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: turn tutor choice into sustained bookings

12-month strategy | Board decision requested

Recommendation

Commit our one major bet to helping students find a suitable tutor and establish a recurring lesson habit. Authorise up to £1.2m in staged funding. Defer broad paid student acquisition until we demonstrate that we can reliably convert demand into repeat bookings at sustainable economics.

Why this bet

Tutor supply has grown 60%, while bookings have grown just 8%. With 70% of tutors receiving no bookings in a typical month, recruiting more supply is unlikely to unlock growth. Students identify finding the right tutor as their biggest problem. Our working hypothesis is that Tutorly offers access without enough confidence to choose.

The board’s acquisition thesis remains plausible: tutors report insufficient students. But that describes their experience, not necessarily the underlying constraint. More traffic could help, or could send more students into an ineffective matching journey.

The reported 64% repeat rate among students who book twice suggests potential value after an initial relationship forms. It does not establish retention across all students or explain losses before the second booking. We must validate its definition and measurement window.

What we will build

Start with the subject and year-group segment showing the strongest combination of existing demand, available tutors and repeat behaviour. Offer students a short intake covering learning goals, syllabus, budget and availability, followed by three suitable, available tutors with clear reasons for each recommendation.

After the first lesson, check fit, offer a straightforward rematch where needed and make recurring booking easy. Begin with manually assisted matching to learn what predicts successful relationships before automating it. Broad tutor recruitment and unrelated marketplace expansion will be deferred.

Execution and investment

In months 1–2, release £150,000 to establish the funnel from qualified enquiry through first, second and subsequent lessons, interview students who abandoned booking and run a controlled matching pilot. Audit “active” user definitions: the headline counts do not establish usable demand or supply.

In months 3–6, release a further £350,000 only if the pilot improves second completed bookings within 30 days by at least 20% relative to the existing journey, without materially increasing refunds or complaints. This is a proposed investment threshold, not a forecast; sample size and measurement rules will be agreed before testing.

In months 7–12, release the remaining £700,000 only if gains persist and contribution economics support expansion. Automate proven matching steps and extend into adjacent segments. Small paid-acquisition tests can then establish whether additional demand scales profitably.

Success and downside protection

Target 8,000 completed monthly bookings by month 12¹, with term-time and holiday comparisons reported separately. Track contribution after payment costs, support, incentives and matching operations alongside booking growth.

Assuming one-hour bookings, current commission revenue is only £24,000–£36,000 monthly. Doubling bookings would not itself establish sustainability. Acquisition spending must therefore recover its cost from observed contribution within six months, rather than speculative lifetime value.

If matching fails the month-six gate, stop further funding and reassess the acquisition thesis. With 18 months of runway, preserving capital when evidence disappoints is part of the strategy.