A one-stop shop for students to learn about student loans and personal finance. It adapts to users’ knowledge build-up through customized learning modules, trust-worthy content curation, and mentorship.
During the research phase, I conducted qualitative studies including surveys and interviews, and led synthesis workshop. During product development, I created initial wireframes, conducted usability tests, and designed mid-high fidelity prototypes. I independently created V2 of hi-fidelity prototype after the team project ended.
INTRODUCTION
Challenge
How might we connect students to relevant information so they can make sensible decisions about student loan and personal finance?
Why Is This Important
~75% of American college students rely on student loans, but they are neither mentally nor financially ready to be in-debt
Among student loan holders:
were late with a payment at least once in the past year
are concerned that they’re unable to pay off their student loans
didn’t estimate monthly payments when obtaining their loans
Dumble understands every student’s unique situation and changing journey. Unlike existing solutions, user go through a natural knowledge build-up from learning factual information, to contextualizing that info through mentors.
Understand everyone's unique situation
Onboarding Flow
All personal information is exclusively used to better personalize content for you later. Sensitive information are optional and could be filled in later.
Knowledge Quiz
Dumble takes you through a lightweight quiz to gauge your current understanding of student loan and personal finance topics, in order to structure your learning modules later.
Provide personalized, trust-worthy content
Learning Module
Topics are structured to suit a healthy build up of financial knowledge and relevancy to you, in your preferred learning format.
Home Feed
Similarly, your home feed will be personalized with content that is the most relevant to you from vetted sources. It will be a combination of factual info and real accounts from mentors and peers.
Find a mentor that you can rely on
Mentor Recommendation & Structured Discovery
You can trust the system to recommend a selection of mentors based on your info, or feel free to browse our trusted and reviewed mentors.
Mentor Profile & Connecting
Browse through detailed mentor profiles and see their story.
Reach out by either setting up a meeting or directly messaging them.
RESEARCH
Research Process
Desk Research
Literature Review Competitive Analysis
7 Interviews
Screener Survey Interview
3 Design Probes
Contextual Inquiry Dairy Study
Research Questions
1. How did students develop financial literacy surrounding student loans? 2. How do/did they navigate the student loan process? 3. How do student loans affect their day-to-day emotions and behaviour? 4. How do student loans affect their long term plans?
I drafted screener survey and recruited participants across channels of 200+. 13 participants completed our survey. It was surprising to us that while they rely on student loans heavily, all of them expressed strong, negative feelings towards them. They used the words such as "travesty", "trapped" and "predatory".
Survey Data click to view more
Semi-structured Interview
7 participants were selected from the survey pool to cover a variety of demographics, financial situation and points in student loan journey. I moderated and took notes for 5 interviews.
Next, we conducted a 3-day design probe to contextualize participants' financial behavior and decision-making rationale. I collaborated with 2 designers to design the probe and collect responses.
Day 1
Tracking Finances
Record their transactions and briefly describe their decision-making process
Day 1 Response click to view more
Day 2
Resource Allocation
Allocate a set a set of imaginary financial resources, and how this allocation might change when student loans is introduced?
Day 2 Prompt & Response click to view more
Day 3
Choose Loans
Choose your student loan payments (length of time vs payment amount)
Day 3 Prompt & Response click to view more
Probe Findings
1. Most participants plan to prioritize spending on necessities, but makes impulsive small purchases sometimes 2. Only one out of seven participants invests currently, but they all plan to invest in the resource allocation scenario 3. When taking student loans into consideration, participants often cut back on experiences and hobbies
Financial literacy are often gained through mistakes that can have significant costs later on.
02.
Enough but not good enough
Student loan information exists, but it's hard for students to assess what's trust-worthy and relevant to them.
03.
The beginning of a toxic relationship
There is a cyclical pattern between people's student loans and career: their early career plans are often dictated by loan burdens.
IDEATION
HMW
How might we connect students with relevant information so they can make sensible decisions about personal finance?
Design Principles
Considerate
Accessible
Trustworthy
Down-selecting 120 Ideas
After grouping the initial concept sketches based on design principles, I led the team ideation session where we down-selected using dot-voting, barometers and priority matrix.
Down-selection Board click to enlarge
Until we got stuck...
But every idea had its pros and cons and not one satisfies all of our design principles. The priority matrix didn't provide clarity either because ideas could be very close to each other depends on the execution.
Priority Matrix click to enlarge
Epiphany!
That's when I shared mycake metaphor epiphanywith my team, which became our product model. Our initial ideas either touched on: 1. Vilified information 2. Other people’s lived experiences 3. Relevant content. But we need not just one, but all of these pieces to come together for a comprehensive solution.
It's like making a cake: People need to first establish a layer of factual knowledge to understand student loan as a topic; Then to make decisions, they want to know other people’s lived experience to contextualize the abstract knowledge. The best technology platform is a web app where people already use to search for information, and is accessible to most students.
ITERATION
User Flows
Based on our key offerings: curated content (learning modules + feed) and mentorship program, I developed two user flows to visualize a typical journey for both primary and secondary users.
click to enlarge and view secondary user flow
Challenge 1
How can we best help user learn?
Knowledge Quiz
From research insights, we know users crave relevancy. I created the wireframes of a knowledge quiz, with which the system could use explicit user inputs to personalize learning modules. But we were worried about demotivating users with this barrier of entry: should we make it mandatory? How long should the test be? I conductedA/B testing between a 5 question (short, less accurate) and 15 question (longer, more accurate) knowledge quiz path.
Knowledge Quiz Wireflow click to enlarge
Related Finding
1. Users are okay with a 10-15 minute knowledge quiz as long as the purpose is made clear- they prefer to provide sufficient data for better personalization. 2. Knowledge quiz should be optional and retake-able. 3. Avoid critical statements or numbered scores - it makes users feel judged.
Learning Home
Now that we have a baseline of users' knowledge, the next question is, how can we best cater for their learning style? I designed and tested 3 iterations of learning homepage:
V1. Locked Path
V2. Tiered Path
Related Finding
1. Users want a designed curriculum for guidance and structure 2. At the same time, they dislike the locked path because it limits their ability to explore beyond the determined structure
Final Version. Combining the UI of V1 and concept of V2
Challenge 2
What does it take to make a good match?
First impression is important. We investigated: 1. How do user want to match with a mentor? 2. What do users need to know about a mentor before they initiate a first connection?
Competitive Analysis
I analyzed theonline finance/professional mentorshiplandscape to understand their business model, matching mechanism and mentor interaction methods for reference. I found that: 1. Most are on a volunteer basis, mentors are incentivized for professional recognition 2. Most platforms look generic and does not immediately provide a sense of trust 3. Platforms that are the most successful provide detailed mentor profile and reviews
Mentor Discovery
I designed and conducted user testing where user can choose to be matched based on their info or they can browse through a list of mentors.
Mentor Matching Wireframes click to enlarge
Related Insights
1. Users want both system driven and free-discovery matching mechanisms. 2. Users like that they can message the mentor before they connect and offload the logistics to Dumble if they do connect 3. Users like seeing mentors' forum responses to gauge their style
Final Design. Both matching mechanisms available. click to enlarge
Mentor Card
I designed and conducted user testing where user can choose to be matched based on their info or they can browse through a list of mentors.
Card Sort Template click to enlarge
Card Sort Ranking click to enlarge
Related Findings
1. Users rank info relating to mentors' loan story and current status highest 2.Reviews and loan data builds credibility and trust
Final Design incorporating card sort ranking results click to zoom
BACKSTAGE
After the team project ended, I independently designed the hi-fidelity prototype with full feature flows as well as the visual system.
Dumble was my second end to end product design ever, which meant was a rapid process of learning through doing and implementing theory into practice. I learned a lot from working with more experienced designers and researcher. But I also had a lot of courage leading team conversations and tasks because I knew it's okay to be wrong - and iterate.
My proudest moment in the project was when I came up with the product model. While I leaned towards my instinct, I went back to user accounts to verify that the idea was grounded in research data, and came up with an effective way to communicate it with my teammates. And kudos again for my talented teammates: Aspen Tng, Makeda Adisu and Ojurere Shonekan - for their awesome work, trust and humor.
