Extract Unique Tags And Attributes

Output Contract

Produce one JSON object with this structure:

{
  "tag_groups": {
    "device_tags": ["imported_token", "tag_example"],
    "ua_mobile_platform": ["android", "ios"],
    "ua_opt_in": ["false", "true"],
    "named_user_id": []
  },
  "attributes": ["cid"],
  "device_attributes": ["ua_named_user_id", "ua_device_os"],
  "subscription_lists": ["list_a", "list_b"],
  "excluded": {
    "tag_group_values": ["named_user_id"]
  }
}

Privacy Rules

• Do not output channel IDs, named user IDs, aliases, push addresses, timestamps, or any per-row audience objects.
• Include named_user_id as a tag group key if present, but do not include its values.
• Keep only deduplicated metadata values in the final JSON object.

Step 1: Retrieve Audience Data

Use the Download Entire Audience workflow to fetch channels at scale. You can execute this in parallel as documented there, but this workflow only keeps metadata fields during processing.

Expected channel fields of interest:

• tags (top-level array)
• tag_groups (object of group -> array of values)
• attributes (object keys only)
• device_attributes (object keys only)
• subscription_lists (array)

Step 2: Initialize Aggregation Sets

Initialize in-memory sets (or equivalent deduplicated collections):

• tag_groups_map: map of group_name -> set(values)
• attributes_set
• device_attributes_set
• subscription_lists_set

Also initialize:

• synthetic tag group device_tags inside tag_groups_map
• excluded tag groups set: {"named_user_id"}

Step 3: Extract and Normalize Metadata

For each channel record:

1. Fold top-level tags into tag_groups_map["device_tags"].
2. Iterate tag_groups:
   • Normalize group name (recommended: lowercase).
   • If group name is named_user_id, create/retain the group key but skip values.
   • Otherwise add normalized values into that group set.
3. Add keys from attributes into attributes_set.
4. Add keys from device_attributes into device_attributes_set.
5. Add each entry in subscription_lists into subscription_lists_set.

Recommended value normalization:

• Trim whitespace
• Normalize case for platform-like/system values (IOS -> ios)
• Ignore empty strings / nulls

Step 4: Build Final JSON Object

Convert each set to a sorted array for deterministic output:

• tag_groups[group] -> sorted array
• attributes -> sorted array
• device_attributes -> sorted array
• subscription_lists -> sorted array

Include explicit exclusions metadata:

"excluded": {
  "tag_group_values": ["named_user_id"]
}

Step 5: Validate Output Safety and Completeness

Validation checklist:

• Output is a single JSON object
• No audience identifiers or per-channel rows are present
• tag_groups.device_tags exists (even if empty)
• named_user_id appears as a group key with an empty array when observed in source data
• Arrays are deduplicated and sorted

Outcomes

• Single metadata-only JSON artifact ready for taxonomy analysis
• Unique tags grouped by tag group (plus synthetic device_tags)
• Unique attribute and device attribute key catalogs
• Unique subscription list catalog
• Privacy-safe output without audience identity leakage

Use Cases

• Build governance dictionaries for tags and attributes
• Review segmentation vocabulary across projects
• Prepare integration mappings without exporting audience-level data
• Detect casing inconsistencies and normalize taxonomy

Related Workflows

• Download Entire Audience Workflow - Source retrieval workflow used for channel pagination

Best Practices

1. Normalize early in extraction to prevent duplicate variants.
2. Keep exclusion rules explicit and testable (named_user_id values excluded).
3. Stream process channels and discard raw rows immediately.
4. Treat JSON output as generated metadata and re-create rather than manually editing.
5. Re-run periodically to detect taxonomy drift.

Additional Resources

• Channel Listing Skill
• Download Entire Audience Workflow
• Airship Channel Listing API