churn-risk

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Score customer segments for churn risk from behavioral signals — email engagement decline, purchase recency, usage drops, support sentiment — producing a 0-100 risk scorecard with four tiers, per-tier intervention playbooks (actions, timing windows, channels, messaging), LTV-at-risk totals, and retention-ROI prioritization. Assesses and recommends; it does not send outreach or launch campaigns. Triggers on "/digital-marketing-pro:churn-risk", "which customers are about to churn", "score our segments for churn risk", "email engagement is dropping, who is at risk", "build a retention intervention plan". Pulls behavioral data from a connected CRM MCP (Salesforce or HubSpot) or user-provided exports, runs scripts/churn-predictor.py, and reads the brand profile for lifecycle context.

AI & Automation 809 stars 134 forks Updated 4 days ago MIT

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Skill Content

# /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 c...

Details

Author
indranilbanerjee
Repository
indranilbanerjee/digital-marketing-pro
Created
7 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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churn-risk

Score customer segments for churn risk from behavioral signals — email engagement decline, purchase recency, usage drops, support sentiment — producing a 0-100 risk scorecard with four tiers, per-tier intervention playbooks (actions, timing windows, channels, messaging), LTV-at-risk totals, and retention-ROI prioritization. Assesses and recommends; it does not send outreach or launch campaigns. Triggers on "/digital-marketing-pro:churn-risk", "which customers are about to churn", "score our segments for churn risk", "email engagement is dropping, who is at risk", "build a retention intervention plan". Pulls behavioral data from a connected CRM MCP (Salesforce or HubSpot) or user-provided exports, runs scripts/churn-predictor.py, and reads the brand profile for lifecycle context.

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Identify at-risk customer accounts by analyzing usage patterns, engagement signals, and support history to generate churn risk scores and intervention recommendations. Use when the user requests churn analysis or provides relevant inputs for this workflow.

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