R U N N I T: Designing the Best Run
Task
To find a primary user’s problem and design a solution.
Timeline | 1 week
Discovery & Research
In-Depth Interview
I began by interviewing my primary user in a conversational manner, about his daily struggles and day to day activities. I learned that:
- he is new to the city
- he enjoys running to unwind
- he has very particular preferences for where he likes to run
- he has not found a place to run in the city because he doesn’t know the area in order to find a place that meets his needs
- he is becoming quite stressed due to his inability to release his stress with a run
User Interviews
After deciding that I would help User 1 find an adequate place to run, I asked other users some questions to aid me in definging the problem and finding the best solution. Questions I asked were:
- What type of terrain do you like to run on?
- What’s the proximity (to your home) you like to run in?
- Is scenery important to your run?
- Is tracking your distance important?
Interviewees also gave me great insights on their our, such as how some prefer running alone while others thrive in groups.
Affinity Mapping
I created an affinity map to clarify my findings and start searching for patterns and themes.
The themes I found to be consistent between users were:
- Popularity — most users avoid crowds
- Proximity — users prefer nearby runs, some even just leave from their door
- Tracking Capabilities — users like to know how much they’ve run and even choose paths because they know it’s distance
- Scenery — most users enjoy having some type of view
- Challenge Level — most avoid hills
- Time of Day
- Weather — some prefer evening because it’s cooler (also applies to time of day)
- Familiarity
Insights
I synthesized my research into the following insights:
- The pattern between the themes in my affinity map seemed to be that these were all important factors to a runner choosing a path.
- User 1 needed help finding a path for his preferences in his new unfamiliar territory
- Other users who are familiar with the area usually run the same path, or just around their neighborhood, but would explore other areas if they knew about paths that met their specific requirements
Design Direction
I saw an opportunity to solve for this problem:
Users want an easy way to check for all factors that are important for them to achieve the most effective run for their needs when choosing a path to run on.
While there are apps that check for some of these factors separately, there are none that check for all and tailor suggestions.
This solution would be an app that assesses the user’s preferences and suggests paths that are convenient in proximity to the user and will provide the most effective run to fulfill their needs.
- Routes would be added and reviewed by other user’s in order to provide information about path’s factors and keep paths up to date.
- User’s would receive alerts about particular runs really worth their time if not in direct proximity, such as for users who want a long, scenic run with few others running around them; or alerts about group runs for those who like to run around others.
- They would also receive notifications if there was a great run near their current location and the weather and population level was at their preference at the time.
RUNNIT 1.0
After sketching, a few prototype iterations, and user testing, this is RUNNIT 1.0.
Prototyped using Flinto.
Phase 2
Task
To find and implement potential improvements to RUNNIT 1.0.
Timeline | 2 Weeks
Discovery & Research
Google Survey
Sent out an open survey asking questions about running habits, reasons, and concerns. Also asked about app usage.
User Interviews
I began by talking openly to some of my previous users, some new users, and friends about their general running habits and needs. Topics included why they run, what apps they use, and why they share on social media.
Competitive Analysis
Tested the most favored running app, Nike+ for myself and took a deeper look at all it’s features.
User Testing
Tested early iterations and found out even more about user’s needs and processes; what they liked so far and what they felt was missing.
Synthesis
After analyzing my research, I came to 3 insights:
- motivation is important and often necessary in going for a run, especially on days when you’re just not feeling it
- running with others — with friends or in groups — pushes your limits and is less boring , keeping people motivated
- social sharing presents people with a feeling of accountability, making them want to share progress
Design Direction
With these insights, I saw the opportunity to enhance the existing experience of finding new routes in RUNNIT 1.0, with the ability to share with friends and stay motivated through peer support.
RUNNIT 2.0 would keep the existing preference assessment and route recommendation, and have 2 new features:
1. Social Support, where users can stay motivated by:
- Seeing friends activity
- Meeting up and joining a friend on their run in real life
- Challenging a friend to beat their best runs
- Recommending routes to each other
and
2. Mood Filters, so users can:
- Choose to take it easy if they’re not feeling their usual settings
- Step it up if they’re feeling up to a bigger challenge
When implementing these features, I kept a user persona in mind — Marie — who I felt had all the characteristics, needs, and wants of the users I spoke to. I also developed 3 users flows to guide me in best fitting her needs.
Prototype
Here are demos of the 3 main features for RUNNIT 2.0, after user testing and iterations.
Prototyped using Flinto.
This is how users would find new recommended routes.
This is how users would find motivation to run through social support.
This is how users could change their challenge level to keep them going at any mood.
Here’s the full preview of RUNNIT 2.0
Current Success
RUNNIT has been successful in it’s route recommendations since inception, but users really loved the Social Support and Mood Filter, saying:
“I feel like challenging others would definitely get me moving more, and having friends recommend me routes they think I’d like.”
“I can definitely think of times where I needed an option to take is easier.”
Next steps
Future enhancements to RUNNIT could include:
- create a slim & sleek Apple Watch companion app — it was observed in my research that users prefer to not have to carry their phone while they run
- expand tracking capabilities so users can truly see and feel motivated by their improvement — another important motivator
- additional motivational features, such as Role Model Updates, i.e. “The Rock just ran 7 miles!”
Thank you for taking an interest in RUNNIT.
