| 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 |
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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:
- Seller growth
- Platform revenue
- Marketplace liquidity
Despite strong seller intent, conversion into paid campaigns wasn't scaling as expected — and that's where this work began.

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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