Curating the Leisure Ride
The Challenge
Due to the closure of gyms, indoor activities and general uneasiness about the safety of public transit in response to COVID-19, many people are picking up a bicycle again regularly for the first time in years — creating a diverse cross-section of new riders.
However, existing cycling apps primarily cater to athletes and fitness enthusiasts, leaving leisure bicyclists to fend for themselves.
Role: UX/UI Designer, Researcher
Product: Mobile App, Concept
Project Duration: 2 weeks - June 2020
Status: Complete
Our Solution
Kickstand is a native mobile application that lets users choose curated ride experiences based on their goals and preferences.
Design Process
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User Interviews
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Competitive Feature Analysis
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Business Model Canvas
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Persona
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User Journey Map
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Problem Statement
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Design Studio
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MoSCoW Map
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Mid-fidelity Figma Prototype
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Usability Testing
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Task Analysis
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High-fidelity Figma Prototype
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Google Heart Framework
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Spec Doc
DISCOVER
Who are our Leisure Riders?
In order to learn more about non-competitive riders, we screened in and interviewed five frequent leisure riders using a standardized guide focusing on their biking habits, activity goals, who they bike with, and pain points they experience. The interviews were conducted over Zoom by three members of the UX team.
Male, 30
Male, 42
Female, 38
Male, 35
Male, 56
"I don't know about the different experiences I could have"
"It would be nice to know more information about the route so I don't end up in 60 mile/hour traffic"
"Novelty is always memorable"
We learned that there is a market segment of leisure riders whose needs are not being met by popular cycling apps.
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So what did we find out about these riders?
Leisure riders want to choose routes based on the activity goals and preferences of themselves and their companions.
Riders are interested in exploring new scenic routes and points of interest.
Riders use various different tools and word-of-mouth to find and plan routes.
Riders want to be separated from cars to feel safe.
Leisure riders are concerned with having amenities and facilities along the way.
EMPATHIZE
What is Important to Our Riders?
To keep our target audience front and center throughout the design process, we developed our primary persona, Benjamin (below). Our persona is an amalgamation of the trends and insights uncovered from user research.
Benjamin
The Bicyclist
38 year old Data Scientist
Married with 2 kids
Goals
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Discover new bike paths
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Explore new parts of the city
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Take kids on scenic rides
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Get some exercise
Needs
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Suitable routes for activity type and companions
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Pack based on route chosen
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Know time commitments of routes
Painpoints
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Concerned about finding nearby amenities and restrooms
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Wants to be separated from cars to feel safe
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No resource to find specific information about routes
The Problem
How might we help leisure bicyclists discover new routes that are suitable for their activity goals and companion's needs?
IDEATE
Design Studio
To jumpstart our design process, we conducted a design studio session - creating quick sketches in a highly time-constrained setting. This helped us get some initial ideas on paper without getting too attached to concepts, and let us build on each other's designs.
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The team then used the MoSCoW Map tool to prioritize feature for the MVP. This was the first step in an iterative design process that developed Kickstand through rounds of design and testing.
Early concept sketches from Design Studio - the team sketched for rounds of 1-5 minutes in order to rapidly generate and refine ideas.
Introducing Kickstand
Kickstand helps leisure cyclists choose curated ride experiences based on their goals and preferences.
Solving for Benjamin
Insight: Leisure riders want to choose routes based on the activity goals and preferences of themselves and their companions.
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Insight: Riders want to be separated from cars to feel safe.
Feature: Filter routes based on goals, activity levels and needs.
Insight: Leisure riders are interested in exploring new scenic routes and points of interest.
Feature: Select rides that match your preferences and track your location and nearby points of interest.
Insight: Leisure riders are concerned with having amenities and facilities along their route.
Feature: Browse nearby amenities and add a stop to your ride.
Insight: Leisure riders use various different tools and word-of-mouth to find and plan routes.
Feature: Rate your ride and recommend routes to other riders.
TEST & ITERATE
Does Our User and Screen Flow Work?
Following an iterative design process, we conducted two rounds of design and testing to evaluate our concept throughout the design process.
After the Design Studio, we developed a mid-fidelity prototype (below) and tested it with 5 members of our target audience over Zoom. Testers were asked to complete 4 tasks, corresponding to the primary features, using the prototype.
Testers were asked to complete tasks using a mid-fidelity grayscale prototype of the product. This round of testing assessed the core functionality and user flows of our app.
What We Learned
For each participant, the team recorded time on task, success rate, and easiness rating - as well as any participant comments, behaviours and feedback.
Key Takeaway: Testers had difficulty initiating the ride in task 2 because they were confused by the use of the GPS icon.
Success Rate
Avg. Time on Task
Task 1
Sign in with your google account and look for bike paths in Seattle that are suitable for children.
90%
44.8s
Task 2
The Elliott Bay Trail looks like it would meet your family's needs. Choose that bike path and start your ride.
60%
53.4s
Task 3
Your kids get hungry during the ride. Tack on a stop at the Soundview Cafe to your ride.
80%
21.6s
Task 4
You have arrived at your destination. Please rate your bike ride and enter a comment.
90%
13.8s
Did the Changes Improve Results? (Mostly)
We developed a high-fidelity prototype to evaluate our design iterations and understand how riders would interact with our product with the incorporation of colour and other UI elements. We tested the high-fidelity prototype with 5 additional participants, following the same script as in round 1.
Key Takeaway: Direct success rate dropped on task 3 as testers expected functionality to allow selection of stops by tapping on the map. As this is conventional in mapping applications, we should consider adding this feature post-MVP.
Success Rate - Round 2
Success Rate - Round 1
T1
90%
100%
T2
60%
T3
80%
T4
90%
100%
70%
100%
Avg. Time on Task - Round 2
32s
6.6s
23s
10s
IMPLEMENT
How Will We Track Success?
We propose tracking the performance and success of Kickstand based on key goals and metrics developed through the Google Heart Framework. Since we are launching a new product to the market, we will be focusing primarily on adoption, engagement and task success as we strive to acquire new active users.