Woo average order value trends

woo-average-order-value-trends

Purpose

Compute average order value (AOV) for WooCommerce over a series of rolling time windows and compare periods to detect significant changes. Segments by order count ranges and payment method to surface insights. Read-only.

Prerequisites

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

Parameters

Parameter	Type	Required	Default	Description
store_url	string	yes	—	Base URL of the WooCommerce store
consumer_key	string	yes	—	WooCommerce REST API consumer key (ck_...)
consumer_secret	string	yes	—	WooCommerce REST API consumer secret (cs_...)
dry_run	bool	no	false	No effect — read-only skill
format	string	no	human	Output format: human or json
date_after	string	yes	—	Start date (YYYY-MM-DD)
date_before	string	yes	—	End date (YYYY-MM-DD)
window_days	int	no	30	Rolling window size in days
change_threshold_pct	number	no	10	Flag period-over-period AOV change above this %

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 completed orders in range

GET /wp-json/wc/v3/orders
  ?status=completed&after=<date_after>T00:00:00Z&before=<date_before>T23:59:59Z&per_page=100&page=1

Extract: id, total, date_created

Step 2 — Compute AOV per window

Divide the date range into rolling window_days windows. For each window: aov = sum(total) / count(orders).

Step 3 — Compare periods and flag changes

For each consecutive pair of windows: change_pct = (aov_current - aov_previous) / aov_previous * 100. Flag if abs(change_pct) > change_threshold_pct.

Step 4 — Export

API Endpoints Used

GET  /wp-json/wc/v3/orders          — order totals for AOV calculation
GET  /wp-json/wc/v3/reports/sales   — aggregate check

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-average-order-value-trends   ║
║  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-average-order-value-trends║
║  RECORDS PROCESSED: <n>                  ║
║  OUTPUT: <filename>                      ║
╚══════════════════════════════════════════╝

COMPLETION (json format):

{
  "skill": "woo-average-order-value-trends",
  "store": "<store_url>",
  "completed_at": "<ISO-8601>",
  "records_processed": <n>,
  "output_file": "<path>",
  "dry_run": false
}

Output Format

CSV filename: woo-average-order-value-trends_<YYYY-MM-DD>_<YYYY-MM-DD>.csv Columns: period_start, period_end, order_count, total_revenue, aov, change_pct_vs_prev, flagged

Error Handling

Error	Cause	Resolution
401 Unauthorized	Invalid credentials	Verify consumer_key and consumer_secret
403 Forbidden	Key lacks Read scope	Regenerate with Read scope
429 Too Many Requests	Rate limit	Wait 2 seconds and retry
Too few orders per window	Window too short or low traffic	Increase window_days for meaningful averages

Best Practices

• AOV spikes often correlate with promotions — check against your marketing calendar.
• AOV drops may signal an influx of new customers with smaller first orders — segment by customer age.
• Compare monthly AOV year-over-year to account for seasonal patterns.