Mapping Zendesk custom fields to GC Analytics custom_attributes

General Discussion
1

What’s the cleanest way to map Zendesk’s ticket_fields.custom_15 to Genesys Cloud’s conversation.analytics.custom_attributes without triggering a 422 Unprocessable Entity on the /api/v2/analytics/conversations/details/query endpoint in our build 2024.02 environment? Over in the Zendesk setup, custom metadata just flows straight through the ticket API, but the GC Analytics pipeline drops the payload when the JSON schema doesn’t match the pre-approved whitelist, and I’ve seen folks patch it by routing through a Data Action to sanitize the payload before the Conversation Analytics block, per that thread on the architect workarounds board. Console just throws INVALID_SCHEMA: custom_attributes mismatch and the queue stays hot while the parser chokes.
{"errors":[{"code":"INVALID_SCHEMA","message":"custom_attributes mismatch"}]}

2

PureCloudPlatformClientV2 validates the custom_attributes schema strictly before the analytics engine accepts the payload. When you push ticket_fields.custom_15 directly into the conversation metadata, the platform expects a flat key-value structure with predefined data types. The 422 error usually appears because the JSON structure contains nested objects or unregistered attribute names that the analytics index hasn’t been provisioned for yet. The schema parser is notoriously strict about nested objects. It just chokes on them. You’ll need to flatten it first.

First, you need to register the custom attribute in the organization settings before attempting the query. The attribute must exist via platformClient.AnalyticsCustomAttributeApi().postAnalyticsCustomattribute(...) with a matching data type. If you’re mapping a Zendesk string field, the type has to be STRING. You can’t just invent the key on the fly. The platform will reject it immediately.

Second, the payload structure for the analytics query needs to drop that nested Zendesk path. The ticket_fields.custom_15 reference won’t parse correctly inside the select array. You’ll need to remap it during the integration flow, typically using a Data Action or a webhook transform. Here’s how the corrected payload should look when you finally hit the query endpoint:

{
 "select": [
 {
 "name": "custom_attributes.zendesk_custom_15"
 }
 ],
 "where": [
 {
 "path": "conversation.type",
 "filter": "equals",
 "value": "voice"
 }
 ],
 "timeFrom": "2024-01-01T00:00:00Z",
 "timeTo": "2024-01-02T00:00:00Z"
}

Third, if you’re managing this via infrastructure as code, the terraform-provider-cxascode resource genesyscloud_analytics_custom_attribute handles the registration automatically. State drift often occurs when manual UI changes overwrite the Terraform plan, so locking the attribute definition in your .tf files prevents the schema mismatch from recurring. You should back up the state file regularly to catch those silent overwrites.

The query itself just needs the flattened key. Make sure your OAuth token includes analytics:view and analytics:query. Run the request against the staging environment first. The index usually takes a few minutes to propagate. Sometimes the cache sticks around longer than expected. You’ll have to wait it out.

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