Growth project

I build this little vercel project to test a few things.

Project:
https://gmd-assessment.vercel.app/

Github:
https://github.com/micheleborn/gmd-assessment

The Context

This project was built as part of a Growth Marketing Designer assessment — a role that requires end-to-end ownership of landing pages, from design to deployment, with performance measured by conversion rate .

But the brief came with a twist:

  • No clear audience
  • No defined product positioning
  • No brand guidelines
  • No funnel stage

So instead of “executing,” I had to define the strategy from scratch.

The Goal

Design and build three landing page variants:

  • /control
  • /variant-1
  • /variant-2

Each with:

  • A different conversion hypothesis
  • A consistent structure for testing
  • Measurable behavioral tracking

Step 1 — Defining the Strategy (Before Design)

Instead of jumping into UI, I started with who this is for.

Target audience (assumed)

  • Mid-size DTC brands ($3M–$30M)
  • Paid acquisition fatigue (Meta, CPMs rising)
  • Distrust in attribution
  • Pressure from finance teams

Core emotional state

“I don’t trust my growth anymore.”

That became the foundation of everything.

Step 2 — Hypothesis Design

Each variant explores a different persuasion model:

1. Control — USP Clarity

  • Straightforward messaging
  • “What it does” framing
  • Minimal psychological layering

2. Variant 1 — Authority + Social Proof

  • Borrowed credibility
  • Proof-driven messaging
  • Reduces skepticism

3. Variant 2 — Risk Mitigation (Most interesting)

  • Focus on predictability + control
  • Addresses emotional anxiety directly
  • Designed for decision-makers under pressure

This is not UI variation.
This is psychological positioning testing.

Step 3 — UX & UI Decisions

UX Principles

  • Single goal: conversion
  • No distractions, no navigation complexity
  • Repeated CTA for scanning behavior
  • Clear hierarchy (problem → solution → action)

UI Decisions

  • Clean, high-contrast typography
  • Modular components reused across variants
  • Visual consistency to isolate messaging impact

Important:
I intentionally avoided “fancy design”
Because design should not contaminate the experiment

Step 4 — Development (Next.js + Vercel)

Built with:

  • Next.js + TypeScript
  • Component-based architecture
  • Route-based variant system

Routing strategy

  • /control
  • /variant-1
  • /variant-2

Each route:

  • Injects different content
  • Reuses the same components

This ensures:

  • Fast iteration
  • Clean experiment structure
  • Scalability for future tests

Deployment

  • Deployed via Vercel
  • Instant previews + production parity
  • CI/CD built-in

Step 5 — Analytics (PostHog)

Implemented full tracking:

Events

  • variant_viewed
  • signed_up
  • CTA clicks (with placement tracking)

Data captured

  • Variant name
  • CTA location (hero, footer, etc.)
  • Referral codes

Every interaction is measurable.

Also:

  • Ensured event capture before redirect
  • Structured for funnel analysis

Step 6 — Experiment Design

This is the part most people skip.

What I controlled:

  • Same layout across all variants
  • Same CTA wording
  • Same visual design

What I changed:

  • Messaging strategy only

That’s how you isolate causality

What AI Changed

This project took ~3 days.

Without AI, it would realistically take ~2 weeks.

AI helped with:

  • Validating messaging directions
  • Exploring persuasion angles
  • Speeding up iteration

But:
The strategy decisions were still human

Key Takeaways

  • Conversion is psychology first, design second
  • Most landing pages fail because they skip strategy
  • A/B testing is useless without clear hypotheses
  • Speed matters — but only if direction is correct

Final Thought

This wasn’t a design exercise.

  • It was:
  • Strategy
  • Psychology
  • Engineering
  • Measurement

All in one system.

I actually love the way this turned out – super happy with the results.