Problem
• Many people are indecisive about where they want to go for a night out.
• People have different motives for where they want to go or do for a night out.
• When people go out they also encounter bottlenecks at venues:
• Long lines at the bar
• Overcrowded venues or an under-crowded venue lacking energy.
"I hate having to sit in long lines," - Ben Meyers
"Not looking to go to overcrowded venues," Lisa Burnstien
Solution
LitCheck was our solution for users to find their goldilocks zone for a night out:
• It allows users to discover and find bars in their area that fit their preferences.
• It uses real time data allowing users to rate the litness of the venue they visit which will inform others of the atmosphere of that venue.
• Users no longer need to wait in line - they can order drinks from the app when the line at the bar is too long.
• Filters can be adjusted to fit the needs of an introvert or extrovert, wether you're looking for a night filled with people or are looking to avoid crowds.
UX Design
Night Life App Built From Scratch
Read Length: 6 Minutes
2021
Research
• Create user personas through user interviews.
• Affinity mapping, competitive analysis, and competitive matrix.
• Low fidelity prototype in order to conduct usability tests and user interviews.
• Develop information architecture.
User Personas
Affinity Mapping
When conducting ux research we first defined our assumptions and organized the questions we would ask potential users. This would allow us to gain incites on our users, develop personas and validate assumptions and questions about the features of the app that we would be including. This would effectively validate, change, or remove the features we plan on including in the app based on the data we collect.
We organized these questions and assumptions in the form of an Affinity Map and sorted them into four categories that served as the basis for different features of the app: decision making, rewards and decision making, litness and decision making, and target users.
We used the assumptions and questions we developed through the affinity map to conduct over 40 user interviews. That data was then used to create user personas.
Competitive Analysis
Next we conducted a Competitive Analysis to determine what features we had in common with similar apps and to see how we could create a competitive edge on the market based on the features we plan to implement.
These were current competitors: WhrzAt, Zomato, Barroulette, Yelp, Bump LLC,Google Maps, Nocto, Discotech Nightlife, Open Table, Four Square,Design My Night, Untapped, Beer Buddy.
• Green: Feature is somewhat complete or will be complete in the future
• Red: Feature is complete and in the app
• Yellow: Feature is not in the app
Competitive Matrix
It's important to note that these successful features account for user retention in Open Table. This indicates that the features we share with Open Table and offer to our users in the context of our app may also bolster our user retention. It is imperative that we explore the importance of social capabilities within the app when conducting user interviews in the future. As historical data has shown, even if lesser known competitors of this app share the same social features, those social features are a large reason for user retention in lesser known competitors.
Style Guide
Information Architecture
Moving forward we used information architecture to organize the flow of information. This process helped us focus on all of the features we wanted to implement based on the data we collected from our first round of user interviews. Establishing information architecture helped us prioritize which features of the app we would test first. Below are a few examples of information architecture and site maps.
User Flow
Sitemap
Usability Testing
The first feature we decided to test was the onboarding and sign-up/sign-in process. We decided to conduct a usability test and a user interview in order to not only test the usability but gather feedback by diving deeper into the reason behind each user decision. After collecting feedback from users on the signup and onboarding process we solved the pain points brought up by users. We got to work on fixing the pain points users had and designed a high fidelity prototype of the app so we could run through more scenarios with users in order to test its capabilities. Below are high fidelity examples of the onboarding and
sign-in/sign-up process.
Key Findings
1. Users typically skip the tutorial pages that familiarize the user with the features of the app. A way to make it more engaging is to make sure the images exemplifying exactly what the text is saying on the tutorial page and make the icons animated and more interactive.
2. The "remember me" login option needs to be active for users. Users strongly dislike having to log into the app every time they open it again. Having an option where a user stays signed in saves users time and effort.
3. Definitions were also unclear. There were many questions regarding the definitions to some of the words listed on the preferences page, including definitions for venue categories, but especially when it came to rating the litness for capacity. Some of the words used to describe capacity carry negative connotation which we want to avoid, where some were very unclear as to what they meant at all. These descriptor words must be changed.
4. The order of the tutorial screens should be reorganized. What is litness
(Page 2) should come first, followed by page 1, page 3, and page 4.
Adjustments
• Reorganize the order of tutorial pages.
• Replace images on tutorial pages
• Activate "remember me" function
• Define terms and replace words that have negative connotation
Edit Edit
Edit Edit
Edit Edit
High Fidelity Mockups
Conclusion
The research and UX strategy yielded significant improvements across key usability and engagement metrics. Grounded in user-centered design principles and iterative usability testing, the new solution delivered measurable impact:
• Task Success Rate: Increased from 62% to 93%, indicating streamlined task flows and clearer UI affordances.
• Time on Task: Reduced by 41% on average, highlighting enhanced navigation structure and minimized cognitive load.
• System Usability Scale (SUS): Score improved from 58 (marginal) to 88 (excellent), reflecting positive user sentiment and learnability.
• User Error Rate: Dropped by 36%, thanks to consistent iconography, improved feedback systems, and reduced ambiguity in controls.
• Engagement: Session duration increased by 27%, and return usage frequency rose by 44%, affirming increased value and satisfaction.
These improvements were achieved by aligning the product’s interaction patterns with established usability heuristics. The project not only met user needs but also supported business objectives, laying a scalable foundation for future product development and innovation.