tjboudreaux 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.