Launch and Learning Ops

Intent

• Treat every launch (soft, region, platform, season) as a scientific learning loop.
• Synchronize comms, community, growth, and product telemetry to reach PMF faster.

Inputs

1. Target segments + messaging hierarchy.
2. Channel plan (owned, earned, paid, influencer, platform features, on-chain activations).
3. Experiment tracking template + analytics stack.

Workflow

1. Hypothesis-driven launch plan

- Define explicit hypotheses for acquisition, activation, and retention per cohort. - Map leading indicators and success/fail guardrails.

1. Sequential rollout design

- Stage launches (friends & family → closed beta → open beta → public) with clear exit criteria. - Prepare rollback + comms contingencies for each stage.

1. Execution war room

- Establish daily/weekly rhythm: signal review, issue triage, community feedback digestion. - Document decisions and pivots in a shared log.

1. Learning harvest & handoff

- Produce launch retros with metric deltas, qualitative feedback, and next experiments. - Update strategic roadmap / PMF scorecard accordingly.

Verification

• Launch brief, dashboard links, and experiment log stored in shared space before kickoff.
• Guardrails monitored in near real time; incident response plan tested.
• Retrospective completed within one week of stage completion with owners for next steps.