Josué Cornejo — CRO & Growth PM

Case studies

Funnel CRO Mobile Healthcare · B2C

Scheduling funnel optimization — Blue Medical

Web + mobile · Guatemala & Costa Rica · GA4 · Hotjar · ~500–700 daily appointments

~50%

checkout conversion lift

25%

avg. time-to-book reduction

6,500+

daily unique sessions

Identified two high-impact drop-off points in a medical scheduling funnel: a mandatory login wall and a doctor/time-slot search that caused users to loop between options. Shipped two targeted interventions and measured results through GA4 and Hotjar.

Funnel audit

Blue Medical's scheduling platform served 6,500–8,000 daily unique sessions in Guatemala and Costa Rica. Using GA4 funnel analysis and Hotjar session recordings, I found only 19–23% of sessions reached the appointment type selection step. Primary blocker: users had to log in or register before seeing any availability — friction before value.

Hypothesis 1 — remove the login wall

Hypothesis: allowing guest access will push more users past the top-of-funnel drop-off. I wrote the spec, defined the success metric (% of sessions reaching appointment type selection), and coordinated with engineering. After launch, that step improved from 19–23% to 24–28% of sessions.

Hypothesis 2 — smart scheduling suggestions

Hotjar recordings showed a second problem: users looped between doctors and clinics searching for better time slots, extending booking time and causing mid-funnel abandonment. I proposed "smart suggestions": after specialty selection, show the 3 nearest available slots by location, plus the user's recent doctor if returning. Users could skip and browse manually. Result: avg. scheduling time dropped ~25% and overall conversion improved from ~3.2–4% to ~5.7% of sessions — with flat traffic, meaning pure conversion lift.

Outcome

Volume shifted from contact center and social media booking channels to self-service digital. Measurement was pre/post via GA4 — no A/B framework in place at the time. Isolating each intervention's contribution would have been the logical next step with a proper testing layer.

Experimentation Killed assumption Healthcare · B2C

Post-consultation NPS experiment — Blue Medical

Controlled experiment · ~500 patients · 4 weeks · Multiple clinics & specialties

~500

patients in experiment

actually needed a dr. follow-up

4 wks

to call the result and redirect

The CEO believed post-consultation NPS would improve if patients could speak with a doctor after their visit. I designed a controlled experiment to test this — and the data killed the assumption. We moved fast, read the results honestly, and redirected investment to the real problem.

The hypothesis (not mine)

The CEO hypothesized that patient NPS was lower than it could be because patients had unresolved questions for their doctor after the consultation. His proposed solution: offer every post-consultation patient a follow-up call with their doctor or contact center support.

Experiment design — no dev required

Rather than build and ship, I proposed a lightweight operational test first. We selected ~500 patients post-consultation across multiple clinics, doctors, and specialties. The test group was offered the doctor follow-up or contact center support. We tracked NPS scores and — critically — the reason for contact.

The result: the assumption was wrong

After 4 weeks, NPS did not improve consistently across clinics or specialties. Of the ~12% of patients who engaged, only 6% had a medical question for their doctor. The majority had operational issues: insurance problems, lab results not yet delivered, or medications not yet received — none of which were medical follow-up needs.

Decision: kill it, redirect investment

I presented findings to the CEO with a clear recommendation: do not build the doctor follow-up call feature. Redirect investment to insurance coordination, lab result delivery, and medication fulfillment workflows. The experiment cost zero in development and saved significant engineering time from being spent on the wrong solution.

SaaS Activation Discovery Subscription · B2C

Prescription calculator redesign — Bluemeds

Recurring medicine subscription · Guatemala · Hotjar · User interviews · Usability testing

2.1×

quotes generated per visitor

1.5%→3.2%

visitor-to-quote conversion

↓ rage

frustration signals on calculator

Bluemeds' savings calculator — its core acquisition tool — had high abandonment and visible frustration signals in Hotjar. I ran user interviews, built prototypes, ran usability tests, and shipped a redesign that matched patients' mental model of their medications. Conversion more than doubled.

Identifying the problem

Hotjar recordings on the Bluemeds calculator showed high rage-click rates and purposeless navigation — signs of confusion, not intent. The calculator required inputs for dosage, frequency, medication type, and delivery timing. Visitor-to-quote conversion sat at 1.5%. Most users dropped off without generating a quote.

Discovery — talking to users

I identified the highest-frustration sessions via Hotjar and GA4, then recruited those users for interviews. Key insight: patients don't think about medications in doses and frequencies — they think "I take this pill daily" and "it should arrive monthly." They also didn't understand the delivery date logic the system was producing, which created distrust.

Prototype & usability test

I designed prototypes that matched patients' mental model: monthly delivery as the default, smart defaults for common dosages, and a simplified savings comparison (monthly cost, not per-unit math). Ran usability tests with target patients before any development. The redesigned flow let users reach a quote in significantly fewer steps with less cognitive load.

Result

After shipping, visitor-to-quote conversion went from 1.5% to 3.2% — a 2.1× lift. Rage clicks dropped. The redesigned quote output also became a useful sales tool, shared directly by the sales team with prospective patients. Metrics approximate; NDA-compliant.

I move conversion metrics — and I show my work.