Project Overview

Title Monetisation Systems – Scaling Promoted Closet Through Frictionless Payments & Conversion Optimization
My role Product Designer
Team Product Managers, UX Designer, Engineers (iOS, Android, Web, Backend), Data Science, Analytics, QA
Timeline Jan 2026 → Present
Project Type Optimization / Growth / Monetisation
Tools Used Figma, FigJam, JIRA, Analytics dashboards, Experimentation tools
Scope End-to-end checkout funnel, payment systems, onboarding experience, recurring billing flows

Index

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Heads up…

I joined this project in January 2026, initially collaborating with the senior designer leading Promoted Closet. By February, I had transitioned into the sole designer for the feature, owning end-to-end design across all new initiatives.

This case study reflect work I led within my team: From problem framing and research synthesis through to final shipped designs. My work spanned iOS, Android, and Web, with close collaboration across Product, Engineering and QA.

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Understanding the Product & Its Stakes

Promoted Closet is Poshmark's primary revenue driver — a tool that lets sellers pay to boost their listings and drive more sales.

Over 4 months, I redesigned the monetisation system across four funnel interventions spanning onboarding, payments, value communication, and conversion — resulting in a 3x higher seller reactivation rate and measurable improvements to trial activation and payment drop-off.

It sits at the intersection of three platform priorities:

Despite strong seller intent, conversion into paid campaigns wasn't scaling as expected — and that's where this work began.

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Understanding the Problem

Existing User Scenario

From a user’s perspective, starting a promotion is designed to be quick and lightweight:

Landing → Payment Setup → Trial Start → Promotion Goes Live

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While the flow appears simple, it sits at the intersection of user intent and business revenue—making even small points of friction highly impactful.

This is especially critical because:

This context became important as we started analyzing where and why users were dropping off.

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