Develop product strategy

Tutorly: 12-Month Strategy Board paper, for decision

The problem Bookings have grown 8% while tutor supply grew 60%. We are not short of tutors or students. We are failing to connect them. Students say their top problem is finding the right tutor. Tutors say they lack students, and 70% get no bookings in a typical month. Once a student books twice, 64% keep booking, so the product works when a match happens. The leak is in the first match.

Why not paid student acquisition The board's instinct is to buy more students. I recommend against it for now: - Paid students would arrive at the same broken search and matching flow that already frustrates them. We would be paying to acquire customers we are likely to lose before their second booking. - Our tutor base is already larger than our demand. More students would mainly bring more tutors into the same competition for the few bookings available. - Acquisition spend scales with cost. Matching improvements scale with use.

The bet: fix first-match conversion (£1.2m) Over 12 months we will build a guided matching system and test it against the current search:

1. Structured intake (Months 1–4). Students give subject, exam board, year group, goals, budget, and availability. Required before search.
2. Curated shortlists (Months 2–6). Within one hour, students receive three ranked tutors with a free 15-minute intro call. A small concierge team handles hard cases such as niche subjects and last-minute exam prep.
3. Ranking that rewards fit and responsiveness (Months 3–8). Ranking weighs subject expertise, response time, and student outcomes. New tutors get guaranteed early exposure so that supply does not stall at the bottom.
4. Tutor activation (Months 4–12). Tutors with zero bookings in 30 days receive coaching on profile quality and availability, plus targeted introductions.

Budget (£1.2m) - Product and engineering: £600k - Concierge matching team: £300k - Tutor activation and targeted student tests: £200k - Measurement and contingency: £100k

Targets (12 months) - Bookings: 4,000 → 5,500 per month - Students with a first booking within 7 days of intake: baseline in Month 1, target +50% - Share of tutors with at least one booking per month: ~30% → 45% - Repeat rate (second booking): hold at or above 64%

Decision gates - Month 3: Intake and shortlist live for a test cohort. Confirm the conversion baseline and the uplift. - Month 6: If first-match conversion has not improved by at least 20% against control, stop further build and move remaining funds to a small, controlled paid-acquisition test. If it has, scale the concierge model and begin targeted acquisition in segments where our supply is deepest. - Month 9: Review unit economics. Each booking should contribute enough commission to cover concierge cost.

Runway This plan spends £1.2m over 12 months and leaves about six months of runway for the gates above. If the Month 6 gate fails, we retain the option to pivot without running out of cash.

What we are asking the board to approve 1. Fund the £1.2m matching-led plan. 2. Approve the Month 6 gate and the reallocation rule if it fails. 3. Defer paid student acquisition until conversion data supports it.

The board's instinct is right that growth needs more students. But we already have more tutors than we can match, and the evidence points to the match as the bottleneck. Fixing that first makes every future pound of acquisition work harder.

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.