churn-signal-detector

Churn Signal Detector

Prerequisites

This skill needs the iGPT MCP at https://mcp.igpt.ai/.

If the MCP tools aren't available or return an auth error, tell the user to install the iGPT plugin (/plugin marketplace add igptai/skills) or add https://mcp.igpt.ai/ as a connector, then complete OAuth and say "ready". Retry once after they confirm. Never invent tokens or OAuth URLs. For deeper troubleshooting: https://raw.githubusercontent.com/igptai/skills/main/shared/mcp-guard.md

What This Skill Does

Scans all customer email threads for the language and behavioral patterns that precede churn — dissatisfaction signals, complaints going unresolved, reduced engagement, competitor mentions, and anything suggesting a customer's commitment to the relationship is weakening.

Workflow

1. Before calling any tool, collect these values from the user. Offer the defaults and let the user override them; do not invent values they did not give.

   • [time_range] — what window of email to scan. The user may give this in any form ("last 90 days", "the last 3 months", "May 2024", "since the last QBR"). Default: the last 90 days. Keep the user's natural phrasing for use in the ask input; convert to ISO dates separately for the search call.
   • [account_scope] — either "all" (default) or the name of a specific customer account to focus on.
   • [account_clause] — derived. When [account_scope] is not "all", set to " for account [account_scope]". When [account_scope] is "all", set to empty string.

2. Call search with:

   • query: unhappy frustrated cancel disappointed alternative competitor considering switching not working not happy (if [account_scope] is not "all", append the account name to the query)
   • date_from: ISO start date derived from [time_range]
   • date_to: ISO end date derived from [time_range] (or today if open-ended)

3. Call ask with:

   • input: Review all customer email threads from [time_range][account_clause]. Identify every churn signal across all accounts — dissatisfaction language, unresolved complaints, mentions of competitors or alternatives, reduced response frequency, requests for cancellation information, escalating frustration, and any pattern where a customer's engagement or satisfaction appears to be declining. For each signal note the customer, the type of signal, the evidence, and how serious the churn risk appears.
   • output_format: { "strict": true, "schema": { "type": "object", "description": "Churn risk report across the customer base", "additionalProperties": false, "properties": { "as_of": { "type": "string", "description": "ISO8601 date when this report was generated" }, "churn_signals": { "type": "array", "description": "List of every churn signal detected across customer email threads", "items": { "type": "object", "description": "A single churn signal for a specific customer", "additionalProperties": false, "properties": { "customer": { "type": "string", "description": "Name of the customer company showing this churn signal" }, "contact": { "type": "string", "description": "Name or role of the customer contact who expressed this signal" }, "signal_type": { "type": "string", "description": "Category of churn signal", "enum": [ "dissatisfaction_language", "unresolved_complaint", "competitor_mention", "cancellation_inquiry", "reduced_engagement", "escalating_frustration", "feature_gap_complaint", "value_question", "other" ] }, "evidence": { "type": "string", "description": "Quote or paraphrase from email that surfaces this churn signal" }, "date": { "type": "string", "description": "ISO8601 date when this signal appeared" }, "churn_risk": { "type": "string", "description": "Assessment of how likely this customer is to churn based on signals found", "enum": ["critical", "high", "medium", "low"] }, "recommended_action": { "type": "string", "description": "Recommended immediate action to reduce churn risk for this customer" } }, "required": [ "customer", "contact", "signal_type", "evidence", "date", "churn_risk", "recommended_action" ] } }, "at_risk_customers": { "type": "array", "description": "Deduplicated list of customers with at least one churn signal, ordered by risk", "items": { "type": "object", "description": "A single at-risk customer with aggregated risk assessment", "additionalProperties": false, "properties": { "customer": { "type": "string", "description": "Name of the customer company" }, "highest_risk_level": { "type": "string", "description": "The highest churn risk level found for this customer", "enum": ["critical", "high", "medium", "low"] }, "signal_count": { "type": "number", "description": "Total number of churn signals detected for this customer" }, "primary_concern": { "type": "string", "description": "The main reason this customer appears at risk in one sentence" } }, "required": ["customer", "highest_risk_level", "signal_count", "primary_concern"] } }, "critical_count": { "type": "number", "description": "Number of customers with critical churn risk" }, "summary": { "type": "string", "description": "One or two sentence summary of overall churn risk across the customer base" } }, "required": [ "as_of", "churn_signals", "at_risk_customers", "critical_count", "summary" ] } }

4. Present at-risk customers ordered by risk level first, then the detailed signals. Lead with critical count and the most at-risk account.

5. Ask: "Would you like me to draft a rescue outreach email for any of these customers?"