C.O.T

COT’s tutoring ran on intake forms and phone calls. We turned it into a self-serve app where learners sign up, get matched, book and prepay.

Status

MVP, in development

Client

COT, an online tutoring company (under NDA)

My role

UX research and product design; I owned payment and checkout

Team

Jr designer, Sr designer, the founder and COT’s engineering team

Timeline

6 weeks

Segment

Learners: students and adult learners, with parents as the purchaser

Tested with

4 participants, 100% task success

North star

Prepaid sessions booked in the app without staff help, and completed, per week

Stack

Figma, Notion

1. Background

COT is an online tutoring company that connects students and adult learners with professional tutors. It had a working website but no mobile app, and every new customer still went through a manual intake. We delivered a five-flow, high-fidelity prototype with annotations, handed off for development.

2. Project overview

Mission. COT gives students personalized one-on-one virtual tutoring from qualified educators, helping them build confidence and reach their academic potential.

The business problem. Before anyone could book, they filled in a form, waited for an internal review, then got on a live call. The founder said this was “leading to lost revenue from potential customers due to wait times,” and it tied staff time to every booking and reschedule. He wanted customers to book on their own, in an app built for actions, not reading.

Problem statement. Parents and adult learners struggle to find a tutor they trust who fits their goals, then hit friction scheduling, paying and communicating. The app had to cut the time from first contact to first booked session without losing the trust and tutor quality the manual process protected.

3. Ideation overview

Stakeholders. Weekly reviews with the founder to agree scope and priorities, and alignment with his engineering team on what could ship. The founder’s must-haves set the guardrails: onboarding, a dashboard, pricing and payment, session booking and events.

Scope. We built on an earlier team’s research and added our own in week one:

• 4 user interviews: a parent, a student, a teacher who tutors and a working professional upskilling
• 4 competitor apps reviewed by signing up and walking their flows (Wyzant, Kadama, Varsity Tutors, Chegg)
• AI-assisted secondary research on the tutoring market, then affinity mapping into themes

Target customer. Research produced four personas:

4. Customer segmentation

We prioritized learners: the students and adult learners who take the sessions. Parents stay in the picture as the purchaser.

Scored 1–5, retrospectively from research findings.

Learners feel the pain at every session and are most of the user base, so they shape the core flow: match, book, attend. Parents mostly enter at one moment, when they pay. That split shaped the MVP: matching and booking are built around the learner, and the checkout I owned is built for whoever holds the card. Tutors mattered for trust, so they appear through profile cards rather than a tutor-side app.

5. Customer pain point

We prioritized finding a tutor who fits, just ahead of booking and payment friction.

Scored 1–5, retrospectively from research findings.

Booking friction was the pain the founder felt most, and interviews backed it up: one parent had to phone in just to move a session, and when a tutor cancelled, her next session slipped a full week. But scoring changed the order. A smooth booking flow is worthless if the learner doesn’t trust the match, so matching came first, and booking and checkout followed from it.

6. Prioritized solution

We chose a matching questionnaire that leads straight to top-matched tutors and booking: the buildable first version of smart matching.

Scored retrospectively from the feature backlog, client meetings and research.

The questionnaire reuses the intake questions COT’s staff already asked on the phone, so it cuts the review-and-call wait without new data or infrastructure. AI matching would fit best long term, but needs session data COT doesn’t have yet. The questionnaire collects that data, which makes it AI matching’s first version. Search and profile cards stayed as supporting paths.

From 25 ideas to an 8-feature MVP. We ranked 25 features P1 to P5 with the founder. The final P1 set: sign-up and onboarding, matching and booking, search by subject, tutor profile cards, pricing and purchase, a learner/admin dashboard, events and an FAQ chatbot.

What we cut, and why

7. Develop MVP

The MVP is one continuous journey: sign up → get matched → book → pay → confirm, plus a chatbot and events to bring people back.

North star: prepaid sessions booked through the app without staff help, and completed, per week. It rewards what the founder cared about: customers booking on their own, fewer abandoned bookings, sessions paid in advance, and sessions that actually happen.

• Booking-flow completion rate, the inverse of abandonment
• Share of sessions paid with credit bundles rather than one at a time
• Reschedules handled in the app instead of by staff, a proxy for admin cost
• Post-session satisfaction score

COT had no in-house payment system, so booking and payment were designed to run on a third-party tool, which kept engineering scope small.

User stories and acceptance criteria

Every story was held to the test plan’s bar: at least 90% task success, under 5 minutes per task, no critical errors.

User flows

Each flow maps the happy path, alternative paths and error paths. Select a flow to open it full size.

8. Launch plan

The app is in development and hasn’t launched yet, so this is the go-to-market plan we handed over, not a result.

• First market: existing customers who already book through the manual intake, mainly parents and adult learners. They already trust the service, so the app replaces a process instead of having to win them over.
• Target population: a closed beta of current active customers first, then every new inquiry from the website.
• Promotion: intake-form replies say “book instantly in the app,” an email to existing customers, and discounted credit bundles as the launch incentive. In-app events give people a reason to come back.

9. Customer feedback and iterations

All 4 usability test participants completed every task: 100% task success, beating our 90% bar. Participants had used or considered online tutoring, and worked through six tasks on the hi-fi prototype: book a tutor and add it to the calendar, ask the chatbot a question, view upcoming sessions, buy more credits, register for an event, and create an account.

Between low and high fidelity we iterated through weekly founder reviews, team critiques and mentor feedback, each round aiming for fewer steps from discovery to confirmation, clearer pricing and less to think about at checkout. Once the beta is live, the north star and its inputs replace task success as the bar.