Modernisation of Expert QnA Platform
Background
We are investing in a unified Expert System to solve tomorrow's problems and not just handle the scaling needs of today. To do this, we are rebuilding the existing expert system piece by piece from the old tech stack (PHP) into a new tech stack (React), which is faster, more secure, easier to maintain, and more robust.
This study represents 20% of the traffic on chegg.com. We want to separate the expert experience from the student experience to scale the systems independently.
What Problem are we solving?
As a Chegg Expert, I require a better mobile answering experience for uploading images, typing text, and viewing all the answering elements.
As a business, I need a scalable, robust Expert System with standardized analytics on par with modern technologies.
As a business, I want to upgrade the CK editor tool, which experts use to answer questions.
As a business, I want to release expert initiatives faster by having dedicated infrastructure for Expert System.
As a business, we want to release expert initiatives faster by having a dedicated authoring editor infrastructure for the Expert System.
Who Is The User?
We have 160k+ Expert Answerers from India who freelance with Chegg to answer questions.
They answer 152k+ questions/week from desktop and mobile-web.
Expert Empathy map & persona
Herein we describe the creation of a set of 2 personas focusing on the needs and motivations of two different subjects around assessment and teaching.
Competitive analysis: Editor Authoring Tools Benchmarking
Wyzant
Hello Tutor
Hello Tutor
Hello Tutor
Hello Tutor
Hello Tutor
Edulastic
Hello Tutor
Edulastic
Ideation and Wireframe: Low Fidelity
Building Superior Quality Editor Authoring Tools | Enhanced content creation
IDEATION AND WIREFRAME: HIGH FIDELITY
Building Superior Quality Editor Authoring Tools | Enhanced content creation
Responsive Breakpoints
1200px > 900-1199px
600-899px > 375-599px > 320-375px
600-899px > 375-599px > 320-375px
- Count: 12
margin: 48px
gutter: 24px. - Count: 12 and 8
Margin: 48px
Gutter: 24px
600-899px > 375-599px > 320-375px
600-899px > 375-599px > 320-375px
600-899px > 375-599px > 320-375px
- Count: 8
Margin: 48px
Gutter: 24px - Count: 4
Margin: 16px
Gutter: 16px
Post Launch in Oct 2021-Guerrilla Testing and UX Survey@At-R
UXR Interview Key findings
UXR Survey Results
UX Process & Methodology
Product Discovery Process:
We have created a modified version of the Google design sprints methodology to help us integrate design thinking in our discovery process.
Step 1: Understand, Define and Align - 2-3 weeks.
Kickoff, Understand needs, collect data, User journey mapping
Step 2: Ideate - 1 week per design sprint
Recap of learnings, Ideation workshop, Prototype, test, Wrap up with next steps
Step 3: Iterate & Combine - 4 weeks
Top ideas generated in design sprints , Identify areas of friction & dissonance, Create a unified end to end experience across web & native.
TImeline: 1 week per design sprint
Participants: Core (PM, UXD, EXR, UXCD, PMM), Specialists (Data science, content) Run parallel design sprints for each focus area identified. •These sprints are run best when all participants are together in person for a week. They clear out their calendars and are 100% focused on the sprint.
Include native team in each area of work. They will work asynchronously on ideation & prototyping but join in for synchronous sharing at specific milestones.
Design System Visual components
Editor icons
Vector illustrations
Color palette
Increased focus on accessibility than it was previously. Additionally, each component has been reviewed by Deque for additional to ensure we meet a11y standards and will be acceptable for all types of users.
Enhanced components
More advanced components with cleaned-up code.