Needs more than a quick edit3 of 10 checks passed
A PM would need to remove invented current-state claims and add a hard decision gate and explicit sacrifice before using it. The reasoning is directionally strong but not evidence-safe as written.
Output
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
| Metric | Current | 12-Month Target |
|---|---|---|
| :--- | :--- | :--- |
| Monthly Bookings | 4,000 | 10,000+ |
| Search-to-First-Booking CVR | Baseline | +40% |
| % Tutors with ≥1 Booking/mo | 30% | 65% (of active tier) |
| Annualized Net Revenue (20%) | £360k | £900k+ |
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.
Claims the judge couldn’t find in the brief
- Tutorly currently operates an open-directory model in which parents sift through 6,500 profiles.
- The current model causes decision paralysis and drop-off.
- The problem is a liquidity and conversion bottleneck, not an awareness problem.
- Paid traffic into an unguided directory will burn £1.2m on high CAC and low conversion.
- There is a high-performing top quartile of supply of about 1,500 tutors.
Grades and run details
Decision model 65 · LLM judge 3 of 11 checks
Decision model checks
- failUses the supplied evidence correctly76%
- passAddresses the actual decision69%
- passRespects explicit constraints40%
- partialIdentifies material uncertainty42%
- failAvoids unsupported claims85%
- passProduces the required deliverable67%
- passSurfaces the central contradiction100%
- passMakes a clear choice95%
- passDiagnosis before prescription100%
- failProposes tests that could fail28%
Run
- Run
- #1
- API response time
- 16 s
- Submitted
- 1 Oct 2026