peopleanalyst

Tools · Marketing

Retention Plan

Describe your churn situation — get a lifecycle retention plan.

How it works

Corpus-grounded (lifecycle/retention/loyalty via the marketing cluster). Maps lifecycle stages with goals + tactics, diagnoses the real churn drivers, names the highest-leverage retention levers (usually time-to-value), suggests loyalty mechanics where they fit, and sets the cohort/churn metrics.

You bring

{ context, cluster? }

You get

{ context_summary, lifecycle_stages[]{stage, goal, tactics[]}, churn_drivers[], retention_levers[], loyalty_mechanics[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }

Use it for

See it work

example output

Context: a meditation app with strong installs but ~70% of new users churning within 30 days, mostly before building a daily habit.

Retention Plan — meditation app (30-day churn problem)

Installs are healthy; the leak is early. Most new users churn before meditation becomes a habit, which points the whole plan at one lever: time-to-habit, not feature breadth.

Lifecycle stages

Onboarding (days 0–3) · Goal: one complete session + a set reminder.

  • Tactics: a 3-minute first session (not a 20-minute commitment); ask for a reminder time on day 0; celebrate session #1.

Habit (days 4–21) · Goal: cross the streak threshold where the habit sticks.

  • Tactics: streak mechanic with a forgiving "don't break the chain" reset; smart reminders at the user's chosen time; surface "you've meditated 5 days — here's what changes next."

Expansion (days 22–60) · Goal: deepen, not just sustain.

  • Tactics: unlock themed series once the habit holds; introduce sleep + focus use-cases beyond stress.

Win-back (lapsed) · Goal: re-activate the recently churned.

  • Tactics: a low-pressure "come back for 2 minutes" nudge; restore the streak as a one-time welcome-back.

Churn drivers

  • The first session feels like a chore (too long, too abstract).
  • No reminder set → the app is out of sight by day 3.
  • Value (calmer days, better sleep) isn't felt fast enough to justify the daily open.

Retention levers (highest first)

  1. Time-to-habit — get to a set reminder + a 3-day streak fast; this is the whole game.
  2. Felt early value — reflect mood/sleep change back to the user by week one.
  3. Forgiving streaks — a single miss shouldn't end the relationship.

Loyalty mechanics

  • Streaks with a monthly "milestone" unlock; an optional buddy/accountability pairing for the socially motivated.

Success metrics

  • Day-7 and day-30 retention; % who set a reminder on day 0; median days-to-first-streak; reactivation rate of win-back nudges.

Riskiest assumptions

  • That early churn is a habit-formation problem, not a content-fit problem — validate with exit surveys before over-investing in streaks.

Grounded in: lifecycle/retention + loyalty canon (marketing cluster).

Run it now

Build a retention plan

Get a lifecycle retention plan: stages with goals and tactics, the real churn drivers, the highest-leverage retention levers, loyalty mechanics, and the metrics to watch.

Prefer code? Call it over the API or hand it to your AI agent via MCP — POST /api/bicycle/retention-plan · build_retention_plan. API & agent access →

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