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 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.