Shopify Admin Bulk Customer Tag Update

Purpose

Applies bulk tag changes (add, remove, or both) to customers selected by a query filter (e.g., total_spent:>=500, tag:newsletter) or by an explicit list of customer GIDs. Tags are how Shopify segments customers for discounts, marketing, and support workflows; this skill makes batch changes safe, dry-runnable, and auditable. Use when migrating from one tag taxonomy to another, when retiring a campaign-specific tag, or when applying a new segment tag identified by an analytics report.

Prerequisites

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

Parameters

Safety

⚠️ Step 2 executes one customerUpdate mutation per customer in the matched set. Tag changes are immediate and visible to staff and to any apps reading customer tags (loyalty, marketing automation, segmentation). mode: replace overwrites existing tags entirely — manually-applied operational tags will be lost. The default is dry_run: true and mode: merge. Always run dry-run first, review the matched count, and confirm add_tags/remove_tags are spelled correctly — Shopify tags are case-sensitive.

Workflow Steps

1. OPERATION: customers — query Inputs: When filter is set: query: <filter>, first: 250, pagination cursor. When customer_ids is set: batch query with query: "id:<id1> OR id:<id2> ..." (chunk into batches of 25 IDs). Select id, displayName, defaultEmailAddress { emailAddress }, tags. Expected output: Customer list with current tags. Abort if match_count > max_customers.

2. For each matched customer, compute the target tag set:

   • If mode: merge: target = (existing ∪ add_tags) \ remove_tags
   • If mode: replace: target = add_tags (remove_tags ignored) Skip the customer if target == existing (no-op).

3. OPERATION: customerUpdate — mutation Inputs: For each customer with a non-empty diff: input: { id: <customer_id>, tags: <target_tag_array> } Expected output: customer.id, customer.tags, userErrors; collect failures

GraphQL Operations

# customers:query — validated against api_version 2025-01
query CustomersForBulkTagging($query: String!, $after: String) {
  customers(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        displayName
        firstName
        lastName
        defaultEmailAddress {
          emailAddress
        }
        tags
        numberOfOrders
        amountSpent {
          amount
          currencyCode
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

# customerUpdate:mutation — validated against api_version 2025-01
mutation CustomerTagsUpdate($input: CustomerInput!) {
  customerUpdate(input: $input) {
    customer {
      id
      displayName
      tags
    }
    userErrors {
      field
      message
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Bulk Customer Tag Update             ║
║  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>

If dry_run: true, prefix every mutation step with [DRY RUN] and do not execute it.

On completion, emit:

For format: human (default):

══════════════════════════════════════════════
BULK TAG UPDATE OUTCOME
  Filter:           <filter or "<n> explicit IDs">
  Mode:             <merge|replace>
  Add tags:         <list>
  Remove tags:      <list>
  Customers matched: <n>
  Customers updated: <n>  (or "skipped — dry_run")
  No-op (already in state): <n>
  Errors:           <n>
  Output:           bulk_tag_update_<date>.csv
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "bulk-customer-tag-update",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": true,
  "mode": "merge",
  "outcome": {
    "matched": 0,
    "updated": 0,
    "noop": 0,
    "errors": 0,
    "add_tags": [],
    "remove_tags": [],
    "output_file": "bulk_tag_update_<date>.csv"
  }
}

Output Format

CSV file bulk_tag_update_<YYYY-MM-DD>.csv with columns: customer_id, name, email, previous_tags, tags_added, tags_removed, new_tags, status

The status column reports updated, noop, or error: <message>.

Error Handling

Best Practices

• Always run with dry_run: true first — review the matched count and a sample of previous_tags → new_tags diffs before committing.
• Prefer mode: merge (the default) for almost all use cases — mode: replace is appropriate only when fully resetting a customer's tag taxonomy and you have an audited backup of prior tags.
• Tags are case-sensitive: VIP and vip are distinct in Shopify. Standardize casing in your taxonomy.
• For ongoing operational segments, prefer date-stamped tag names (e.g., cohort-2026-Q2) so historical cohorts remain identifiable as new tags accumulate.
• Pair with vip-customer-identifier or customer-spend-tier-tagger to feed segment tags from analytics outputs into the customer record.
• Use remove_tags as the cleanup pass after a campaign — leaving stale campaign tags clutters segmentation in marketing tools.
• When customer_ids is supplied directly (e.g., from another skill's CSV), the run is fully deterministic — no filter ambiguity.