Develop product strategy

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.

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.