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Product Management · Mentored Project · 2025–2026

From deadstock problem to MVP

Atacama desert landscape

ROLE

Product Manager

DURATION

July 2025 – January 2026

FORMAT

24 mentored sessions · 252 hrs total

OUTCOME

Validated low-fidelity MVP

The problem

Fashion brands generate significant deadstock - unsold inventory and leftover materials that typically end up in landfill or are destroyed. Large brands lack a dedicated system to track this inventory in a way that connects it to upcycling opportunities. The question I set out to answer: could a purpose-built CRM give brands visibility over their deadstock and help them manage relationships with upcycling partners, manufacturers, and internal teams - turning a waste problem into a traceable, manageable process?

Discovery & research

Before defining any solution, I invested in understanding the problem deeply. I conducted structured user interviews using the Jobs-to-be-Done framework - focusing not on what users do, but on what outcome they are trying to achieve and what is getting in the way. I built 2–3 user personas based on roles within large fashion brands who would interact with the system, and synthesised interview findings into thematic clusters to arrive at a clear, validated problem statement.

FRAMEWORKS

Jobs-to-be-Done (JTBD)

ARTEFACTS

User personas · Interview script · Thematic insight clusters · Problem statement

Defining the solution

With a validated problem statement in hand, I moved into solution design. I defined the product vision using a Product Vision Board - aligning the target user, their needs, the product's key features, business goals, and competitive landscape on a single canvas. I built a Competitive Matrix to map existing solutions against key dimensions - features, pricing, target users, and gaps - to understand where the market was underserved and where the product could differentiate. To define what to build first, I used a Value vs. Effort Matrix to prioritise features, then translated priorities into a User Story Map and a structured Product Backlog. Hypotheses were documented in a Hypothesis Table - each mapped to an assumption being tested, a test method, and success criteria.

FRAMEWORKS

Product Vision Board · Value Proposition Canvas · Competitive Matrix · Value vs. Effort Matrix · Hypothesis Table

ARTEFACTS

Product Vision Board · Value Proposition Canvas · Competitive Matrix · Market Research · MVP Feature Definition · Prioritised Product Backlog · User Story Map

Prototype & testing

I translated the MVP scope into a low-fidelity interactive prototype using Google AI Studio, covering the core user flows. Rather than building for completeness, I built for testability - focusing on the flows most critical to validating the core hypothesis. I developed a User Testing Protocol, ran sessions with 5–7 users, documented findings in a User Testing Results Summary, and iterated on the design based on what I learned.

FRAMEWORKS

Low-fidelity Prototyping · Usability Testing

ARTEFACTS

Low-fidelity prototype · User Testing Protocol · User Testing Results Summary

Launch & metrics

With a validated prototype, I shifted focus to launch readiness and measurement. I defined the North Star metric as the volume of deadstock inventory successfully matched to upcycling partners per month - the single number that captures whether the product is delivering its core value. Not just that brands are tracking deadstock, but that material is actually moving through upcycling channels. I developed a GTM plan covering market segmentation, positioning, channel strategy, and the steps needed to move from prototype to first users.

FRAMEWORKS

GTM Planning · North Star Metric

ARTEFACTS

GTM plan · North Star metric definition

What I learned

This project was my first end-to-end experience as a Product Manager - from an idea on paper to a validated, launch-ready MVP. What I valued most was the breadth of the role: understanding the market, shaping the product direction, defining how it reaches its first users, justifying every decision, and testing every assumption. This is product management at its most complete - driving a business idea forward, not just shipping features - and that scope is what I find genuinely compelling about it.

Jobs-to-be-Done (JTBD) - for structuring user interviews around outcomes, not features

Product Vision Board - for aligning product direction, user needs, and business goals on a single canvas

Value Proposition Canvas - for mapping customer pains, gains, and the product's fit

Competitive Matrix - for mapping existing solutions against key dimensions to identify market gaps

Value vs. Effort Matrix - for prioritising features based on impact and feasibility

Hypothesis Table - for documenting assumptions, test methods, and success criteria

User Story Mapping - for translating priorities into a structured backlog

Low-fidelity Prototyping - for testing ideas with users before committing to build

Usability Testing - for validating flows with real users and iterating based on findings

GTM Planning - for defining market segmentation, positioning, channel strategy, and launch sequencing