Develop product strategy

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.

Tutorly: 12-month strategy — make every good match count

Recommendation: Put our £1.2m behind improving the first tutor match, not broad paid acquisition. Tutor supply is growing far faster than bookings, 70% of tutors get no booking in a typical month, and students say finding the right tutor is their main problem. Buying more student traffic before fixing that experience risks paying to send more families into the same frustration. The encouraging signal is that students who book again show a 64% repeat rate: when a match works, there is reason to believe students stay.

The bet: a guided matching and first-booking service

Build a faster, more trusted path from a student’s need to a first lesson. Ask about subject, exam level, goals, budget, availability and preferred teaching style; then show a short, ranked list of tutors who are available and suitable—not a directory students must search alone. Make tutor profiles clearer on relevant experience and approach, and prompt tutors to respond quickly. For students who still struggle, offer human-assisted matching.

Start with the subjects and exam levels that already have the strongest demand and enough suitable tutors. Use actual enquiries, bookings, repeat behaviour and student feedback to improve recommendations. This is one integrated liquidity bet: make existing demand easier to serve with existing supply, rather than adding supply or buying broad traffic.

How we will execute

In the first 90 days, analyse the booking funnel and pilot assisted matching in a few high-demand subject/exam segments. Compare it with the current journey on first booking, time to match, cancellations and student satisfaction. In months 4–6, automate the most successful parts and improve availability and profile information. In months 7–12, expand only where the pilot improves bookings without harming quality. Help tutors without bookings improve their profiles or availability; stop actively promoting tutors who are unavailable or consistently unresponsive.

Allocate the £1.2m across the year: £450k product and data, £300k matching and student support, £250k tutor onboarding and quality, £100k testing and measurement, and £100k contingency. Release funding in stages, with a formal review after the pilot.¹

What success looks like

By month 12, target 6,000 monthly bookings (up from 4,000) and at least 45% of active tutors receiving a booking in a typical month (up from 30%). Also improve first-booking conversion and time to first lesson, while maintaining or improving repeat booking, cancellation and satisfaction rates. Report results by subject and exam level so growth cannot hide weak or overloaded segments.

Hold broad paid acquisition for now. Run only small, measurable acquisition tests in segments where matching is working; scale spend only if the resulting students book and repeat at acceptable acquisition cost. If the pilot fails to improve first bookings, stop and reassess before committing the remaining funds. This gives the board a measurable growth bet while protecting our 18-month runway.