Fancy Bakery: A Simpler Mobile Ordering Experience

Researching how people choose and buy bakery products before designing the app.

Fancy Bakery is an academic Google UX Certificate exercise for a fictional French bakery in Montevideo, with online ordering, store pickup, and home delivery.

1. Foundational research
2. Synthesis and problem definition
3. Early Figma prototype
4. UI refinement and usability testing

The scope covered foundational research, problem definition, user journeys, and an early Figma prototype. It was not a launched product and was not measured in production.

This is an unfinished academic case that will continue through a new Figma iteration, AI-assisted exploration, and real usability testing.

Ordering ahead could reduce waiting, but the first question was what made bakery purchases difficult.

The initial hypothesis was that product choice and waiting for service created friction.

Before designing a solution, I researched how people bought bakery products, which questions came up, and what they valued when placing an order. The challenge was to shape an experience that helped people decide, allowed them to order in advance, and clearly communicated pickup or delivery.

Six remote interviews grounded the exercise in real bakery-buying habits.

Participants lived in Montevideo and bought bakery products at least once a week. The sample was intentionally broad for an academic exercise, although it was too small to support statistical conclusions.

• Waiting to be served at the store.
• Difficulty choosing among several options.
• Low visibility of healthier alternatives.
• A need for suggestions and combinations.
• Interest in adding drinks to an order.

From interview notes to two user archetypes and their journeys.

I organized the research in an empathy map and translated it into personas, user stories, and journey maps. The purpose was not to create artifacts for their own sake, but to connect what people said with specific product opportunities.

The journeys reinforced two critical moments: deciding what to buy and collecting the order without unnecessary waiting.

The first product decisions responded directly to choice and waiting friction.

• Organize the catalog through recognizable categories.
• Add suggestions and combinations to reduce indecision.
• Support scheduled pickup or home delivery.
• Show order status and the selected time.
• Treat reviews, coupons, and new products as supporting features, not the center of the flow.

A critical flow from discovery to pickup or delivery.

The main flow covered registration, product browsing, cart, pickup or delivery, and payment.

The problem definition focused on Carlos and Sandra, two archetypes representing different needs. Their stories informed paper sketches, low-fidelity wireframes, and an early Figma prototype.

The visual proposal remained at an early stage and does not represent my current UI level. That gap is part of why the case is explicitly presented as work in progress.

The study protocol was defined, but the validation loop was not completed.

I prepared an unmoderated usability study protocol for five remote participants, with planned sessions of 30 to 45 minutes. Tasks covered registration, product selection, cart, pickup scheduling, and payment.

The protocol exists, but I found no documented results proving that the study was completed. The case therefore makes no claims about measured improvements or validated outcomes.

Continue the case in Figma, use AI as support, and validate the critical journey.

• Rebuild the mid and high-fidelity experience in Figma.
• Define a clearer visual system, spacing, hierarchy, and reusable components.
• Use AI to explore alternatives, review consistency, test microcopy variants, and document decisions under human judgment.
• Run a real usability study on the complete critical journey.
• Iterate the prototype from observed evidence.

AI will support the work, not stand in for participants or turn untested assumptions into results.