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: 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.