retention-predictor

<!-- version: 0.1.0 | outputs: memory/ideas/<slug>/retention.json -->

Skill: retention-predictor

Purpose

Retention determines LTV. An app that churns users in week 1 can't build a business regardless of acquisition. This skill evaluates how sticky the idea is structurally — not based on feature lists, but on the underlying usage pattern and habit formation potential.

Input

• Idea slug
• App concept description
• memory/ideas/<slug>/desire_scores.json (desire strength informs habit potential)
• memory/ideas/<slug>/user_extraction.json (usage frequency from pain map)

Evaluation Factors

Factor	High Retention Signal	Low Retention Signal
Usage frequency	Daily or multiple times/day	Weekly or less
External trigger	Clear real-world trigger (meal, workout, payday)	No natural trigger
Progress/reward loop	Clear progress visible over time	No feedback loop
Network effects	Gets better with more users	No network component
Data lock-in	User data accumulates	Nothing to lose by leaving
Habit stack	Fits into existing daily routine	Requires behavior change

Process

<!-- TODO: Add D1/D7/D30 retention benchmark database by app category --> <!-- TODO: Add churn risk scoring rubric -->

1. Estimate natural usage frequency based on the problem (daily tooth brushing vs. annual tax filing).
2. Identify external triggers that would cue app usage.
3. Score habit formation potential (1–5) across the six factors above.
4. Estimate D1, D7, D30 retention benchmarks for the app category.
5. Flag high churn risk factors.

Output

Write to memory/ideas/<slug>/retention.json:

{
  "natural_usage_frequency": "multiple daily | daily | weekly | monthly | infrequent",
  "external_trigger": "",
  "habit_formation_score": 0,
  "churn_risk_factors": [],
  "estimated_retention": {
    "d1": 0,
    "d7": 0,
    "d30": 0
  },
  "churn_risk": "low | medium | high",
  "retention_verdict": "sticky | moderate | disposable"
}

Notes

<!-- TODO: Cross-reference with desire_scores — survival/control desires = higher retention -->