/digital-marketing-pro:churn-risk

Purpose

Assess churn risk across customer segments and generate intervention strategies. Score segments using behavioral signals — email engagement decline, purchase frequency drops, login pattern changes, support ticket escalations — to categorize each segment into risk tiers and produce actionable intervention playbooks. This command bridges the gap between knowing customers are churning and knowing what to do about it. Instead of reactive "win-back" campaigns after customers have already left, it identifies at-risk segments early enough to intervene while the relationship is still recoverable. Each intervention playbook includes specific actions, timing windows, channel recommendations, and messaging approaches calibrated to the risk tier and customer value.

Input Required

The user must provide (or will be prompted for):

• Customer segments to score: The segments to evaluate — can be predefined CRM segments (e.g., "Enterprise accounts," "Monthly subscribers," "First-time buyers") or behavioral cohorts (e.g., "Users who haven't purchased in 60 days," "Users with declining email opens"). Each segment should include available behavioral signals: email engagement trends (open rate, click rate, unsubscribe rate over time), purchase frequency and recency, login or product usage patterns, support ticket volume and sentiment, and any other engagement indicators tracked in the CRM
• CRM data source: Which CRM system holds the customer data — Salesforce, HubSpot, or another connected CRM MCP. The command will pull behavioral data directly from the CRM if connected, or the user can provide exported data
• Intervention budget (optional): Total budget available for retention interventions — used to prioritize which segments and actions to focus on based on LTV-at-risk versus intervention cost. If not provided, all recommendations are generated without budget filtering
• Lookback period (optional): How far back to analyze behavioral trends — defaults to 90 days. Shorter windows catch rapid deterioration, longer windows identify slow-burn churn patterns
• Custom churn signals (optional): Brand-specific behavioral indicators beyond the defaults — e.g., "stopped using feature X," "downgraded plan tier," "removed payment method," "decreased order size" — that have historically preceded churn for this brand

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply customer lifecycle data, historical churn rates, known retention patterns, and industry benchmarks. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load any communication frequency limits or channel restrictions that constrain intervention options. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with industry defaults.