Develop product strategy

MEMORANDUM

TO: Board of Directors, Tutorly FROM: Chief Executive Officer DATE: October 2023 SUBJECT: 12-Month Strategic Plan: Unlocking Growth via Managed Matching

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Executive Summary: Diagnosing the Growth Plateau

Our marketplace has stalled at 4,000 bookings/month (~£30k monthly net revenue) despite healthy user numbers (11k students, 6.5k tutors).

The Board’s instinct to deploy our £1.2m reserve into paid student acquisition is understandable, but premature. The data reveals a liquidity and conversion bottleneck, not an awareness problem: * Supply is bloated and disengaged: Tutor supply grew 60% YoY, yet 70% receive zero bookings monthly. * Friction kills discovery: Students cite “finding the right tutor” as their primary obstacle. Our open-directory model forces parents to sift through 6,500 profiles, causing decision paralysis and drop-off. * Underlying product-market fit is proven: When students reach a second booking, 64% retain.

Pouring paid traffic into an unguided directory will burn our £1.2m on high CAC and low conversion. Our single bet for the next 12 months is transitioning from an open directory to an intelligent, Managed Matching Engine.

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The Single Bet: "The Perfect Match" Engine

Instead of browsing endless profiles, parents will complete a 60-second diagnostic intake (curriculum tier, personality, learning barriers, availability). Our matching engine pairs them with the single best tutor—backed by a 100% “First Lesson Satisfaction Guarantee” (rebooked free if not a fit).

By actively directing demand to a curated, high-performing top quartile of our supply (~1,500 tutors), we solve both sides of the marketplace: students find immediate trust, and active tutors earn dependable income.

#### Allocation of the £1.2m Capital: 1. Product & Matching Tech (£500k): Algorithmic intake flow, automated calendar sync, and in-app instant booking to eliminate messaging back-and-forth. 2. Onboarding & Operations (£300k): Quality vetting, tiering supply, and a proactive concierge team to manually rescue failed first sessions. 3. Targeted Performance Marketing (£400k): Phased acquisition deployed only once the match-to-book conversion rate improves.

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12-Month Execution Roadmap

• Q1–Q2 (Fix the Engine):
• Replace public browse with the guided diagnostic matching flow.
• Restrict active routing to the top 25% highest-rated tutors to concentrate liquidity.
• Launch the "Fit Guarantee" to de-risk the first booking.
• Q3 (Solve the 2nd Booking):
• Automate recurring weekly reservation slots right after lesson one (optimizing for the 64% retention threshold).
• Introduce 4-lesson starter packs to mathematically guarantee lesson two.
• Q4 (Scale What Works):
• Deploy the remaining £400k into intent-driven paid channels (Google Search for GCSE/A-Level revision), scaling into a proven, high-converting funnel.

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Targets & Milestones

Conclusion: Fixing search friction activates the high-retention engine we already possess. By funding matching instead of brute-force acquisition, we reach sustainable unit economics and double our run-rate within our existing 18-month runway.

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.