Develop product strategy

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.

Tutorly: 12-Month Growth Strategy (Board Briefing)

Executive Summary

Tutorly has reached a critical inflection point, plateauing at roughly 4,000 bookings per month. While tutor supply has surged by 60% year-on-year, demand has lagged behind, with bookings up only 8%. Consequently, 70% of our active tutors receive zero bookings in a typical month, threatening supply quality and platform retention.

With 18 months of runway and £1.2m in capital, we face a strategic choice. While the board traditionally advocates for paid student acquisition, qualitative research reveals that our primary bottleneck is not awareness, but friction in matching. Students cite “finding the right tutor” as their top hurdle, while tutors report “not enough students.” Throwing capital at top-of-funnel paid acquisition will merely leak budget into an inefficient discovery funnel.

Instead, our single major bet for the next 12 months is to re-engineer the marketplace discovery and matching engine, transforming Tutorly from a static directory into an intelligent, high-conversion matching platform.

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The Core Problem & Diagnosis

Our core metrics tell a clear story of imbalance: * The Supply Glut: 6,500 active tutors vs. 11,000 active students, resulting in 70% of tutors experiencing zero-booking months. * The Demand Efficiency: Repeat booking is healthy at 64% for students who book twice, proving that product-market fit exists once a match is made⁴. The failure is entirely in initial activation and discovery.¹

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The 12-Month Strategic Bet: Intelligent Matchmaking & Activation

We will deploy our £1.2m budget across three synchronized pillars over the next 12 months to drive bookings from 4,000 to 10,000+ monthly transactions.

1. Algorithmic Matching & Guided Discovery (Months 1–4)

• Action: Shift from manual search filters to an AI-driven, intent-based matching questionnaire for incoming students. Students specify learning style, goals, budget (£30–45/hr), and schedule, and receive top-3 instant tutor recommendations.
• Investment: £400k in product engineering, UX redesign, and recommendation algorithms.

2. Supply-Side Activation & Tiered Visibility (Months 5–8)

• Action: Address the 70% zero-booking tutor rate by introducing performance-backed visibility tiers, response-time incentives, and automated profile optimization tools. Tutors who maintain high responsiveness and trial conversion rates are prioritized in the new matching engine.
• Investment: £300k in tutor success tooling, automated coaching, and CRM workflows.²

3. Targeted Demand Amplification (Months 9–12)

• Action: With conversion rates optimized, deploy the remaining £500k into targeted performance marketing and SEO. Rather than broad, leaky acquisition campaigns, we will target high-intent secondary-school parents with hyper-specific landing pages tied directly to instant matching funnels.
• Investment: £500k in performance channels, referral loops, and parent trust initiatives.

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Financial Impact & Board Recommendation

By fixing the matching bottleneck first, we project a rise in trial-to-booking conversion rates from current lows to over 25%, lifting monthly bookings past 10,000³ and securing a sustainable, high-margin marketplace model well within our 18-month runway.

We request board approval to allocate the £1.2m capital to this intelligent matching strategy, prioritizing structural efficiency over superficial top-of-funnel spend.