Develop product strategy

Tutorly 12-Month Strategy: Fix Matching, Then Scale

Recommendation

Put the £1.2m behind one bet: guided matching with a first-lesson guarantee. Paid acquisition comes second, released in stages once the funnel converts.

Why not paid acquisition first

Tutor supply is up 60% and bookings only 8%, so the constraint is not volume. Seventy percent of tutors get no bookings, and students say their biggest problem is finding the right tutor. Tutors say "not enough students," but that is a symptom of poor discovery, not a shortage of demand alone.

The economics are also tight. At an average £37.50 lesson and 20% commission, we earn about £7.50 per booking. A £50 acquisition cost needs roughly seven bookings from a student just to break even. Our best evidence of retention is that 64% of students who book twice book again, but we don't yet know how many get to a second booking. Paying to send more students into a search experience they find frustrating risks buying churn.

The bet

1. Guided matching (about £600k). Replace browse-and-search with a short intake (subject, exam board, grade target, learning needs, availability, budget) that returns three ranked tutors. Add structured tutor profiles and quality signals such as response time, outcomes and reviews. Route new demand to under-booked tutors who fit well, rather than only to the top few.

2. First-lesson guarantee (about £300k). If the first lesson isn't right, the student is rematched and the lesson is free, funded by us. This targets the drop-off before the second booking, which is where the strong repeat behaviour begins.

3. Supply curation (about £100k). Stop passive tutor onboarding. Admit tutors by subject-level demand, and give inactive tutors coaching or profile support, or deprioritise them.

4. Gated acquisition reserve (£200k). Held back and released only when the gates below are met.

Gates for releasing acquisition spend

Two consecutive months of: - first-to-second booking conversion up at least 10 points from baseline; - median time to first booking under 48 hours; - modelled payback on acquisition inside 6 months.

Until then, test small paid channels only to measure cost per matched student.

Targets (month 12)

• Bookings: 4,000 → 6,000+ per month
• Tutors with at least one booking a month: 30% → 50%
• Students reaching a second booking: +10 points
• Student satisfaction after first lesson: tracked from launch

Timeline

• Months 1–2: Instrument the funnel (search → enquiry → first lesson → second lesson). Establish the baseline. Interview 30 students and 30 tutors.
• Months 3–6: Launch matching to one or two high-demand subjects, such as GCSE maths and sciences. Run against a control group.
• Months 7–9: Roll out across all subjects, and launch the guarantee if the pilot shows it pays back.
• Months 10–12: Release acquisition spend if the gates are met. Report to the board.

Risks

• Matching doesn't lift conversion. Pilots with control groups will show this by month 6, leaving 12 months of runway to change course.
• Guarantee costs exceed benefit. We cap exposure per student and monitor abuse.
• Tutor churn from curation. We prioritise communication and coaching before any deprioritisation.

Ask of the board

Approve the matching bet and the staged acquisition gate. If the board is not willing to defer scaled paid acquisition, the fallback is a capped £150k test with cost per second booking as the success measure.

Tutorly: 12-Month Strategy for the Board

Decision requested: Back one major bet: a guided matching service that helps students find the right tutor and converts successful introductions into repeat lessons. Commit up to £1.2m, released against evidence. Do not make broad paid acquisition the primary strategy yet.

Why this bet

Tutorly’s constraint appears to be successful matching, not tutor availability. Tutor supply grew 60% year on year, while bookings grew just 8%; 70% of tutors receive no bookings in a typical month. Adding more supply will not solve this imbalance.

Students identify “finding the right tutor” as their main problem. The 64% repeat booking rate among students who book twice suggests value after a relationship forms—but excludes students who never reach a second lesson. It does not establish strong retention across the whole marketplace.

The board’s acquisition thesis may be right: insufficient student demand could explain underutilisation. But buying more traffic before testing the matching bottleneck risks paying to reproduce today’s disappointing conversion. At £6–9 commission per lesson, acquisition economics require sustained repeat bookings.

The major bet: guided matching

Replace open-ended tutor browsing as the default journey with a short intake covering subject, exam board, goals, budget, availability and learning preferences. Present three suitable, genuinely available tutors, explain each recommendation and make the first booking straightforward.

Start with human-assisted matching rather than a large algorithm build. Use this service to learn what predicts a successful relationship, then automate proven steps. After the first lesson, collect fit feedback, offer a quick rematch where necessary and prompt a recurring booking where appropriate.

Concentrate initially on the highest-volume subject and exam-board segments identified in our data. Pause broad tutor recruitment; prioritise responsive existing tutors with relevant availability. Success means productive student–tutor relationships, not equal booking distribution across all 6,500 tutors.

Execution and investment

Months 1–3: Diagnose and test. Establish funnel baselines from student arrival through first, second and subsequent lessons. Run a controlled experiment comparing guided matching with today’s journey, using comparable student cohorts. Measure tutor responsiveness and matching-service cost.

Months 4–6: Prove repeatability. Expand only if the pilot improves completed bookings and repeat behaviour—not merely enquiry volume. Refine recommendations, automate intake and scheduling, and establish contribution economics after matching support costs.

Months 7–12: Scale the winner. Roll out proven workflows across suitable segments. Increase paid student acquisition only where matched cohorts demonstrate viable acquisition payback, turning acquisition into distribution for the same bet.

Allocate £450k to product and engineering, £350k to matching operations, £150k to research and measurement, and £250k as a gated scaling reserve. Review spending quarterly against the remaining runway.

Board scorecard and stop rules

Set initial targets of 25% higher first-booking conversion and 20% higher second-booking conversion versus control, with no deterioration in cancellations or lesson satisfaction. Track 90-day bookings and contribution per student, including acquisition and service costs.

Aim to reach 6,000 monthly bookings by month 12, as a planning target rather than a forecast. If the six-month evidence shows no meaningful improvement or an uneconomic service cost, stop expansion and preserve capital.

Strategic principle: Prove that Tutorly can reliably turn student demand into lasting tutoring relationships before paying substantially more to generate that demand.