From kostja94-marketing-skills-5
Guides retention strategies for reducing churn via health scoring, segmentation, onboarding, dunning, and lifecycle tactics. Use for SaaS customer lifecycle planning.
npx claudepluginhub joshuarweaver/cascade-data-analytics --plugin kostja94-marketing-skills-5This skill uses the workspace's default tool permissions.
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.
Conducts multi-round deep research on GitHub repos via API and web searches, generating markdown reports with executive summaries, timelines, metrics, and Mermaid diagrams.
Dynamically discovers and combines enabled skills into cohesive, unexpected delightful experiences like interactive HTML or themed artifacts. Activates on 'surprise me', inspiration, or boredom cues.
Generates images from structured JSON prompts via Python script execution. Supports reference images and aspect ratios for characters, scenes, products, visuals.
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.
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read Sections 4 (Audience), 9 (Documentation).
Identify:
| 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.
| Approach | Conversion |
|---|---|
| Reactive (after cancel) | 15–20% |
| Proactive (before decision) | 60–80% |
Move from lagging indicator to early warning systems.
| 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 |
| 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.
Retention occurs after conversion; ongoing investment in customer success, not isolated campaigns. Map touchpoints: onboarding → adoption → expansion → renewal.