Year
2024
Category
UX / UI Design
As Lead Product Designer, I:
Drove end-to-end UX from discovery to validation.
Collaborated daily with developers to ensure feasibility.
Built a modular design system scalable for multi-platform expansion.
Advocated for accessibility, ethical personalization, and user trust.
The Challenge
Checking the weather is easy. Preparing for it is not.
Professionals and travelers across Italy navigate multiple climates and social contexts in a single day, morning commute, client meeting in sunny Turin, dinner in a breezy weather.
Existing apps show temperature and icons, but fail to answer the real question:
“What should I wear and bring, given where I’ll be and what I’ll do?”
Business goal: Position WearCast as the first lifestyle-driven weather platform, turning daily forecasting into a personalized experience that increases retention and engagement.
UX goal: Deliver reliable, human-centered guidance with minimal friction, high clarity, and emotional resonance.
Discovery & Research
Competitive Audit
I analyzed 7 apps (Il Matteo, AccuWeather, Cladwell, Apple Weather, Google Weather, Stylebook, Yahoo Weather).
Findings:
Il Matteo had the largest install base (≈ 2.3 M) but; complaints about ads, outdated visuals, and irrelevant information.
No competitor connected forecast data + outfit logic + daily schedule.
Wardrobe apps had < 20% retention after day 3 due to manual item input fatigue.
User Interviews & Surveys
I conducted 10 semi-structured interviews (ages 23-42, split between commuters, students and workers) + a survey of 142 Italian users.
Key insights
76% check weather ≥ 2 times/day but still feel unprepared.
68% experience different micro-climates or dress codes in one day.
82% consider outfit choice a daily stressor (more for women + urban professionals).
91% abandon wardrobe apps within 3 days, setup too manual.
Trust in AI suggestions increases when the “why” is transparent.
Got The Users Covered
a sleek and animated design, developed by relying on data and insights from the research phase. Providing users with the necessary data in an interactive and seamless experience, showing alerts and overall metrics.
Animated
Alerts
User-Centered
Defining the Opportunity
From this data, I built the experience :
“Make users feel prepared, not by showing more data,
but by transforming data into context-aware action.”
Design principles:
Predictive clarity - Show only what matters for the next decision.
Minimal input, maximum value - Automate wardrobe tagging with AI.
Contextual adaptation - Adjust outfits across time, location, and event.
Transparent AI - Explain every recommendation in human language.
Visual delight - Subtle motion + color psychology to reinforce trust.
Real-Time Track of Weather
Real-Time track of weather forecast at a glance helps users to be ready all the time. the dynamic and interactive design built with having user needs and accessabilty in mind.
Weather Forecast
Real-Time Data
Dynamic Design
Proactive Smart Alerts
Users can set personalized alerts for their daily outfit suggestions. For complex itineraries with multiple locations, the system sends proactive reminders for outfit changes or items to pack, ensuring users are effortlessly prepared for their entire day.
Feature Design
Proactive UX
Personalization
Design Process
1. Information Architecture
I flattened Il Matteo’s 4-layer navigation to a 2-layer model: “Forecast” + “Wardrobe.”
Everything else, timeline, planner, settings, became secondary.
→ –45% navigation depth, measurable in click heatmaps during testing.
2. User Flows
Mapped core flows: “Check today,” “Plan ahead,” “Add clothes,” “Change location.”
Each flow was optimized for < 3 taps to outcome (time-to-insight ≈ 2.8 s).
3. Wireframes & Prototyping
Low-fidelity wireframes validated information hierarchy.
Iterated to mid-fidelity and interactive prototypes in Figma.
Ran two rounds of Maze testing (N = 15).
Average task success: 92%.
4. Visual Design & System
Built a component library with atomic tokens (color, type, spacing).
Created light/dark variants with contrast AA +.
Documented behaviors and interaction patterns for dev handoff.
5. Usability Testing
Conducted 3 usability sessions (post-prototype): N = 20 (Italian urban users).
Measured 5 metrics: time-to-first-insight, error rate, trust score, NPS, delight.
→ Average trust +48%, perceived effort –37%, overall NPS +36 points vs. Il Matteo control group.
Detailed Environmental Trends
To empower our most data-sensitive users, we created a trends feature to visualize historical and forecasted humidity, temperature, and pressure. This tool allows specialized users like mountaineers, outdoor photographers, and event planners to make critical decisions by analyzing deeper weather patterns beyond the standard forecast.
Data Visualization
Persona-Driven Design
Advanced Analytics
AI-Powered Weekly Outfit Planner
Our AI-powered Weekly Planner helps users prepare for the week ahead in seconds. By analyzing the complete seven-day weather forecast, the feature intelligently curates a full week of outfits from the user's wardrobe. It presents a complete plan that is weather-appropriate and ready to go, saving significant time and reducing daily decision-making. Users can quickly review and edit the suggestions for full control.
AI in UX
Predictive UX
Automation & Planning
Collaboration & Cross-Functionality
Partnered with data engineer to translate meteorological variables into UX logic (temperature × humidity × wind = comfort score).
Co-defined API structure for real-time outfit logic with developers.
Ran design critiques bi-weekly; used Loom walkthroughs for async feedback from remote stakeholders.
Facilitated mini design sprints to prioritize features for MVP.
Maintained transparent communication via Notion project docs and hand-off checklists in Zeroheight.
Outcomes
48% higher usability vs Il Matteo
62% more engagement
95% satisfaction
Scalable, accessible design system
