Trend Forecast Database
Overview
WGSN’s Trend Database was a 0-to-1 product transformation, shifting the company from editorial-led insight delivery to a structured, data-driven platform. I led UX strategy, system design, and UI direction across discovery, alignment, and delivery, operating as the design lead across Product, Editorial, Data, and Engineering.
My Role:
Design Goal
The design goal was simple “Help buyers, designers, decision makers and marketers find the right trends, at the right time, for the right consumer, in seconds, not hours”
The Challenge
This project started with a few slides and a single spreadsheet, with a target to launch by the end of Q1. However, there was no defined information architecture, product definition, UX flows, or interaction model in place. This wasn’t simply a feature design task. It was a strategic challenge. We were redefining what a “trend” is as a core product object, shaping both how it’s structured and how users interact with it.
Early validation through wireframes
Tested low-fidelity wireframes with users early in the process to validate key hypotheses around filtering behaviour, trend discovery, and decision making needs. Using quick, clickable prototypes, we ran guerrilla sessions with internal users and early client conversations to observe how people navigated the flows in real time.
Design Approach
Design as a Strategic Driver
Instead of waiting for requirements, I positioned design as the tool for alignment. Using low-fidelity concepts, I surfaced gaps in thinking, challenged assumptions, and built a shared understanding across teams. This reframed design from output to a decision making tool driving clarity, alignment, and faster, more confident product decisions.
Translate Ambiguity into Structure
The first critical move was transforming a raw spreadsheet into a scalable filter system, turning static data into a structured, usable product foundation. This decision defined the core product architecture, shaped the underlying data model, and ensured the experience could scale over time, ultimately anchoring how users navigate, explore, and interact with trends.
Define Core Design Principles
Prioritised scalability, recognising that trends are dynamic and constantly evolving, so the system needed to be modular and built for growth. Mobile-first thinking, focusing on core user needs before scaling the experience to desktop. Together, these principles acted as decision filters, guiding the product toward clarity, flexibility, and long-term sustainability.
Key User Flow Mapping (From Complexity to Clarity)
Why this mattered
At the start of the project, the biggest risk wasn’t visual design, it was unclear user journeys. We were dealing with multiple user types, including buyers, designers, and marketers, alongside a high level of data complexity across filters, scoring, and timelines. On top of that, there was no clearly defined end-to-end experience. Without clear flows, we risked designing disconnected screens, building inefficient features, and slowing down engineering delivery. To address this, I led the effort to map and define key user flows early, using them as the backbone of the experience.
My Design Process
Trend Database Forecast
Translated a spreadsheet into filter logic, created low fidelity wireframes, and built a clickable prototype. Key leadership move: Kept fidelity intentionally low to avoid premature visual debates and focus stakeholders on logic and structure.
Conducted guerrilla testing on filters with internal teams and early customer validation. Iterated rapidly based on feedback. Outcome: Reduced ambiguity early and built confidence across teams.
I led a focused design sprint developing UI concepts. Explored visual hierarchy, interaction patterns, and micro-interactions. Leadership decision: Prioritised direction over perfection and forced a decision within 1 week to maintain momentum.
Facilitated decision-making across stakeholders and consolidated best elements into a single direction. Challenge: Design was moving faster than other streams.
Designed key screens: Trend profile, filtering experience, feed view, and 'My Trends' workspace. Outcome: Partnered with Engineering across sprint cycles.
Impact (Metrics & Outcomes)
Speed & Efficiency
Reduced time to insight by ~70%. Enabled rapid iteration cycles (daily/weekly vs traditional phases).
AI & Data Impact
Improved Pulse AI response quality. Enabled structured trend indexing for future AI capabilities.
Team & Delivery Impact
Strong cross-functional alignment early. Maintained aggressive Q1 delivery timeline. Reduced rework through early decision-making.
Execution, Engineering Collaboration & Design QA
Embedding Design into Engineering Sprints
Rather than a traditional handoff, I worked within sprint cycles alongside engineers. Joined sprint planning to clarify UX logic and edge cases, and align on feasibility and trade-offs. Prioritised features based on user value, technical complexity, and delivery timelines. Leadership decision: Treat design and engineering as a shared problem-solving function, not sequential phases.
Real-Time Collaboration & Trade-Offs
During implementation, I partnered closely with engineers to resolve complex filter logic behaviours, data loading and performance constraints, responsive behaviour across breakpoints, and component reusability within the design system. Instead of pushing for pixel perfection, I focused on intent over exact visuals and preserving core UX principles (clarity, hierarchy, usability). This ensured we maintained product integrity without blocking delivery.
Design QA as a Continuous Process
I established Design QA as an ongoing practice, not a final step. Regular design reviews during sprint demos, side-by-side comparisons (design vs build), and early QA on partially built features. What I validated: interaction behaviour (filters, states, transitions), information hierarchy and readability, component consistency (spacing, typography, tokens), and edge cases and empty states.
Design QA Snapshots
Key Learnings
Speed is a Strategic Advantage
Rapid iteration created momentum. Prevented overthinking and stagnation.
Design Leads When Ambiguity is High
Design became the clearest articulation of the product. Teams aligned around artefacts, not opinions.
Design QA as a Continuous Process
Introduced need for clear decision logs and defined decision owners. Without this, context gets lost and rework increases.
Design Often Outpaces the Organisation
Design moved faster than Product/Engineering at times. Required active alignment and communication.