Coffee discovery for the specialty lover.
Summary
Firstbloom was a personal project that evolved into an official business. The MVP was a directory of specialty coffees that fed a mobile application intended to help serious coffee-lovers get personalized recommendations and discover new coffees to enjoy.
My Role
I designed the UX and UI for the mobile app and collaborated with my technical cofounder on product strategy and building the data entry platform to capture and manage complex coffee information.
Outcome
The public-facing mobile app was officially launched over two years ago. While Firstbloom has now moved away from the app towards a more guided, educational coffee experience, the MVP allowed the team to test multiple hypotheses about what coffee-lovers want from their coffee experience.
Process
Key User Types
Based on exploratory user interviews, spending time in a community of coffee professionals and very rough initial concepts that we tested in the community, we developed an understanding of key user types for the app and for the data entry platform that populates it.
Mobile App
Coffee lovers - Newcomers
Once they find a coffee they like, they try other sure options (ex. coffee from same neighbourhood cafe, same roaster or with same notes). They generally aren't as inclined to experiment with unusual products because they want to avoid spending money on coffees they aren't sure they'll enjoy.
Coffee lovers - Seasoned Hobbyists
They often rely on origin information or the specifics of the bean (variety, fruit processing, etc.) to try a coffee.
They recognize the characteristics of coffee and generally know what to expect and what they are likely to like. They are therefore more conducive to experimenting or trying new products.
Data Platform
Internal App Team
The internal team’s goal is to capture data, validate ambiguous data by verifying multiple sources, and fix errors or missing information in the database. They must manage several types of data:
- Data linked to roasters (name, location, etc.)
- Data linked to origins (region, name of producer, farm, etc.)
- Data linked to the particularities of the grain (variety, processing, altitude)
Roasters & Importers
Although they are the primary source of coffee information, the data obtained or presented by roasters or importers is often vague, lacking or incorrect. Roasters especially face several obstacles when it comes to data:
- They often cannot communicate with the source (producers) in their native language
- They lack geographic knowledge of producing countries
- They may be unaware of misspellings or common errors in names and places
Project Evolution
Preliminary Flows & Wireframes
To help us determine which screens would be most useful to prototype in higher-fidelity for testing and audience validation, we mapped out an initial information hierarchy and flow.
Then we planned the interfaces and screen sequence in more detail with low-fidelity wireframes.
Validating concepts with clickable prototypes
We chose to perform user tests with high-fidelity mockups to test several hypotheses in terms of layout, relevance of the information, ease of use and visual direction. Based on customer feedback, our initial concepts were validated in terms of what felt like relevant information to potential users. We made slight UI refinements based on the feedback.
Building the data platform
Without disclosing too many details about our custom data platform, the most complex piece of this puzzle was determining the hierarchy of various data points and the relationships between them and how to display these both internally and in the app.
We played around to find the right primary data point (the container in which other data would “live”) and chose to mimic the mental model of how our end-users browsed for coffee in the wild: with the roaster as the main anchor piece of information.
Once we had an idea of how the information should fit together, we used the front-end framework Clarity UI to quickly spin up a data entry web app.
Final Screens Sample
Key Design Decisions
We kept our design process and decisions for the data platform quite lean and focused primarily on the UX for the consumer app. Here are a few of the functionalities we decided to prioritize:
Many ways to explore
In order to maximize the discovery of new coffees, we chose to offer users three avenues to find products that interests them:
A precise search tool (by coffee or roaster name)
An overview of recent activities (coffees added to app, recently rated etc.)
Personalized weekly recommendations
Crowdsourced tasting notes
Taste is subjective - and the tasting notes indicated by roasters don’t always reflect the palate of your average coffee enthusiast. We chose to tally all tasting notes submitted by users for a particular coffee, to create an average taste “portrait” based on various points of view.
App onboarding
As a result of user feedback, we realized that an important step was missing on the first visit to the application. The onboarding aims to encourage those who are hesitating to explore to test the waters with more tailored recommendations.
