Shopify Admin Traffic By Page Report

Purpose

Queries Shopify's built-in analytics engine (ShopifyQL) to surface session-level traffic data scoped to product and collection pages. Shows which pages are attracting the most traffic, how many sessions convert to orders, and where visitors are bouncing — ready input for SEO prioritisation, merchandising focus, and A/B test targeting. Read-only — no mutations are executed.

Prerequisites

• Authenticated Shopify CLI session: shopify auth login --store <domain>
• API scopes: read_reports
• Shopify plan: ShopifyQL analytics is available on Basic and above; availability of sessions as a data source requires Shopify plan or higher

Parameters

Workflow Steps

1. OPERATION: shopifyqlQuery — query (all landing pages) Inputs: ShopifyQL string FROM sessions SHOW sessions, conversion_rate GROUP BY landing_page_path SINCE -<days_back>d UNTIL today ORDER BY sessions DESC LIMIT 250; sessions and conversion_rate are the confirmed available metrics for this data source Expected output: All landing pages with session counts and conversion rates; paginate via OFFSET if result count equals 250

2. In-memory filtering: Filter rows where landing_page_path starts with /products/ (product pages) or /collections/ (collection pages); apply page_type parameter; sort by sort_by; truncate to top_n; flag pages with sessions above median and conversion_rate < 0.02 as high_traffic_low_conversion

Note: ShopifyQL does not support LIKE, WHERE string prefix filters, or aggregate aliases that shadow reserved column names (sessions, conversion_rate). All page-type filtering must be done in-memory after fetching all rows.

GraphQL Operations

graphqlCopy code
# shopifyqlQuery:query (page traffic) — validated against api_version 2025-01
query TrafficByPage($query: String!) {
  shopifyqlQuery(query: $query) {
    parseErrors
    tableData {
      columns {
        name
        dataType
        displayName
      }
      rows
    }
  }
}

The $query variable (single call — all landing pages, filtered in-memory):

Copy code
FROM sessions
SHOW sessions, conversion_rate
GROUP BY landing_page_path
SINCE -<days_back>d
UNTIL today
ORDER BY sessions DESC
LIMIT 250

Then filter rows in-memory:

• Product pages: landing_page_path.startsWith('/products/')
• Collection pages: landing_page_path.startsWith('/collections/')
• conversion_rate is returned as a decimal (e.g. 0.016 = 1.6%) — multiply by 100 for display

Confirmed live against 2025-01: sessions (INTEGER) and conversion_rate (PERCENT) are the available metrics. WHERE … LIKE, bounce_rate, converted_sessions, and aggregate aliases that shadow reserved names are not supported in ShopifyQL FROM sessions.

Session Tracking

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

On start, emit:

Copy code
╔══════════════════════════════════════════════╗
║  SKILL: traffic-by-page-report               ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

After each step, emit:

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

On completion, emit:

For format: human (default):

Copy code
══════════════════════════════════════════════
OUTCOME SUMMARY
  Lookback window:      <days_back> days
  Page type:            <products|collections|both>
  Pages analysed:       <n>
  Top session page:     <path> (<n> sessions)
  Top converting page:  <path> (<pct>%)
  Errors:               0
  Output:               traffic_by_page_<date>.csv
══════════════════════════════════════════════

For format: json, emit:

jsonCopy code
{
  "skill": "traffic-by-page-report",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "ProductPageTraffic", "type": "query", "params_summary": "products, last <days_back> days", "result_summary": "<n> product pages returned", "skipped": false },
    { "step": 2, "operation": "ProductPageTraffic", "type": "query", "params_summary": "collections, last <days_back> days", "result_summary": "<n> collection pages returned", "skipped": false }
  ],
  "outcome": {
    "days_back": 30,
    "page_type": "both",
    "pages_analysed": 0,
    "results": [],
    "errors": 0,
    "output_file": "traffic_by_page_<date>.csv"
  }
}

Output Format

CSV file traffic_by_page_<YYYY-MM-DD>.csv with one row per page:

For format: human, a ranked table is printed inline truncated to top_n, followed by a short list of optimisation candidates flagged with high_traffic_low_conversion.

Error Handling

Best Practices

1. A conversion_rate_pct below 1% on a high-traffic product page is worth investigating — check whether the product is out of stock, has poor images, or lacks a clear call-to-action.
2. Collection pages with high bounce rates often signal a poor match between the ad or search term that drove the session and the collection content — review the collection SEO title.
3. Use page_type: products after a new product launch to track early traction without noise from collection traffic.
4. Combine with top-product-performance to correlate high-converting pages with the products generating the most actual revenue.
5. Re-run the report weekly after making on-page changes (copy, imagery, pricing) to measure the impact — the 7-day window (days_back: 7) isolates post-change behaviour cleanly.