01
Read the funnel
Sessions, patients who started, patients who submitted, and the ones lost on the way, in one view. Completion stops being a single number you can't act on.
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
Read the funnel, find the step that leaks, fix it, and watch the number move.
02
Find the step that leaks
Every screen reports how many patients reached it and how many left there. The worst step is obvious instead of buried in an overall conversion rate.
03
Fix it and watch the rate move
Edit the step in the builder, A/B test it against the control, and track the exit rate day by day. The team that read the data ships the fix the 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.
Step completion tracking Completion rate per step, not just end-to-end. See where the exact screen patients are abandoning.
Drop-off waterfall Visualize the funnel as patients move through each step. Quantify the friction at each transition.
Eligibility analysis See which eligibility questions disqualify the most patients and whether that rate matches your program's intent.
A/B experiment scaffolding Run variants in parallel with your control. Split traffic, track outcomes, and roll out the winner with confidence.
Non-technical iteration No data team required. Ops and growth teams read the analytics, make changes in the builder, and publish without engineering.
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
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.
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