Know exactly where patients stop.
Step-level funnel analytics and A/B testing built for health intake, not generic web traffic.
Step completion tracking
See completion rates for every step in your flow, not just overall form completion. Know exactly which screen is losing patients.
Drop-off analysis
Identify the highest-friction points in the journey. Prioritize fixes that move the needle instead of guessing what to improve.
A/B testing
Test copy variations, step reordering, or routing logic changes against a control. Measure statistical impact before committing to a change.
Same-day iteration
The same team that reads the analytics can make changes in the builder. No engineering ticket required. Insight to published change, same day.
Why funnel analytics matter for health intake
A health intake flow can fail in many places: a question that confuses patients and causes them to quit, an eligibility criterion so strict that too many qualified patients are rejected, a checkout step that arrives too early before trust has been established. Without step-level data, you're guessing which problem to solve.
Generic web analytics (page views, bounce rate) don't tell you any of this. PathwAI's analytics are built specifically for the intake funnel, each step is tracked independently, so you see the exact shape of patient behavior through your flow.
The iteration loop
Analytics and A/B testing are most valuable when wired into a fast iteration cycle. In PathwAI, the team that reads the data can ship the fix, same day.
Analytics designed for intake funnels.
Not page views and bounce rates. Step-by-step patient behavior data that tells you what to fix next.
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Step completion tracking
Completion rate per step, not just end-to-end. See where the exact screen patients are abandoning.
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Drop-off waterfall
Visualize the funnel as patients move through each step. Quantify the friction at each transition.
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Eligibility analysis
See which eligibility questions disqualify the most patients and whether that rate matches your program's intent.
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A/B experiment scaffolding
Run variants in parallel with your control. Split traffic, track outcomes, and roll out the winner with confidence.
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Non-technical iteration
No data team required. Ops and growth teams read the analytics, make changes in the builder, and publish without engineering.
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Shareable reports
Keep operators, clinicians, and growth teams aligned on what's working and what's the next iteration priority.
Questions you can actually answer.
Step-level analytics answer the questions that matter for health intake performance.
- Where's the biggest drop?
- Which step has the highest abandonment rate, so you know where to start.
- Is eligibility too strict?
- Which eligibility question disqualifies the most patients, and whether that matches your clinical intent or is a miscalibration.
- Does progress context help?
- Does adding a progress indicator before the intake section improve completion, and by how much.
- Which CTA converts better?
- Which version of the checkout CTA results in more completed checkouts when tested against each other.
- Who abandons at checkout?
- What percentage of patients reach the final step but don't complete checkout, and what's happening in that last drop.
Common questions
Do I need a data team to use the analytics?
No. The analytics dashboard is built for non-technical operators. You see step completion and drop-off in plain terms, no SQL or data analysis required.
How does A/B testing work?
You set up a variant of your flow with specific changes, define the traffic split, and PathwAI runs both versions in parallel. You see results for both and choose when to roll out the winner.
Can I run A/B tests without engineering?
Yes. The same team that reads the analytics can create variants and configure tests in the builder. Engineering is not required.
What events does PathwAI track?
Step starts, step completions, eligibility outcomes, checkout initiations, and final form submissions, for every patient that enters the flow.
See what your intake funnel is actually doing.
We'll show you how step-level analytics and A/B testing work for a flow similar to yours, and what the first meaningful improvement usually looks like after launch.