Your pricing page bounces a lot of visitors. Your signup form drops users at a specific field. Your trial cohorts churn before they activate. You can see each of these in a different tool — but none of those tools tell you which one to fix first, or what the test should be. Conversion Research reads them together.
Most SaaS teams have GA4 for web analytics, a heatmap tool for visual behaviour, a survey platform for qualitative signal, and Mixpanel or Amplitude for product analytics once users are logged in. None of these tools cross the anonymous-to-authenticated boundary — and none of them tell you which friction point to fix first.
Conversion Research covers the anonymous pre-signup path where your product analytics can't reach. It connects to GA4, your heatmap data, your survey responses, and your form analytics — then runs a structured analysis across all of them to find where visitors stall on their way to becoming trial users.
The output is a ranked list of findings, each with a hypothesis and the source data one click away. Not a dashboard to monitor — a backlog to work through. See the full Conversion Research platform, how heatmaps and survey analysis feed into findings, and how findings connect to A/B Testing.
Tools in your SaaS stack
Conversion Research
Ranked findings across the full self-serve funnel
Self-serve SaaS has a deceptively short conversion path: visitor to trial to activated to paid. The problem is that each stage breaks for a different reason, and the data you need to diagnose each one sits in a different tool.
Visitor → Pricing page: Value prop and social proof
GA4 shows who landed and bounced. Heatmaps show what they read and where they stopped. Surveys capture why.
Pricing → Signup: Plan confusion and trial friction
Form analytics surfaces field-level drop-off. Heatmaps show the toggle no one finds. GA4 shows device-specific conversion gaps.
Signup → Activated: Onboarding flow and time-to-value
GA4 cohort analysis identifies the activation cliff. Survey data from churned trials explains what blocked them.
Activated → Paid: Upgrade prompt timing and feature discovery
Where do trial users stall before they see the paid feature? What's the last screen they hit before they cancel?
Self-serve conversion stages
Friction categories: Value prop clarity, social proof density
Friction categories: Plan confusion, free trial availability
Friction categories: Onboarding flow, time-to-value
Friction categories: Feature discovery, upgrade prompt timing
For SaaS self-serve, findings consistently surface across four categories. Each finding links to the data behind it — not a heuristic, not a guess.
Pricing page friction
Annual/monthly toggle visibility, plan comparison clarity, social proof positioning, and the gap between what the visitor wants to know and what the page answers.
Signup flow drop-off
Field-level form abandonment, email validation friction, account creation steps, and the moment between 'I'm interested' and 'I'm signed up' where intent dies.
Activation gaps
Where trial users stall before they see value — onboarding checklist completion, first-run UX, and the cohort drop-off between signup day and day 3.
Ad-to-page message match
What the ad promises versus what the landing page delivers. The scent-and-message-match lens catches the mismatches that inflate bounce rate on paid campaigns.
Annual/monthly toggle buried — most visitors never see annual pricing
Scroll maps show the plan comparison table is below the fold on mobile. Visitors who reach the annual tab convert at a higher rate; most don't find it.
Work email validation is creating unnecessary friction
Form drop-off spikes at the email field. Survey responses mention free email blocks as unexpected — the friction signal and the reason are in different tools, now in one finding.
Trial users who don't complete the onboarding checklist in session 1 churn
GA4 cohort analysis shows a sharp activation cliff at day 3. Survey data from churned trials cites 'didn't know where to start.' The checklist exists — it's not surfaced prominently enough.
It's the right question to ask before you build a test backlog around AI output.
Every finding in Conversion Research links to the source data behind it: the GA4 report, the heatmap session, the survey quote, the form drop-off number. The AI doesn't invent the signal — it reads the data your tools are already generating and synthesises it into a structured finding with a hypothesis attached.
If you open a finding and the evidence doesn't hold up, you don't run the test. Nothing is hidden. The source is always one click away.
The compounding part:
You can add your own past winning experiments, internal docs, and custom hypotheses to the team knowledge base. The AI reads that library on every run. Recommendations get sharper as your history grows — it won't suggest testing something you already tested and killed.
The trigger is usually a moment where the team needs to justify its test backlog — or where the conversion gap between what should be happening and what is happening becomes hard to ignore.
When the top-of-funnel is working but the activation and expansion numbers don't move, the problem is downstream of acquisition — and it needs diagnosis, not more traffic.
Redesigns reset baselines. A research run before and after tells you whether the new design moved the right metrics or just changed the aesthetics.
When a competitor undercuts your plan structure, the urgency to understand your own pricing page conversion path goes up fast.
A ranked, evidence-backed finding list with hypotheses attached is a different conversation than a heuristic audit. One is a plan. The other is a guess dressed up in a PDF.
Conversion Research connects directly to A/B Testing. Every finding already contains the hypothesis — the observed friction, the proposed change, the predicted outcome. One click turns it into a draft experiment. The AI variant editor writes the JS/CSS from a plain-English description of the change.