2022 - 23
Barcelona Technology School
UX, UI, Branding, Business + Marketing Strategy
Figma, FigJam, Miro, Canva
'Recs' is the personalized assistant that finds places to eat and drink based on the users interests and preferences. Recs is for individuals who want to experience new places with friends and family or recommend them.
Main user pain points: time-consuming process, overwhelming numbers of results, inaccurate and untrustworthy information, and no one solution that cohesively meets all their needs.
By harnessing the power of AI,'recs' can efficiently filter search results against users' individual preferences and requirements. Streamlining the overall searching and decision-making process, by presenting users with only the most relevant, and reliable, options that seamlessly meet their needs.
UX Research, UI Design, UX - Business - Marketing
During the early stages of 'recs,' we conducted extensive qualitative research using methods such as: surveys, interviews & shadowing analyzing user demographics, identifying pain points and needs, and observing user behavior to gather insights. With these, we received valuable feedback that shaped the development of Recs.
All participants used a combination of multiple apps to find and book a restaurant.
Users value the ability to receive recommendations from people they know and trust
Major products in the market lack desirable features and feel impersonal, outdated and untrustworthy
A compare feature with relevant information is paramount when searching for restaurants or bars.
Too much information on screen makes users feel overwhelmed, they'd like a more curated experience
Irrelevant suggestions feel impersonal and clutter the UI, filters are highly used tools when searching
To identify competitor's positioning in the market to define the market strategy for Recs features and information structure.
In this perceptual map, we plotted the competitors along with 'recs' based on two key
dimensions: ease of searching and saving places and the social aspect of recommendation of places from friends.
After conducting user interviews, all the participants responses were synthesized to identity themes, opportunities, and features that Recs as a product could focus and improve upon.
Based on our research findings, we created a user persona to better understand our target audience. A persona helps us empathize with users, align the team around their needs, and make informed design decisions.
To kick-off the design process, quick sketches helped us get ideas on paper to establish which elements were necessary for each screen. A low fidelity prototype was then created for initial user testing.
Rough sketches were done to get my initial thoughts on paper and brainstorm new ideas for specific UI elements.
We prioritized features that provide the most value and address key pain points. Our MVP includes AI-powered search (voice or text), AI chat for refining searches, a swipe feature for selection, and expanded result details. We then test with users to identify what works.
We started with low-fidelity prototypes to explore ideas and gather early feedback, then refined them into high-fidelity designs for more detailed testing. This iterative process helped us validate usability and ensure a polished final experience.
We started with a low-fi prototype to map out key user flows and gather early feedback, then refined it into a mid-fi version with clearer layouts and interactions for usability testing. Based on insights from testing, we iterated on the design, adding visuals, micro-interactions, and polished details to create a high-fi prototype ready for development.
We focused firstly on developing the main flow, which we defined as three core sections: search, results, and decide.
We tested user interactions through assigned tasks to evaluate usability and identify potential challenges. Simultaneously, A/B testing was conducted by presenting participants with two variations of the product. This allowed us to compare performance metrics, such as task completion rates and engagement, while gathering valuable feedback. The combined insights from usability and A/B testing informed data-driven decisions to optimize the design and enhance the user experience.
AI-powered search
accessible main screen to launch a search including all your personal preferences
swipe feature for search results
intuitive interface to choose whether the place is a fit or not
profile with saving feature
possibility to save places and curate collections, follow friends
discover: recommendations search
overview of new recommendations and places to discover and save them
sharing feature
possibility to share single places, a comparison or a collection with friends
on-boarding
an easy and understandable tutorial explains all functionalities to optimise the overall use
We created a design system to support the users to have a seamless experience with the product by ensuring consistency, familiarity, and an appealing and playful yet simple design. It includes reusable components, intuitive navigation patterns, and clear visual hierarchies, all tailored to help users quickly discover and decide on options.
A revolutionary platform designed to simplify the process of finding places to eat and drink.
