NBRS
Mobile app that bypasses architectural barriers and builds tight neighborly relations.
About project
The project was developed as part of postgraduate studies in User Experience & Product Design at SWPS University, which took place from October 2022 to June 2023. As a team, we collaboratively led the design process, starting from defining the problem and progressing through the first full iteration.
Client
SWPS University
Services
UX/UI Design
Year
2022-2023
Industry
Socialmedia
Problem to solve
For our design project, we chose to focus on neighborhood relations. Currently, especially in large cities, these relations are limited or nonexistent. Social media platforms are filling the gap. On the other hand, suburbanization (the trend of moving to suburban areas) leads to the exclusion of many individuals from socio-cultural life. We decided to explore this topic and design an application that benefits both individuals and entire local communities.
Exploring the problems
Netnography, desk research and life gave us some directions to dive in:
🔊 Noise - Noise complaints are among the most common issues in neighborhoods. They often arise from loud music, barking dogs, or late-night parties, leading to disturbances and potential conflicts.
🧽 Cleanliness - Issues related to cleanliness, such as overflowing trash bins or unkempt yards, can create unpleasant environments and contribute to neighborhood disputes.
🐶 Pets - Uncontrolled pets, especially dogs that bark excessively or roam freely, are a significant source of tension among neighbors. Responsible pet ownership is crucial to maintaining harmonious relationships.
👶🏻 Children - While children are a joy, their noise and behavior can sometimes lead to disturbances. It's important for parents to ensure their children are supervised and considerate of others.
🚘 Parking - Limited parking spaces and improper parking practices can cause frustration and disputes among neighbors.
🥳 Parties - Frequent or loud parties, especially late at night, can disrupt the peace and lead to complaints from neighbors.
Higher anonymity = more problems reported
Scientific research analyzing large-scale administrative data from Brisbane, Australia, indicates that neighborhoods with higher resident mobility (anonymity) are more likely to report a greater number of neighbor-related issues to local authorities. This trend is not associated with the affluence or social status of the neighborhoods.
Problems with Neigbors, sagepub.com, 2021
Suburbanization is our blue ocean
Suburbanization is a key social challenge in contemporary urban development. It leads to the separation of social groups that once lived side by side, weakening interpersonal bonds, creating closed communities, and diminishing traditional forms of shared leisure. Moreover, suburbs often lack rich cultural and recreational offerings for residents.
From a product design perspective, suburban residents represent an ideal group of early adopters. These individuals recognize the need for integration within their local community and actively seek alternatives to the urban cultural amenities they enjoyed before relocating.
Based on the reaserch, we created two protopersonas @local_activist and @enthusiast.
Shaping the product
We have recruited 10 people that fit to our protopersonas to proceed further with In-Depth Interviews. Here are the most important insights:
We are the same, but the wall is between us
In today's world, there is a coexistence of being connected to others and feeling lonely at the same time.
People live right next to each other, yet they know nothing about each other's passions, views, problems, or dreams.
Most people don't know about their neighbors hobbies
In in-depth interviews, users were enthusiastic about the possibility of making real friends through a mobile app. Most of the respondents (approx. 80%) had no idea about the interests of their closest neighbors. Many of them declared that such knowledge would be a good topic for starting a casual conversation and, consequently, for making closer friends.
"What's his name?"
Many respondents (approximately 70%) reported that they formed closer neighborly relationships through shared responsibilities such as walking the dog or taking children for a walk. A significant portion of these individuals know the names of their neighbors' dogs and children but not the neighbors themselves. Nevertheless, they exchange smiles, engage in conversations on various topics during walks, and sometimes, these interactions lead to genuine friendships.
Unlike other apps, this one will encourage you to step outside
and spend time in a meaningful
and healthy way.
Unique Value Proposition.
The process led us to draw low-hi wireframes for the core of the product:
Matching user by their interests (my scope),
Organizing the local community's social life,
Helping each other in need.
My idea for matching users
Comparing the most popular social-media apps, I decided to implement some ideas above:
Users list sorted by distance limited to your neighborhood (from Grindr),
Activities filers above profiles set for 24h only (like Instagram Stories),
The rule of mutual desire (like matches on Tinder / Rooms on Discord).
The idea for this product module was easy, based on well-known solutions.
You see your neighbors, then you choose the activity you want to practice (activating the activity filter for 24h), and then you see the list of people that have an active desire for the same activity (on top). Then you see users that pinned this activity as their favorite to their profile, but have no active filter.
The border of the filter works as a timer, counting down 24h.
Users can choose their level for each activity, so you can match your activity partner faster.
Each of us designed prototypes for our user flows and then glued it and showed it to 10 users
10 moderated usability tests were conducted, consisting of the following elements:
Verification of recruitment criteria,
In-depth preliminary interviews (IDI),
A task-based section covering 5 main user journeys.
The study was conducted both on-site and remotely. In both cases, Google Meet was used to record smartphone screens.
Based on the recordings, a grid of test observations was created in a Google Sheet. The observations were then analyzed, clustered, and classified according to the types of problems identified.
