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: 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.