Develop product strategy

Tutorly: 12-Month Strategy Board paper, for decision

The problem Bookings have grown 8% while tutor supply grew 60%. We are not short of tutors or students. We are failing to connect them. Students say their top problem is finding the right tutor. Tutors say they lack students, and 70% get no bookings in a typical month. Once a student books twice, 64% keep booking, so the product works when a match happens. The leak is in the first match.

Why not paid student acquisition The board's instinct is to buy more students. I recommend against it for now: - Paid students would arrive at the same broken search and matching flow that already frustrates them. We would be paying to acquire customers we are likely to lose before their second booking. - Our tutor base is already larger than our demand. More students would mainly bring more tutors into the same competition for the few bookings available. - Acquisition spend scales with cost. Matching improvements scale with use.

The bet: fix first-match conversion (£1.2m) Over 12 months we will build a guided matching system and test it against the current search:

1. Structured intake (Months 1–4). Students give subject, exam board, year group, goals, budget, and availability. Required before search.
2. Curated shortlists (Months 2–6). Within one hour, students receive three ranked tutors with a free 15-minute intro call. A small concierge team handles hard cases such as niche subjects and last-minute exam prep.
3. Ranking that rewards fit and responsiveness (Months 3–8). Ranking weighs subject expertise, response time, and student outcomes. New tutors get guaranteed early exposure so that supply does not stall at the bottom.
4. Tutor activation (Months 4–12). Tutors with zero bookings in 30 days receive coaching on profile quality and availability, plus targeted introductions.

Budget (£1.2m) - Product and engineering: £600k - Concierge matching team: £300k - Tutor activation and targeted student tests: £200k - Measurement and contingency: £100k

Targets (12 months) - Bookings: 4,000 → 5,500 per month - Students with a first booking within 7 days of intake: baseline in Month 1, target +50% - Share of tutors with at least one booking per month: ~30% → 45% - Repeat rate (second booking): hold at or above 64%

Decision gates - Month 3: Intake and shortlist live for a test cohort. Confirm the conversion baseline and the uplift. - Month 6: If first-match conversion has not improved by at least 20% against control, stop further build and move remaining funds to a small, controlled paid-acquisition test. If it has, scale the concierge model and begin targeted acquisition in segments where our supply is deepest. - Month 9: Review unit economics. Each booking should contribute enough commission to cover concierge cost.

Runway This plan spends £1.2m over 12 months and leaves about six months of runway for the gates above. If the Month 6 gate fails, we retain the option to pivot without running out of cash.

What we are asking the board to approve 1. Fund the £1.2m matching-led plan. 2. Approve the Month 6 gate and the reallocation rule if it fails. 3. Defer paid student acquisition until conversion data supports it.

The board's instinct is right that growth needs more students. But we already have more tutors than we can match, and the evidence points to the match as the bottleneck. Fixing that first makes every future pound of acquisition work harder.

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.