--- name: shopify-admin-customer-win-back role: marketing description: "Identify customers who have not ordered in N days, export a re-engagement list, and tag them in Shopify." toolkit: shopify-admin, shopify-admin-execution api_version: "2025-01" graphql_operations: - customers:query - tagsAdd:mutation status: stable compatibility: Claude Code, Cursor, Codex, Gemini CLI --- ## Purpose Segments lapsed customers — those who placed at least one order but have not purchased again within a configurable window — and tags them for re-engagement. This skill handles the Shopify-native data layer; sending re-engagement emails requires an external tool. ## Prerequisites - Authenticated Shopify CLI session: `shopify auth login --store ` - API scopes: `read_customers`, `write_customers` ## Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | store | string | yes | — | Store domain | | format | string | no | human | `human` or `json` | | dry_run | bool | no | false | Preview without tagging | | inactive_days | integer | no | 90 | Days since last order to qualify as lapsed | | min_orders | integer | no | 1 | Minimum lifetime order count to include | | tag | string | no | win-back | Tag applied to lapsed customers | | max_customers | integer | no | 500 | Maximum customers to process per run | ## Workflow Steps 1. **OPERATION:** `customers` — query **Inputs:** filter `last_order_date:<(NOW - inactive_days days)`, `orders_count:>=(min_orders)`, `first: 250`, pagination **Expected output:** List of customer objects with `id`, `defaultEmailAddress { emailAddress }`, `firstName`, `lastName`, `ordersCount`, `lastOrder.processedAt`; paginate until `hasNextPage: false` 2. **OPERATION:** `tagsAdd` — mutation **Inputs:** Customer `id`, tag string from `tag` parameter **Expected output:** Confirmation per customer; collect `userErrors` ## GraphQL Operations ```graphql # customers:query — validated against api_version 2025-04 query LapsedCustomers($first: Int!, $after: String, $query: String) { customers(first: $first, after: $after, query: $query) { edges { node { id defaultEmailAddress { emailAddress } firstName lastName ordersCount lastOrder { processedAt totalPriceSet { shopMoney { amount currencyCode } } } } } pageInfo { hasNextPage endCursor } } } ``` ```graphql # tagsAdd:mutation — validated against api_version 2025-01 mutation TagsAdd($id: ID!, $tags: [String!]!) { tagsAdd(id: $id, tags: $tags) { node { id } userErrors { field message } } } ``` ## Session Tracking **Claude MUST emit the following output at each stage. This is mandatory.** **On start**, emit: ``` ╔══════════════════════════════════════════════╗ ║ SKILL: Customer Win-Back ║ ║ Store: ║ ║ Started: ║ ╚══════════════════════════════════════════════╝ ``` **After each step**, emit: ``` [N/TOTAL] → Params: → Result: ``` If `dry_run: true`, prefix mutation steps with `[DRY RUN]` and do not execute. **On completion**, for `format: human`: ``` ══════════════════════════════════════════════ OUTCOME SUMMARY Lapsed customers found: Customers tagged: Errors: Output: winback_.csv ══════════════════════════════════════════════ ``` For `format: json`, emit the standard JSON schema with `outcome` keys: `lapsed_found`, `customers_tagged`, `errors`, `output_file`. ## Output Format CSV `winback_.csv` with columns: `customer_id`, `email`, `first_name`, `last_name`, `orders_count`, `last_order_date`, `tag_applied` ## Error Handling | Error | Cause | Recovery | |-------|-------|----------| | `THROTTLED` | Rate limit | Wait 2s, retry up to 3 times | | `userErrors` on tagsAdd | Customer not found or invalid ID | Log, skip, continue | ## Best Practices - Use a dated tag (e.g., `win-back-2026-04`) so you can track which cohort was targeted each month and avoid re-tagging customers who already received a win-back campaign. - Set `min_orders: 2` to focus on customers who had a genuine purchase relationship, not one-time buyers who may never have intended to return. - Run with `dry_run: true` first to validate the lapsed customer count before tagging — the count informs the scale of your re-engagement campaign.