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
- •Target segments + messaging hierarchy.
- •Channel plan (owned, earned, paid, influencer, platform features, on-chain activations).
- •Experiment tracking template + analytics stack.
Workflow
- •Hypothesis-driven launch plan
- •Define explicit hypotheses for acquisition, activation, and retention per cohort.
- •Map leading indicators and success/fail guardrails.
- •Sequential rollout design
- •Stage launches (friends & family → closed beta → open beta → public) with clear exit criteria.
- •Prepare rollback + comms contingencies for each stage.
- •Execution war room
- •Establish daily/weekly rhythm: signal review, issue triage, community feedback digestion.
- •Document decisions and pivots in a shared log.
- •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.