey responses from paid visitors cite mismatched expectations. The ad promises a specific outcome; the page opens with a generic value proposition.
Visitors scroll past the CTA to look for proof before submitting
Scroll maps show an unusual forward-scroll pattern — visitors pass the form, go deeper into the page, then don't return. Survey data: 'wanted to see more examples before booking.' The proof is there; it's positioned after the ask.
Yes. The platform doesn't run tests for you — it tells you which ones to run.
What it removes is the research phase: the hours spent cross-referencing GA4, heatmaps, and survey data to find the signal, write the hypothesis, and justify prioritising it over the eighteen other things on the list.
For most lead-gen page changes — CTA copy, form field removal, social proof repositioning, above-the-fold layout — the AI variant editor writes the JS/CSS from a plain-English description of the change. A developer is only needed for complex server-side or back-end variants. The gap between a finding and a running experiment is usually one click and a description.
What's automated
What's still yours
The trigger is usually a moment where increasing ad spend is no longer the answer — either the budget is capped, the CAC is unsustainable, or someone asks why the form conversion rate hasn't moved in a year.
When paid acquisition costs increase and the lead volume doesn't follow, the problem is usually on the landing page — not in the ad creative or bidding strategy.
Every new landing page resets the baseline. A research run in the first two weeks identifies friction the new design introduced before it costs you a quarter of leads.
When mobile traffic is high but mobile conversion is half the desktop rate, the answer is in the heatmaps and form analytics — not in more mobile traffic.
When sales says the leads are unqualified, the research question is whether the landing page is attracting the wrong visitors or not qualifying them before they submit.
Lead-gen page tests don't need complex variant code. Most changes — form field removal, CTA copy, social proof placement, headline — are CSS and copy. The AI variant editor handles those from a plain-English description. You review the diff, approve it, and the experiment starts collecting data immediately.