19  Alternative Business Model Canvas — Tutor Workflow Tool (Learner Conversion Secondary)

19.1 Thesis

LangListen is a tutor-centric workflow tool that saves prep time and improves retention by providing cross-lesson memory and progress visibility; learner subscription is a second-order outcome (via tutor-invited learners).

19.2 Customer Segments

  • Primary user: Tutors (esp. “Retention Maximizers”) who already invest unpaid time in personalization and tracking.
  • Secondary user/payer (later): Learners who benefit from the tutor’s curriculum and want between-lesson practice.

19.3 Value Propositions

  • For tutors
    • “Your memory—automated”: summaries, recurring errors, goals, and next steps across lessons.
    • Reviewable AI drafts for feedback, lesson plans, and homework (no auto-send).
    • Retention leverage: makes progress visible and the learning journey coherent.
  • For learners
    • Tutor-aligned personalized practice (often audio) that fits into daily life.

19.4 Channels

  • Tutor communities (italki/Preply/Verbling tutor groups, creator-tutors).
  • Referrals: tutors invite other tutors; tutors invite learners.

19.5 Customer Relationships

  • High-touch early (because workflow change + trust).
  • Templates for common tutor workflows (beginner planning, post-lesson feedback, progress dashboards).

19.6 Revenue Streams (two variants to decide)

  • Variant A (still learner-pays): tutors free; learners pay; tutors act as channel.
  • Variant B (tutor-pays): tutors pay a monthly fee for time-saving tooling (risk: contradicts your current strategy).

This BMC assumes Variant A is still preferred, but reframes value delivery as “tutor tool first.”

19.7 Key Resources

  • Tutor workflow UX (editing, approval, export/sharing).
  • Structured generation prompts and quality controls.
  • Trust-building artifacts (examples, templates, human-in-the-loop).

19.8 Key Activities

  • Build a “5-minute wow” tutor workflow (summary + errors + next practice).
  • Ensure output quality and controllability (avoid reputational harm).
  • Measure retention effects and time saved.

19.9 Key Partnerships

  • Tutors (design partners).
  • Integrations: calendar, docs/export, (maybe) classroom tooling.

19.10 Cost Structure

  • AI inference costs (generation + transcription).
  • Support/onboarding time early.
  • Minimal paid acquisition early; relies on tutor community/channel.

19.11 Assumptions & risks (top 5)

  1. Tutors will adopt a new tool despite platform constraints and workflow inertia.
  2. Output quality is high enough to protect tutor reputation.
  3. Tutor value is strong enough to drive invitations to learners.
  4. Learners will accept “another tool” when invited (big risk).
  5. If we ever switch to tutor-pays, we risk misalignment with original wedge.

19.12 Metrics

  • Leading: tutor activation rate; time saved (self-reported); invites per tutor.
  • Lagging: invited learner activation; subscription conversion; tutor retention (continued weekly use).

19.13 Next tests (2 weeks)

  • 3–5 tutor workflow interviews (live artifact test).
  • Ask “would you invite a student?” and “what would break trust?”
  • Run one learner interview per tutor interview using the invite framing.

19.14 Pivot triggers

  • If tutor adoption is strong but learner conversion is weak → stay tutor tool, change learner offer, or drop learner conversion assumption.
  • If tutors refuse to adopt due to platform constraints → move to learner-direct model.