Find what you want, when you want.
The Find is a location-based recommendation app designed to empower users to find new activities based on their interests and preferences. It provides personalized recommendations as if you are talking to a knowledgeable local of the area that knows exactly what you’re looking for.
The Find was created as part of a student project for Career Foundry to design a location-based recommendation app in which I served both as UX and UI Designer. Responsibility included concept creation, research, visual design, and testing.
Available location-based recommendation apps provide misleading and generalized recommendations which makes it difficult for users to find ideal activity recommendations.
8 weeks total
During my competitor research, I discovered there are many location-based recommendation apps and travel apps on the market. None of these options were equipped to provide highly personalized activity recommendations for the user.
They also have additional goals such as data collection, trip planning, and advertising. I saw a gap in the market to create a really smooth experience for the user to find activities they would enjoy.
After conducting interviews with 3 potential users, several commonalities began to emerge:
“I don’t want any decisions made in advance . . . show me all the options”
“I don’t like messy websites”
Search for recommendations
Provide personalized recommendations
Easily view basic information about recommendations
Provide a map
User Stories & User Flow Diagram
I created user stories, developed each into a user flow, and combined them into one user flow diagram.
To guide the design process, I combined what I learned about my users into user personas.
Exploratory Sketching & Low-Fidelity Wireframes
Utilizing the crazy 8 method, I did some exploratory sketching. After exploring several solutions, I created low-fidelity wire frames.
I used the low-fidelity wireframes to create a prototype in InVision for a usability test. I asked three potential users to complete the following tasks on the low-fidelity prototype:
The testing revealed four areas of improvement:
The A/B test was designed to find the preferred way for users to navigate to their personalized recommendation page from the explore page.
The A/B testing results showed that 90% of user preferred option 2. Many users indicated that the label & button in option 2 were cleaner looking, easier to understand, and more familiar.
After creating my mid-fidelity wireframes, I developed a style guide including typography, color, image styling & more.
Once I applied the style guide to my mid-fidelity screens, my high-fidelity screens were complete.
To identify what went well and areas of improvement for a second iteration, I completed a retrospective.
What went well:
What didn’t go well:
What can be improved:
Second Iteration Improvements