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 — 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.