Develop product strategy

MEMORANDUM TO: The Board of Directors FROM: CEO, Tutorly DATE: October 24, 2023 SUBJECT: 12-Month Strategy: Unlocking Liquidity via Guided Matching

1. The Diagnosis: A Conversion Crisis, Not an Acquisition Crisis Tutorly has stalled at 4,000 monthly bookings, generating roughly £30,000 in monthly net revenue. While the Board has suggested deploying our £1.2m capital into paid student acquisition, the data indicates this would be a fatal mistake.

We do not have a top-of-funnel problem; we have a liquidity and matching problem. We currently have 11,000 active students on the platform, yet they generate only 4,000 bookings. Meanwhile, our tutor supply has grown 60% YoY to 6,500, leaving 70% of tutors with zero bookings a month.

Why are 11,000 active students not booking 6,500 available tutors? User research gives us the answer: students are paralyzed by the paradox of choice, citing "finding the right tutor" as their top barrier. Tutors think there aren't enough students, when in reality, bloated supply is actively hurting student conversion. Pouring £1.2m of paid marketing into a leaky bucket will only burn our 18-month runway faster.

However, our bright spot is retention: when a student books twice, their repeat rate is 64%. Our core strategic challenge is getting existing students from browsing to their second booking.

2. Our One Major Bet: "Tutorly Match" & Supply Curation Instead of buying traffic, we will invest our £1.2m in product-led conversion. We will pivot Tutorly from an open directory (like Craigslist) to a curated, algorithmically matched marketplace (like Uber).

Over the next 12 months, we will execute a three-part strategy:

• Aggressive Supply Curation: We will freeze new tutor onboarding and delist the bottom 50% of inactive/low-converting tutors. By concentrating demand among the top 2,000 tutors, we instantly improve the average quality of a student’s first lesson and keep our best tutors earning and engaged.
• The "Concierge" Matching Engine: We will replace our search bar with a diagnostic intake flow. Students will input their exam board, target grade, and learning style, and our algorithm will present only three ideal tutors.
• The "Two-Lesson" Guarantee: Because a second booking triggers a 64% repeat rate, we will subsidize the friction of trial-and-error. If a student doesn’t click with their first matched tutor, Tutorly will fund the first lesson with a replacement tutor.

3. Capital Allocation (£1.2m) * £650k (Product & Data): Hiring a dedicated squad (PM, Data Scientist, 3 Engineers) to build the matching engine, diagnostic onboarding, and a revamped tutor rating system based on student retention. * £350k (The "Second Booking" Subsidy): Financing the "First-Match Guarantee" and discounting a bundled "First Two Lessons" package to force the habit loop. * £200k (Lifecycle Marketing): Automated CRM flows (SMS/Email) to re-engage the ~7,000 dormant students currently on our platform.

4. 12-Month Success Metrics By fixing the conversion bottleneck first, we build a machine that can profitably scale paid acquisition in Year 2. By Q4 next year, this bet will deliver: 1. Bookings: Increase from 4,000 to 12,000/month (tripling net revenue to ~£90k/month). 2. Student Activation: Increase the percentage of active students reaching their 2nd booking from ~15% to 40%. 3. Marketplace Health: Reduce the "zero-booking" tutor rate from 70% to under 20% (on a curated base of 2,500 tutors).

Tutorly: 12-month strategy — make every good match count

Recommendation: Put our £1.2m behind improving the first tutor match, not broad paid acquisition. Tutor supply is growing far faster than bookings, 70% of tutors get no booking in a typical month, and students say finding the right tutor is their main problem. Buying more student traffic before fixing that experience risks paying to send more families into the same frustration. The encouraging signal is that students who book again show a 64% repeat rate: when a match works, there is reason to believe students stay.

The bet: a guided matching and first-booking service

Build a faster, more trusted path from a student’s need to a first lesson. Ask about subject, exam level, goals, budget, availability and preferred teaching style; then show a short, ranked list of tutors who are available and suitable—not a directory students must search alone. Make tutor profiles clearer on relevant experience and approach, and prompt tutors to respond quickly. For students who still struggle, offer human-assisted matching.

Start with the subjects and exam levels that already have the strongest demand and enough suitable tutors. Use actual enquiries, bookings, repeat behaviour and student feedback to improve recommendations. This is one integrated liquidity bet: make existing demand easier to serve with existing supply, rather than adding supply or buying broad traffic.

How we will execute

In the first 90 days, analyse the booking funnel and pilot assisted matching in a few high-demand subject/exam segments. Compare it with the current journey on first booking, time to match, cancellations and student satisfaction. In months 4–6, automate the most successful parts and improve availability and profile information. In months 7–12, expand only where the pilot improves bookings without harming quality. Help tutors without bookings improve their profiles or availability; stop actively promoting tutors who are unavailable or consistently unresponsive.

Allocate the £1.2m across the year: £450k product and data, £300k matching and student support, £250k tutor onboarding and quality, £100k testing and measurement, and £100k contingency. Release funding in stages, with a formal review after the pilot.¹

What success looks like

By month 12, target 6,000 monthly bookings (up from 4,000) and at least 45% of active tutors receiving a booking in a typical month (up from 30%). Also improve first-booking conversion and time to first lesson, while maintaining or improving repeat booking, cancellation and satisfaction rates. Report results by subject and exam level so growth cannot hide weak or overloaded segments.

Hold broad paid acquisition for now. Run only small, measurable acquisition tests in segments where matching is working; scale spend only if the resulting students book and repeat at acceptable acquisition cost. If the pilot fails to improve first bookings, stop and reassess before committing the remaining funds. This gives the board a measurable growth bet while protecting our 18-month runway.