/retention

name: retention-strategy
description: When the user wants to reduce churn, improve customer retention, or plan lifecycle marketing. Also use when the user mentions "retention," "churn," "customer lifecycle," "churn prevention," "at-risk customers," or "loyalty program." For lifecycle, use growth-funnel.
metadata:
  version: 1.1.1

Strategies: Retention

Guides customer retention and churn prevention. Acquiring new customers costs 5–25× more than retaining; 5% retention improvement can increase profitability 25–95%. Use this skill when reducing churn, building retention programs, or identifying at-risk customers.

**When invoking**: On **first use**, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On **subsequent use** or when the user asks to skip, go directly to the main output.

Initial Assessment

**Project context:** Read root `{project}.md` when present and load only the modules relevant to this task. Without Contextus, use available project material or user-provided facts and ask for missing information; do not create a parallel context system.

Identify: 1. **Churn type**: Voluntary (active cancel) vs involuntary (payment failure) 2. **Signals**: Login frequency, feature usage, support tickets 3. **Stage**: Onboarding, expansion, renewal

Churn Types

| Type | Share | Causes | |------|-------|--------| | **Voluntary** | 60–80% | Pricing, missing features, poor onboarding, relationship | | **Involuntary** | 20–40% | Payment failures, expired cards, billing |

**Predictability**: Most churn is predictable 30–90 days before cancellation via behavioral signals.

Proactive vs Reactive

| Approach | Conversion | |----------|------------| | **Reactive** (after cancel) | 15–20% | | **Proactive** (before decision) | 60–80% |

Move from lagging indicator to early warning systems.

Retention Strategies

| Strategy | Use | |----------|-----| | **Health scoring** | Behavioral + transactional + relationship signals | | **Loyalty programs** | 5–15 percentage point retention lift | | **Segmentation** | Predictive modeling for at-risk | | **Onboarding** | Prevent low value realization early | | **Dunning** | Retry logic; pre-expiry card updates for involuntary |

User Value & Feedback

| Dimension | Use | |-----------|-----| | **Product value** | Registration; feature usage; payment | | **Marketing value** | Testimonials; customer stories; webinar guests; feedback, bug reports, feature requests | | **Feedback analysis** | Email, community, reviews—AI-assisted analysis; prioritize by impact; route to product vs ops |

**Avoid**: Treating users only as MAU/registration denominators. See **creator-program** for creator ecosystem.

Lifecycle Integration

Retention occurs after conversion; ongoing investment in customer success, not isolated campaigns. Map touchpoints: onboarding → adoption → expansion → renewal.

Output Format

• **Churn analysis** (voluntary vs involuntary; signals)
• **Retention tactics** (by stage)
• **Health score** framework (if applicable)
• **Intervention** playbook (at-risk triggers)

Related Skills

• **email-marketing**: Onboarding sequences; win-back campaigns
• **pmf-strategy**: Retention as PMF signal; churn as anti-signal
• **cold-start-strategy**: First users; differs from retention
• **analytics-tracking**: Usage data; churn signals
• **traffic-analysis**: Attribution; retention cohort analysis