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 for the Board

Decision requested: Back one major bet: a guided matching service that helps students find the right tutor and converts successful introductions into repeat lessons. Commit up to £1.2m, released against evidence. Do not make broad paid acquisition the primary strategy yet.

Why this bet

Tutorly’s constraint appears to be successful matching, not tutor availability. Tutor supply grew 60% year on year, while bookings grew just 8%; 70% of tutors receive no bookings in a typical month. Adding more supply will not solve this imbalance.

Students identify “finding the right tutor” as their main problem. The 64% repeat booking rate among students who book twice suggests value after a relationship forms—but excludes students who never reach a second lesson. It does not establish strong retention across the whole marketplace.

The board’s acquisition thesis may be right: insufficient student demand could explain underutilisation. But buying more traffic before testing the matching bottleneck risks paying to reproduce today’s disappointing conversion. At £6–9 commission per lesson, acquisition economics require sustained repeat bookings.

The major bet: guided matching

Replace open-ended tutor browsing as the default journey with a short intake covering subject, exam board, goals, budget, availability and learning preferences. Present three suitable, genuinely available tutors, explain each recommendation and make the first booking straightforward.

Start with human-assisted matching rather than a large algorithm build. Use this service to learn what predicts a successful relationship, then automate proven steps. After the first lesson, collect fit feedback, offer a quick rematch where necessary and prompt a recurring booking where appropriate.

Concentrate initially on the highest-volume subject and exam-board segments identified in our data. Pause broad tutor recruitment; prioritise responsive existing tutors with relevant availability. Success means productive student–tutor relationships, not equal booking distribution across all 6,500 tutors.

Execution and investment

Months 1–3: Diagnose and test. Establish funnel baselines from student arrival through first, second and subsequent lessons. Run a controlled experiment comparing guided matching with today’s journey, using comparable student cohorts. Measure tutor responsiveness and matching-service cost.

Months 4–6: Prove repeatability. Expand only if the pilot improves completed bookings and repeat behaviour—not merely enquiry volume. Refine recommendations, automate intake and scheduling, and establish contribution economics after matching support costs.

Months 7–12: Scale the winner. Roll out proven workflows across suitable segments. Increase paid student acquisition only where matched cohorts demonstrate viable acquisition payback, turning acquisition into distribution for the same bet.

Allocate £450k to product and engineering, £350k to matching operations, £150k to research and measurement, and £250k as a gated scaling reserve. Review spending quarterly against the remaining runway.

Board scorecard and stop rules

Set initial targets of 25% higher first-booking conversion and 20% higher second-booking conversion versus control, with no deterioration in cancellations or lesson satisfaction. Track 90-day bookings and contribution per student, including acquisition and service costs.

Aim to reach 6,000 monthly bookings by month 12, as a planning target rather than a forecast. If the six-month evidence shows no meaningful improvement or an uneconomic service cost, stop expansion and preserve capital.

Strategic principle: Prove that Tutorly can reliably turn student demand into lasting tutoring relationships before paying substantially more to generate that demand.