PRIMARY Business Model Canvas — Audio-first Learner Subscription (Tutor Channel)

Thesis

LangListen is a $12/mo learner subscription that turns language learning into a personalized podcast feed, with tutors as a free acquisition channel and credibility source.

Customer Segments

  • Primary (payer + user): Busy adult learners (e.g., “Busy Professional Paula”) learning for personal goals; constrained time; wants “real progress” without high-friction homework.
  • Secondary (channel user, not payer): Online language tutors (esp. “Retention Maximizers”) who want better cross-lesson memory, progress visibility, and time savings.
  • Future: Learners pursuing exams/immigration; learner cohorts in specific languages (Portuguese-heavy early traction).

Value Propositions

  • For learners
    • Personalized, level-appropriate audio lessons/conversations generated from content they care about.
    • Low friction habit: practice while commuting/chores; feels more meaningful than generic gamified drills.
    • “Preserve context”: everything reinforces what they’re currently working on so learning feels efficient.
  • For tutors (channel)
    • A workflow win that helps retention: progress visibility + continuity between lessons.
    • Reviewable AI outputs that save time while maintaining tutor control (no auto-send).

Channels

  • Tutor channel (wedge + interviews): tutors invite learners via free tool usage.
  • Owned list: your existing email list of language-learning leads (including paid-course purchasers).
  • Creator marketing: podcast interviews / newsletter writing.
  • Paid ads: acquisition tests to validate conversion economics.

Customer Relationships

  • Self-serve onboarding: immediate “first value” (generate + play first audio lesson) in minutes.
  • Habit support: weekly goals, “wins,” and lightweight reminders (without making it feel like homework).
  • Tutor-assisted onboarding (optional): tutor invites learner; pre-configured learning plan/curriculum.

Revenue Streams

  • Primary: learner subscription (~$12/mo).
  • Secondary (later): add-on credits for heavy users; possibly higher tier for more generation / faster queue.
  • Not planned: tutor paid plans (explicitly not the near-term model).

Key Resources

  • Product + infrastructure (Django app, generation pipeline, audio playback UX).
  • AI capabilities (transcription + structured generation + TTS).
  • Content pipeline (user-provided audio/text; eventually podcast ingestion).
  • Distribution assets: email list, tutor relationships, creator partnerships.
  • Founder advantage: two years of workflow iteration / dogfooding.

Key Activities

  • Improve the audio-first core loop: upload/select content → generate → listen → reinforce errors/goals → repeat.
  • Build “context + progress” backbone: cross-lesson memory, visible progress, and personalized next steps.
  • Operate the tutor channel funnel: tutor activation → invites → learner activation → conversion.
  • Measure and optimize unit economics: cost per “credit”, gross margin, CAC payback.

Key Partnerships

  • Tutors (as channel and product feedback loop).
  • Platform-adjacent communities (language learning podcasts, newsletters, creator communities).
  • Infrastructure partners: hosting + storage + TTS/LLM providers (cost and reliability).
  • (Later) language-specific communities / schools / cohorts.

Cost Structure

  • AI inference: transcription + generation + TTS (dominant variable cost).
  • Hosting/storage/CDN for media.
  • Paid acquisition spend (ads).
  • Founder time: engineering + customer development + content/marketing.

Assumptions & risks (top 5)

  1. Learner WTP: learners will pay ~$12/mo for audio-first value (not just “nice to have”).
  2. Habit formation: audio feed leads to repeated weekly use (not novelty).
  3. Tutor channel viability: tutors will invite learners and invites activate (not blocked by learner resistance).
  4. Trust/quality: outputs must be good enough that tutors won’t fear reputational harm (reviewable helps but doesn’t solve quality).
  5. Unit economics: credits model must be understandable and margins viable.

Metrics

  • North Star (early): weekly active learners completing ≥X minutes of study audio.
  • Leading indicators
    • time-to-first-audio (minutes)
    • % who generate + play first lesson in first session
    • listening minutes/week, days active/week
    • tutor activation rate → invites per tutor → invited-learner activation rate
  • Lagging
    • free→paid conversion
    • week-4 retention
    • CAC payback period / gross margin

Next tests (2 weeks)

Aligned to the hypotheses + Aleksandra’s deliverable (updated BMC + recommendation memo): - Learner interviews (6–8): run live artifact tests; validate “$12/mo” WTP and whether “podcast feed” fits their week. - Tutor interviews (2–3): test the channel; would they invite? what breaks trust? what’s the 5-minute “win”? - Concept A/B: audio-first vs feed-first (forced choice) and capture why. - Pricing comprehension test: 2–3 packaging cards; confirm “credit” unit clarity.

Pivot triggers

  • If learners want a feed-first experience (reading/listening support) more than audio-first → pivot to Feed-first BMC.
  • If tutor invites don’t activate learners → deemphasize tutor channel and go learner-direct first.
  • If audio doesn’t produce visible progress → double down on context/progress backbone or change modality.