🚀 Building Smart: Creating an MVP Using the Lean Method

When it comes to launching a new product, the smartest path often isn’t building everything—it’s building the right things first. That’s exactly the approach I took when I helped launch Elligo’s Study Marketplace—a platform designed to simplify and accelerate clinical research by matching studies with sponsors. This post walks through how I used the Lean Startup Methodology to build a Minimum Viable Product (MVP), how I prioritized features for the Proof of Concept (POC), and how I scaled to General Availability (GA).

🎯 Step 1: Understanding the Problem Deeply

Clinical trials often face major hurdles: poor study-provider matching, complex onboarding, and low visibility for providers to discover studies that match their patient population.

We identified a gap: research sites and physicians needed an easier way to find relevant studies, and sponsors/CROs needed a way to reach those providers faster and more effectively.

So, the goal became clear: Build a marketplace that connects study sponsors with physicians and healthcare organizations—with minimal friction.

🧪 Step 2: Defining the MVP – What’s Just Enough?

The Lean approach taught us to validate the riskiest assumptions first with a POC.

We asked ourselves:

1. Will providers actually use a platform to find and apply to studies?
2. Can we match studies based on specialty, location, and capabilities with minimal input?
3. Is there value in showing real-time study opportunities in a B2B setting?

Instead of building a full-fledged marketplace with chat, integrations, dashboards, and analytics, we focused on a lean MVP with just 3 core features:

🔍 MVP Features (for POC)

Features & Why It Made the Cut

1. Provider Sign-Up & Profile Creation - Needed to test if sites/providers were willing to engage. Captured NPI, specialty, capabilities.
2. Study Discovery Feed Core of the product - Validated if providers would browse studies and click to learn more.
3. Basic Manual Matching Algorithm - Even if manual, we needed a way to connect the right study to the right provider to test demand.
We intentionally excluded things like real-time notifications, advanced analytics, or onboarding automation. Those came later—only after the core assumptions were validated.

🧠 Learnings from the POC

1. Providers did engage, especially those who lacked traditional access to sponsors.
2. We saw real interest from sponsors once we could demonstrate site traffic and engagement.
3. Manual processes (like curated matches by our team) worked fine for early learning but highlighted where tech could scale.

📈 Scaling to GA: What We Built Next

After validating product-market fit, we moved toward General Availability with:

1. Automated Machine Learning Engine and providing recommended studies
2. Study Application Flow with workflow statuses (Applied, In Progress, Selected, Pre-Study Visit, To-dos)
3. Admin Portal for Elligo teams to track activity, engagement, documents, and study interest
4. Onboarding Automation to streamline profile setup and compliance
5. Reporting Dashboard to track study performance and provider participation

We used real engagement data from the MVP to prioritize what to build, keeping feedback loops short and grounded in user behavior.

🧩 Key Takeaways

1. Start Small, but Smart – Your MVP should validate the most critical assumption, not try to solve every problem at once.
2. Learn Before You Scale – Manual processes are okay in the MVP stage if they help you learn faster.
3. Let Data Drive Development – Every feature beyond MVP was prioritized based on user actions, not opinions.
4. Keep Iterating – Lean is not a one-time event; it's a continuous loop of build → measure → learn.

💬 Final Thoughts

The Lean Method helped us deliver real value faster without wasting cycles on things users didn’t need yet. Launching Elligo’s Study Marketplace from zero to production wasn’t about being perfect—it was about being useful at every stage.
If you're building something new, don’t ask “What can we build?”
Ask: “What’s the least we can build to learn the most?”