Working at Brainly
Homework help for students
Brainly is an education technology platform supporting +350 million school-aged students worldwide
My role was that of a Senior Product Designer in the B2C paid service, Tutoring, which connected students with tutors.
We worked with mobile (iOS and Android) and a web-based version of the platform.
Projects were built as a team with a strong design thinking process.
We collaborated extensively with product managers, engineers, data analysts, quality assurance, and directors.
My role in the team
Some context to start with
I was the core designer of this team’s projects, leveraging design strategy and business to reach the best outcomes with stakeholders.
I also worked on the research, creating scripts, and prototypes up to moderating.
We performed UX audits, thinking about how to enhance the user’s experience.
Notably, when performing qualitative research, users pointed out product deficiencies and quantitative data supported these claims.
Our team began asking questions…
I mean... of course we asked such things before! but at this point the team maturity was right to solve such issues 😉
Do users understand how to use our product?
Are we giving our users the proper tools to accomplish their goals?
Are we setting up our users for success?
How users ask questions today.
Our topic for this case study
Here’s the laid out user flow:
Why are users dropping out?
A look at quantitative and qualitative data
We noticed that most paying users did not even use the product for the 1st time.
Users often entered the flow to ask a question but did not finish it; they dropped at different points in the flow.
The process of using the product was complex.
Users found workarounds to match their ways of working - mainly sending in images as questions, instead of typing them.
Around 26% of users started a session with a Tutor, a 74% dropout from initial intent to ask a question
4000→
Users entered the flow to ask a question (100%)
1770→
Users selected a subject for their question (44%)
1375→
Users clicked the ask a question button (77%)
1370→
Users reached the screen to be matched with a tutor (99%)
1050
Users started a session with a tutor (76%)
The business perspective
We needed to optimise this flow to ensure both business and user needs were met
11%
Conversion Rate (CVR)
⬆️ Needs to increase
50sec
Average time to ask a question
⬇️ Needs to decrease
0.5
Average sessions per user
⬆️ Needs to increase
★3.9
Session satisfaction rate
⬆️ Needs to increase, but not that impacted by this project
🚧 and after a lot of work… 🚧
Sneak-peek of the ideation Miro board — featured here, UX audits, benchmarking, design inspiration, organisation and more!
We arrive at the testing phase
To make better use of the time we divided the scenario into two and concept-tested new features
We conducted 10 Research sessions via live interviews, two sets of prototypes, with school-aged children.
We redesigned the flow for asking a question - now featuring steps and a quicker experience.
We updated illustrations and the UI, a new concept of a “clean” aesthetic that highlighted diversity and inclusion.
We updated UX writing to ensure the best information is given to users at each step of the flow.
We added complementary ways to ask a question, such as via taking a photo - 70% of users attempt this.
We also concept-tested a new way of having tutoring sessions - to better understand users’ mental models.
The two prototypes
Prototype A
Recurrent users, active Tutoring users, focus on HOW a question is sent to a tutor
→ Sneak-peek: Some early concepts/wireframes and solutions from brainstorming that were not used to opt for simplicity and ease of use.
Prototype B
New users, with no prior knowledge of Brainly. Focus on helping new users through onboarding and picking a question
After the interviews, my notes looked like this, all set for great affinity mapping
— Of course, bit hard to see at this scale, but I’m happy to share my UXR process in-depth in a call
…which resulted in some amazing feedback for all the Tutoring teams!
What were the results of these research sessions?
To summarise a bit…
✅ Redesign is validated! 👏🏼 Developers can start...development ✅
Users were pleasantly surprised about the degree of personalisation for their tutoring sessions.
Users highlighted the importance of the steps and preferred how the information was structured in the new flow.
Users appreciated the improvements in accessibility since texts were bright, clear and easier to read.
Users mentioned diversity and inclusion due to the new illustrations which highlighted different skin colours and body types.
Scaling up: what came next
…and others that failed to launch
✅ “Asking questions via images”
Users had a high interest and need, it was validated in both scenarios since all users mentioned this method of asking questions.
→ This will be a focus area for the team in the upcoming quarter.
⚠️ “Asking multiple questions”
There was a high interest and need, and it was validated in both cases.
→ This is a complex project due to the current development architecture and implementation constraints it will be tackled later on in the roadmap.
“...I typically have about four chapters of work to do a day to get finished with what I need to finish and if I have a bunch of questions on one chapter, I'll be stuck waiting like hours trying to finish that chapter.”
- Maya, Brainly user
🛑 “Help me pick a question”
Users showed low interest and therefore a low need
→ This project was deprioritised, investigation will continue to understand the core issue of users dropping out.
“...I mean I don't think that like… if someone's like going to ask a question… then I don't think that they like need a question to ask.”
- Remi, a Brainly user
👌🏻 “Improvements to matching”
There was a medium interest and therefore a medium need.
→ This project is a small effort, good results project, it will be tackled soon as a nice to have
“...for other subjects it says ‘we will let you know when someone is available’ and they don't follow up on that.”
- Lena, Brainly user
The high-ticket item: Business impact
This project was quickly released and we got some ⭐️ NICE ⭐️ numbers
30%
Conversion Rate (CVR)
Before 11% — a 170% increase!
30sec
Average time to ask a question
Before 50sec — almost reduced in half
1.5
Average sessions per user
Before 0.5 — a x3 increase
💸 Consensus: Good UX does wonders for business! 💸
★4.0
Session satisfaction rate
Slowly getting there, but this metric is more about the whole experience
After all of this… what are my learnings?
A lot of research, a lot of collaboration, and a lot of insights!
We optimised our internal ways of working between designers, ensuring value is delivered more efficiently.
We improved our user recruitment methods, managing to talk to our users directly.
Talking with actual users of our product is a must, and the learnings we get from them aren’t comparable in quality and quantity to generic candidates.
what would I have done differently?
❗️Some alignments with stakeholders could have been done sooner to avoid too much back and forth.
🖥️ Developers should have entered earlier to give insights into the viability of certain features.
…and that is it!
Thank you for reading 💫
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