Develop product strategy

Tutorly: 12-Month Strategy

Sep 24, 2026 · @Martin Slaney

The problem is matching, not demand¹

Tutor supply grew 60% last year; bookings grew 8%. 70% of our 6,500 tutors get no bookings in a typical month, and our 11,000 active students generate only \~4,000 bookings a month. Both sides report the same failure from opposite ends: students can't find the right tutor; tutors can't find students. Adding supply has made the catalogue bigger and choosing harder.

The bright spot: students who book twice repeat at 64%. The relationship works once it starts.³ The leak is between arriving and the second lesson.

Why paid acquisition first is the wrong bet

Paid acquisition sends more students into the same funnel that already fails to convert most of the ones we have. We'd be buying students at full cost and losing them at the step before the relationship forms. At a 20% take on £30–45 lessons (\~£7.50 per booking), we only recover acquisition cost from students who stick; today too few get there. Fix conversion first, and every pound of acquisition afterwards is worth more.

The bet: managed matching

Replace browse-and-hope with matching we take responsibility for.

• Guided intake. Subject, level, exam board, goals, learning style, budget, availability. Two minutes, not twenty profiles.
• We recommend three tutors, ranked on fit and track record, not on who signed up most recently.
• Low-risk first lesson. A discounted trial plus a "right tutor or your next lesson free" guarantee.
• Curate supply. Stop broad tutor recruitment. Recruit only where demand outstrips supply (by subject and exam level); rank and surface tutors on conversion and repeat rate; move inactive tutors out of results.
• Start with human matchers, then automate what works. The concierge data trains the algorithm.

Budget and milestones

Month 3: matching live for new students in top 3 subjects. Baseline set for first-to-second-lesson conversion (we don't track this cleanly today²; it's the number that matters most).

Month 6 gate: first-to-second conversion up 50% on baseline; share of tutors with zero bookings below 55%. Hit it, and we release £350k into paid acquisition, now into a funnel that converts. Miss it, and we hold the cash as runway.

Month 12: 6,000 bookings a month (+50%), with growth coming from bookings per student, not student count alone.

Ask of the board

Back matching as the one bet, with acquisition as its reward, not its substitute. We're not against growth spend; we're against spending it before the product can hold the students it buys. With 18 months of runway, the month-6 gate gives us a clear read while two-thirds of the budget and a year of runway remain.

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.