Free Tier & Conversion Hypothesis (Tutor-as-Channel)

Hypothesis (falsifiable)

A free tier for tutors (and lightweight learner use) will increase access to learners for interviews and reduce acquisition friction, while still enabling sustainable conversion to paid (~$12/mo) once learners experience value.

Why we believe it (evidence so far)

  • Tutor-side adoption is gated by trust + time savings + control; free reduces risk to try.
  • Most tutors on the platforms will typically not pay for a SaaS subscription even if the tool improves retention.
  • Platform economics and retention pressure make tutors seek leverage, but they’re sensitive to added workload.

Supporting quotes (interviews)

  • Platform economics create pressure for retention efficiency:
    • “Por causa do Italki, é mais importante manter os alunos, porque se eu mantenho alunos o algoritmo do Italki me dá novos estudantes.” — “Because of italki, it’s more important to keep students, because if I keep students the italki algorithm gives me new students.”
    • “Depois das mudanças do Italki, é melhor manter um aluno…” — “After italki’s changes, it’s better to retain a student…”
  • Free must not create more unpaid work:
    • “Com certeza [seria útil]… personalizar seria incrível, mas eu não deixaria enviar automaticamente sem eu analisar… seria um divisor de águas pra minha rotina.” — “For sure [it would be useful]… personalization would be incredible, but I wouldn’t let it send automatically without reviewing it… it would be a game changer for my routine.”

Tensions / counterevidence

  • Learners may resist extra tools:
    • “Todos os alunos que eu perguntei quer que eu mande alguma coisa? Não… Não gosta. Só quer conversar, só quer falar.” — “Every student I asked, ‘Do you want me to send you something?’ No… they don’t like it. They just want to chat, just want to speak.”
  • Tutors won’t risk reputation on low-quality output; free does not solve the trust problem.

What must be true in the first 5 minutes

  • Tutor can try the workflow and immediately see:
    • “I save time,” and
    • “I stay in control (review/edit),” and
    • “This won’t embarrass me with students.”
  • Learner invited by tutor can immediately play/consume something valuable with zero setup.

Metrics

  • Tutor activation: % tutors who complete a “first value” action (e.g., generate a post-lesson summary draft) in first session.
  • Invitation rate: invites per activated tutor.
  • Learner activation: % invited learners who complete first value action.
  • Conversion: invited learners converting to paid within 30 days (or after credits run out).

Fastest tests (2-week sprint)

  • 3–5 tutor interviews focused on: “Would you invite a student? Under what conditions?”
  • 5–8 learner interviews that begin with a tutor-style invite flow (even if simulated).
  • Trial offer tests:
    • free tutor account + limited student seats
    • free learner trial credits via tutor invite

Decision rule (double down / pivot / kill)

  • Double down if tutor activation is strong and a meaningful portion of invites lead to learner activation (signal: users actually do something, not just say “cool”).
  • Pivot if tutors like it but won’t invite learners, or if invited learners don’t activate.
  • Kill if free tier drives usage but conversion is structurally blocked (pricing confusion, low perceived value, or high friction).