Project №6 · Healthcare · Marketplace · NDA
Prescription Medication Sales & Delivery in a Marketplace
Confidential case study
Company, marketplace, partner, and competitor names are intentionally withheld. Selected implementation details are anonymized because the project is presented under NDA/confidentiality constraints.
A full-cycle healthcare product concept for turning prescription search, verification, checkout, delivery, and repeat medication management into one digital journey.
Product Manager · End-to-end ownershipCase type
Research · Validation · Launch design
The challenge
Prescription commerce is not a standard e-commerce transaction.
A user must find the exact medication, understand real availability, confirm the prescription, verify dosage and product details, select fulfillment, protect sensitive data, receive the order safely, and repeat the process when treatment continues.
The opportunity was to remove fragmentation and design one controlled journey: prescription → verification → search → comparison → checkout → delivery → repeat purchase.
Quantitative research
Start with behavior, not features.
I ran an exploratory online survey with 20 respondents who had experience buying medication and using digital services.
The sample was used for directional discovery and hypothesis generation—not as a statistically representative market study.
55%
Forgot to buy medication on time
50%
Had difficulty comparing prices
30%
Encountered out-of-stock medication
60%
Ranked complete availability as a key service factor
45%
Considered delivery speed an important factor
~25%
Had been offered an alternative medication
Family medication management
The buyer is often managing care for someone else.
~63%
of respondents had children under 18.
~42%
bought medication for older relatives.
This research signal expanded the concept from a one-person purchase flow into a medication-management system with family profiles, authorized recipients, prescription history, reminders, and fast repeat ordering.
Core audiences
Four recurring contexts shaped the product.
Chronic patients
Need reliable repeat access to the same prescribed medication without interrupting therapy.
Busy professionals
Want to avoid visiting multiple pharmacies and reduce the time spent completing a prescription purchase.
Parents & caregivers
Manage prescriptions and purchases for children, older relatives, or several family members.
Limited-mobility users
Need a safe and predictable way to receive medication without a physically difficult pharmacy visit.
Jobs-to-be-Done
Translate demographics into real jobs.
When a doctor prescribes medication
I want to verify the prescription and order online so I do not need to search across multiple pharmacies.
When a regular medication is running out
I want to see availability and delivery timing quickly so I can avoid interrupting treatment.
When I buy medication for a relative
I want to place the order remotely so I can manage care without an additional trip.
Qualitative research
A research plan built around past behavior.
I designed a 3–6 user interview program for people with previous online medication or delivery experience. The guide followed the real journey: context → search → prescription → selection → checkout → payment → waiting → receiving → problems → repeat order.
Market sizing
Model the opportunity before building it.
~80.5B
Modeled annual medication market for the broad target urban audience.
~16.6B
Modeled serviceable market based on online purchasing penetration.
~8.3B
Modeled obtainable scope constrained by product resources and launch assumptions.
Product strategy
From delivery feature to medication-management experience.
Competitive research showed a gap in connecting prescription verification, live availability, checkout, fulfillment, family management, and repeat purchase in one coherent flow.
Search by medication, active ingredient, and dosage
Real-time availability and price visibility
Prescription upload and verification
Delivery or pickup selection
Online payment and order tracking
Secure recipient verification
Alternative-medication approval flow
Family profiles and recipient management
Prescription and order history
Reminders and one-click repeat ordering
MVP → MLP → Scale
Do not automate an unvalidated behavior.
Concierge MVP
Validate demand
Registration, consent, search, price and availability, prescription upload, manual verification, checkout, payment, delivery/pickup, and feedback.
MLP
Build retention
Personal account, richer search, prescription history, order history, repeat order, family profiles, reminders, notifications, and support.
Target product
Automate & scale
Prescription-system integrations, pharmacy inventory integrations, OCR, automated verification, live comparison, tracking, loyalty, personalization, and AI assistance.
The Concierge MVP deliberately kept prescription verification manual. The first goal was to prove that users would trust the marketplace with the prescription journey before investing in expensive healthcare integrations and automation.
Secure handover
The last meter is part of the product.
Prescription delivery introduced a recipient-verification problem that does not exist in normal marketplace fulfillment. The concept therefore included a secure handover layer such as QR-based verification or another approved identity-control mechanism.
Alternative medications were also treated as a trust-sensitive flow: the system could explain and propose an alternative, but not silently replace the medication prescribed by the clinician.
Product analytics
Connect user behavior to business viability.
I used a learning dataset to work through cohort retention, ARPU, LTV, CAC, and ROI. These values are modeled analytical outputs, not claimed production results.
~8,992
Modeled LTV
~1,229
Average modeled ARPU
~2,857–4,357
Modeled CAC range across channels
106–215%
Modeled ROI range across channels
Experimentation
Use small tests to protect large investments.
I designed a HADI / A-B experiment model to validate expensive functionality with a limited audience before full development. In one modeled scenario, a small experiment represented roughly 5% of full development cost while protecting against a materially larger downside if the hypothesis was wrong.
Progressive rollout
10% → 30% → 50% → 70% → 99% → 100%
Monitor conversion, revenue behavior, errors, and user feedback at every stage before expanding exposure.
Business model
Transaction revenue first. Retention economics second.
The modeled monetization layer combined a transaction commission with an optional subscription layer for frequent users. Subscription benefits focused on delivery, reminders, recurring orders, and loyalty rather than replacing transaction revenue.
Year 1 modeled GMV
~16.55M
Based on modeled traffic × conversion × average order value.
3-year modeled GMV
~57.7M
A planning model for evaluating scale, not a production revenue claim.
Risk management
Healthcare trust depends on what happens when something goes wrong.
Medical & personal data
Encryption, role-based access, consent management, audit logs, and data minimization.
Prescription verification
Manual verification in the MVP, followed by controlled automation and system integration.
Wrong recipient
Identity controls, authorized recipients, and secure handover verification.
Fulfillment
Double-checks, packaging controls, temperature handling, tracking, and staff procedures.
Regulatory
Licensed partners, legal review, controlled product categories, and compliance checkpoints.
Trust & substitutions
No automatic replacement of prescribed medication; alternatives require explanation and user confirmation.
Go-to-market
Use existing intent before buying new traffic.
Because the product lived inside an existing marketplace ecosystem, the launch strategy emphasized internal discovery points: search, relevant category surfaces, contextual banners, account recommendations, push notifications, and loyalty.
The key principle was to surface prescription delivery when medication intent already existed instead of treating the feature as a generic awareness campaign.
Roadmap
Manual validation → retention → automation.
The roadmap intentionally postponed complex infrastructure until after the core user behavior was validated.
Concierge MVP
Validate prescription trust, ordering behavior, and operational feasibility.
MLP
Strengthen repeat use through account history, family profiles, reminders, and support.
Target product
Automate prescription and inventory flows, add live comparison, personalization, and AI support.
My contribution
Full-cycle product work.
Problem framing and product hypothesis
Quantitative research and survey design
Qualitative interview framework and CustDev plan
Audience segmentation, Persona, and JTBD
Customer journey and UX flow
Market sizing and competitive analysis
MVP, MLP, and target-product definition
Product analytics, cohort analysis, and retention
LTV, CAC, ROI, and monetization modeling
A/B and HADI experiment design
Progressive rollout strategy
Risk, compliance, and secure-delivery thinking
Go-to-market, roadmap, and scaling strategy
Case integrity
What this case does—and does not—claim.
This project represents product research, validation, strategy, launch design, analytics practice, and business modeling for a regulated marketplace category.
Survey findings are real research outputs from the project. Financial, cohort, LTV/CAC, ROI, experiment, and GMV figures are modeling exercises used to support product decisions. A production launch of a prescription-medication marketplace is not claimed as an outcome.
Validate trust before automating complexity.
Research first. Concierge MVP next. Scale only after evidence.