/shopify-admin-churn-risk-scorer

name: shopify-admin-churn-risk-scorer
role: customer-ops
description: "Read-only: scores customers by churn probability based on purchase recency, frequency decay, and expected repurchase intervals."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - customers:query
  - orders:query
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI

Purpose

Predicts which customers are at risk of churning by analyzing their purchase patterns against their historical buying frequency. Calculates an expected next-purchase date for each repeat customer, then scores churn risk based on how overdue they are. Read-only — no mutations.

Prerequisites

• Authenticated Shopify CLI session: `shopify store auth --store <domain> --scopes read_orders,read_customers`
• API scopes: `read_orders`, `read_customers`

Parameters

| Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | store | string | yes | — | Store domain | | days_back | integer | no | 365 | Historical window for purchase pattern analysis | | min_orders | integer | no | 2 | Minimum orders to calculate purchase interval (need 2+ for frequency) | | risk_threshold | float | no | 1.5 | Multiplier of avg purchase interval before flagging as at-risk | | format | string | no | human | Output format: `human` or `json` |

Safety

> ℹ️ Read-only skill — no mutations are executed. Safe to run at any time.

Churn Risk Scoring Model

For each customer with `min_orders` or more purchases:

1. **Average Purchase Interval (API)** = total days between first and last order / (order_count - 1) 2. **Days Since Last Order (DSLO)** = today - last_order_date 3. **Overdue Ratio** = DSLO / API 4. **Churn Risk Score** (0-100):

• Overdue ratio ≤ 1.0 → Score 0-20 (Active)
• Overdue ratio 1.0–1.5 → Score 20-50 (Cooling)
• Overdue ratio 1.5–2.5 → Score 50-80 (At Risk)
• Overdue ratio > 2.5 → Score 80-100 (Likely Churned)

5. **Customer Lifetime Value (CLV)** = total spend / customer age in years × expected remaining years

Workflow Steps

1. **OPERATION:** `orders` — query **Inputs:** `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `createdAt`, `totalPriceSet`, `customer { id, email, firstName, lastName }`, pagination cursor **Expected output:** All orders with customer association

2. Group orders by customer, calculate per customer:

• Order dates (sorted chronologically)
• Average purchase interval
• Days since last order
• Total spend
• Order count

3. **OPERATION:** `customers` — query (enrichment) **Inputs:** Customer IDs for at-risk and likely-churned segments **Expected output:** Contact details, tags, total spend

4. Calculate churn risk score and classify into segments

5. Estimate revenue at risk = sum of (annual_spend × churn_probability) for at-risk customers

GraphQL Operations

# orders:query — validated against api_version 2025-01
query OrdersForChurnAnalysis($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        createdAt
        totalPriceSet { shopMoney { amount currencyCode } }
        customer {
          id
          email
          firstName
          lastName
          numberOfOrders
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

# customers:query — validated against api_version 2025-01
query AtRiskCustomers($ids: [ID!]!) {
  nodes(ids: $ids) {
    ... on Customer {
      id
      email
      firstName
      lastName
      totalSpentV2 { amount currencyCode }
      numberOfOrders
      tags
      createdAt
    }
  }
}

Session Tracking

**Claude MUST emit the following output at each stage. This is mandatory.**

**On start**, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Churn Risk Scorer                    ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

**After each step**, emit:

[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome>

**On completion**, emit:

For `format: human` (default):

══════════════════════════════════════════════
CHURN RISK REPORT  (<days_back> days analyzed)
  Repeat customers scored:  <n>
  ─────────────────────────────
  Active (score 0-20):      <n> (<pct>%)
  Cooling (score 20-50):    <n> (<pct>%)
  At Risk (score 50-80):    <n> (<pct>%)   ⚠️
  Likely Churned (80-100):  <n> (<pct>%)   🔴

  Revenue at risk:         $<amount>/year

  Top at-risk by value:
    <name> (<email>)  Score: <n>  Last order: <date>  Lifetime: $<n>

  Output: churn_risk_<date>.csv
══════════════════════════════════════════════

Output Format

CSV file `churn_risk_<YYYY-MM-DD>.csv` with columns: `customer_id`, `email`, `first_name`, `last_name`, `order_count`, `total_spent`, `avg_purchase_interval_days`, `days_since_last_order`, `overdue_ratio`, `churn_risk_score`, `risk_segment`, `expected_annual_value`

Error Handling

| Error | Cause | Recovery | |-------|-------|----------| | `THROTTLED` | API rate limit exceeded | Wait 2 seconds, retry up to 3 times | | Single-purchase customers | Can't calculate interval | Exclude from scoring (need 2+ orders) | | Guest orders | No customer linkage | Skip — cannot build customer profile |

Best Practices

• Pair with `customer-win-back` skill to take action on At-Risk and Likely Churned segments.
• Use with `rfm-customer-segmentation` for a more holistic view of customer health.
• High-value churning customers (top 20% by spend) should get personalized outreach.
• Export At-Risk segment to email marketing platform for automated win-back sequences.
• Adjust `risk_threshold` based on your product type: consumables (1.3), fashion (1.5), furniture (2.0).