Customer Win Back

Purpose

Identify WooCommerce customers who have not placed an order in a configurable number of days and export them with their last purchase details for re-engagement campaigns. Segments by LTV tier to prioritize high-value lapsed customers. Read-only.

Prerequisites

• WooCommerce store with REST API enabled
• Consumer Key with Read scope
• Minimum WooCommerce version: 3.5.0

Parameters

Authentication

WooCommerce uses OAuth 1.0a for HTTP and Basic Auth over HTTPS.

For HTTPS stores (recommended):

Authorization: Basic base64(consumer_key:consumer_secret)

For HTTP stores (development only): Use OAuth 1.0a — include oauth_consumer_key, oauth_nonce, oauth_signature, oauth_signature_method=HMAC-SHA1, oauth_timestamp, oauth_version=1.0

Never log or output consumer_key or consumer_secret values.

See docs/AUTHENTICATION.md for full setup instructions.

Safety

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

Workflow Steps

Step 1 — Fetch all customers

GET /wp-json/wc/v3/customers?per_page=100&page=1

Filter: orders_count >= min_orders and total_spent >= min_spend.

Step 2 — Find each customer's last order date

GET /wp-json/wc/v3/orders
  ?customer=<customer_id>&per_page=1&orderby=date&order=desc&status=completed

Extract date_created of the first result as last_order_date.

Step 3 — Filter by inactivity window

Keep customers where: inactive_days <= days_since_last_order <= max_inactive_days.

Step 4 — Sort by total_spent descending and export

API Endpoints Used

GET  /wp-json/wc/v3/customers   — customer list
GET  /wp-json/wc/v3/orders      — last order date per customer

Pagination Strategy

WooCommerce REST API uses page/per_page pagination (not cursor-based).

Standard pattern:

page = 1
while True:
  response = GET /endpoint?per_page=100&page=page
  process(response)
  if len(response) < 100: break
  page += 1

Maximum per_page is 100 for most endpoints. The X-WP-Total and X-WP-TotalPages response headers report totals. Always read X-WP-TotalPages on the first request to estimate job size.

Session Tracking

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

STARTUP:

╔══════════════════════════════════════════╗
║  SKILL: woo-customer-win-back            ║
║  STORE: <store_url>                      ║
║  TIME:  <ISO-8601 UTC>                   ║
║  MODE:  READ-ONLY                        ║
╚══════════════════════════════════════════╝

PER-OPERATION (emit after each API call batch):

[N/TOTAL] <METHOD> <endpoint> → <result_count> records | params: <key>=<val>

COMPLETION (human format):

╔══════════════════════════════════════════╗
║  COMPLETE: woo-customer-win-back         ║
║  RECORDS PROCESSED: <n>                  ║
║  OUTPUT: <filename>                      ║
╚══════════════════════════════════════════╝

COMPLETION (json format):

{
  "skill": "woo-customer-win-back",
  "store": "<store_url>",
  "completed_at": "<ISO-8601>",
  "records_processed": <n>,
  "output_file": "<path>",
  "dry_run": false
}

Output Format

CSV filename: woo-customer-win-back_<YYYY-MM-DD>.csv Columns: customer_id, email, first_name, last_name, total_spent, orders_count, last_order_date, days_inactive, last_order_total, last_order_items

Error Handling

Best Practices

• Prioritize customers by total_spent — high-LTV lapsed customers have the most win-back potential.
• Use inactive_days: 90 as your standard win-back window; test 60 for higher-frequency categories.
• Personalize the win-back email with last_order_items for higher conversion rates.
• Exclude customers who are already in an active cart abandonment flow.