Redesigning Nowa, an anti-waste food app, with the goal of minimizing attrition, helping users save food automatically and consistently.
Intro
Nowa started as a university group project: a system to reduce household food waste using a mobile app paired with a physical tote bag embedded with RFID technology to scan products. I recently revisited the project and decided to update it. My redesign focused on the app’s user flow, information architecture, and interface, incorporating current AI developments to create a smoother experience. The goal was to simplify the system, reduce user attrition, and make the process more intuitive.
Role: information architecture, interaction design, visual design, hi-fi prototype
Tools: Figma, Miro
A semantic map to help you have a better idea of the areas of impact of the project
Problem
The whole redesign is based off of one premise: while food tracking apps exist, friction is the reason people don't use them. The original group project required users to manually log what they bought, manually update quantities as they cooked, and manually decide what to do with what was left. In practice, it's unlikely anyone will do this consistently. The more steps between opening the fridge and knowing what's inside it, the faster the habit breaks. The original design was asking people to solve a behavior problem (food waste) with more effortful behavior. Working around this contradiction is the challenge for this project.
Research
Six qualitative and analytical techniques used
User personas (×3)
Luca (disorganised professional), Virginia (control-focused student), Beatrice (social hoarder) — each with demographics, goals, frustrations and motivation maps.
Customer journey maps (×3)
Before / While / After / Habits phases mapped across touchpoints, actions, needs, frustrations, emotions and opportunities for each persona.
Empathy maps (×3)
Four quadrants per persona: emotions felt, thoughts, behaviours, and words said — cross-referencing inner state with observable actions.
Scenario posters (×2)
Storyboard-style narratives showing a day-in-the-life with and without Track&Share, alongside an interaction flow diagram illustrating exact system touchpoints.
WRAP survey analysis
Secondary data from 388 reference families across Italy (Nord 47.7%, Centro 26.3%, Sud 26%), segmenting avoidable waste per capita by household size.
Competitive analysis (Porter's 5 Forces + SWOT)
Mapping Carrefour, Esselunga, Lidl, Conad as incumbents; benchmarking their anti-waste initiatives (Myfoody, Banco Alimentare, IoT pilots) against the proposed solution.
Audience attitude split (survey)
49% attenti — aware and actively trying to reduce waste
26% indifferenti — do not believe waste causes harm
11% spreconi — completely disengaged
10% incuranti — aware but uninterested
4% incoerenti — preach against waste but practise it
Design implications
Primary target is the 49% attenti — already motivated, need better tools
Gamification reaches the 4% incoerenti by making change feel fun, not moral
Privacy controls critical: sharing is voluntary, not default
System must be passive — no extra cognitive load on busy users like Luca
Social mechanic (challenges, recipes) converts surplus into connection
Core insight: Food waste at the household level is rarely a values problem — it is an information and coordination problem. People do not know what they have, when it expires, or who around them could use the surplus. Track&Share addresses all three gaps in a single, low-friction kit that integrates into an already-existing shopping and cooking routine rather than replacing it.
Solution
The redesign was built around a single reframe: scan-first, manual-last. Instead of asking users to maintain their pantry through input, the system does the logging for them at two points in the routine where the effort is already near zero. At the grocery store, a RFID-embedded tote bag scans barcodes as items are placed inside it, automatically populating the app's fridge and pantry sections before the user even gets home. The following day, opening the fridge triggers an AI vision scan that recognizes items and adjusts quantities — asking for user input only when something is genuinely ambiguous, like an open container with an unclear amount left. From there the app suggests recipes based on what's available, flags items approaching expiry, and offers ways to share surplus food with friends and family. A grocery list section closes the loop, connecting what's running low to what needs to be bought. I rebuilt the full information architecture around this flow, then redesigned the UI from scratch, and took it to a hi-fi prototype.
Solution
The redesign was built around a single reframe: scan-first, manual-last. Instead of asking users to maintain their pantry through input, the system does the logging for them at two points in the routine where the effort is already near zero. At the grocery store, a RFID-embedded tote bag scans barcodes as items are placed inside it, automatically populating the app's fridge and pantry sections before the user even gets home. The following day, opening the fridge triggers an AI vision scan that recognizes items and adjusts quantities — asking for user input only when something is genuinely ambiguous, like an open container with an unclear amount left. From there the app suggests recipes based on what's available, flags items approaching expiry, and offers ways to share surplus food with friends and family. A grocery list section closes the loop, connecting what's running low to what needs to be bought. I rebuilt the full information architecture around this flow, then redesigned the UI from scratch, and took it to a hi-fi prototype.
MVP for university students. Barcode scanning app + fridge camera. Flatmate-only challenges. Basic privacy settings.
RFID coverage grows. Local server added. Friend network opens. Supermarket discount vouchers as prizes.
Family households added. RFID reader also in fridge. Neighbourhood and family group challenges. Supermarket partnerships formalised.
Full ecosystem integration with IoT, blockchain traceability, AI-calibrated supply chain, smart packaging across all major retailers.
Business model (B2B + B2C): Hardware kit sold to consumers; revenue from supermarket data partnerships and co-branded discount programmes. Competitive positioning is as a low-cost add-on to incumbent retailers' existing anti-waste programmes (Carrefour, Esselunga, Conad) rather than a competing platform.
Information Architecture of the app
Outcome
The result consists in a removal of the RFID tote bag. In its place, a phone number log in at register of self-checkout was implemented (this allows user to see all the new products added to the app without requiring further cognitive load or physical work like bagging items in the tote bag). At home, scanning becomes a predominant section of the app and happens through the implementation of AI vision. The manual layer exists but stays out of the way until it's actually needed. The recipe suggestions and sharing features only become useful once the inventory data is reliable, which is only possible if logging is effortless enough to be consistent. The scan-first approach is what makes the rest of the system viable.
Impact
The redesign reached hi-fi prototype stage, with full UX flows, visual design, and a working prototype. Compared to the original group project, the interaction model is structurally different. The friction that would have driven users away from the habit is removed at the point where it mattered most.
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