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).