Develop product strategy

Tutorly: 12-Month Strategy

Sep 24, 2026 · @Martin Slaney

The problem is matching, not demand¹

Tutor supply grew 60% last year; bookings grew 8%. 70% of our 6,500 tutors get no bookings in a typical month, and our 11,000 active students generate only \~4,000 bookings a month. Both sides report the same failure from opposite ends: students can't find the right tutor; tutors can't find students. Adding supply has made the catalogue bigger and choosing harder.

The bright spot: students who book twice repeat at 64%. The relationship works once it starts.³ The leak is between arriving and the second lesson.

Why paid acquisition first is the wrong bet

Paid acquisition sends more students into the same funnel that already fails to convert most of the ones we have. We'd be buying students at full cost and losing them at the step before the relationship forms. At a 20% take on £30–45 lessons (\~£7.50 per booking), we only recover acquisition cost from students who stick; today too few get there. Fix conversion first, and every pound of acquisition afterwards is worth more.

The bet: managed matching

Replace browse-and-hope with matching we take responsibility for.

• Guided intake. Subject, level, exam board, goals, learning style, budget, availability. Two minutes, not twenty profiles.
• We recommend three tutors, ranked on fit and track record, not on who signed up most recently.
• Low-risk first lesson. A discounted trial plus a "right tutor or your next lesson free" guarantee.
• Curate supply. Stop broad tutor recruitment. Recruit only where demand outstrips supply (by subject and exam level); rank and surface tutors on conversion and repeat rate; move inactive tutors out of results.
• Start with human matchers, then automate what works. The concierge data trains the algorithm.

Budget and milestones

Month 3: matching live for new students in top 3 subjects. Baseline set for first-to-second-lesson conversion (we don't track this cleanly today²; it's the number that matters most).

Month 6 gate: first-to-second conversion up 50% on baseline; share of tutors with zero bookings below 55%. Hit it, and we release £350k into paid acquisition, now into a funnel that converts. Miss it, and we hold the cash as runway.

Month 12: 6,000 bookings a month (+50%), with growth coming from bookings per student, not student count alone.

Ask of the board

Back matching as the one bet, with acquisition as its reward, not its substitute. We're not against growth spend; we're against spending it before the product can hold the students it buys. With 18 months of runway, the month-6 gate gives us a clear read while two-thirds of the budget and a year of runway remain.

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.