Could someone clarify why the routing_prediction_score field is null in the bulk export CSV for digital channels? The environment is Genesys Cloud v3.5.2, London region. We use this score for legal discovery audits. The API returns the correct value, but the S3 export job drops it.
The export job completes successfully, but the chain of custody requires this specific metadata. Is this a known limitation for digital channel exports?
This looks like a schema mapping issue in the export definition rather than a platform limitation. The bulk export jobs rely on explicit field mappings defined in the HCL configuration. If routing_prediction_score is not explicitly included in the columns block of the genesyscloud_bulkexport_job resource, the provider defaults to standard interaction fields and omits predictive metrics.
Check your Terraform module for the export job definition. Ensure the column mapping explicitly references the digital interaction schema. Here is the required configuration snippet:
resource "genesyscloud_bulkexport_job" "digital_audit" {
name = "Digital Predictive Export"
type = "INTERACTION"
column_mapping {
columns = [
"interaction.id",
"routing_prediction_score", # Must be explicit
"media.type"
]
}
}
The API returns the data because it queries the live schema, but the export job uses a static snapshot of the defined columns. If the field is missing from the HCL definition, it will not appear in the S3 CSV, regardless of API availability.
You should probably look at at the specific column definitions within your bulk export configuration, as the default schema for digital channels often excludes advanced predictive metrics like routing_prediction_score to optimize payload size. While the suggestion above regarding Terraform HCL mapping is technically sound for infrastructure-as-code deployments, many organizations managing complex BYOC environments alongside digital integrations find that the Genesys Cloud UI export builder or direct API calls to /api/v2/bulkexports/jobs require explicit inclusion of these fields in the columns array.
If you are using the API directly, ensure your request body explicitly lists routing_prediction_score alongside standard fields like interaction_id and channel_type. The platform does not automatically infer that predictive metadata is required for legal discovery unless specified. For example:
{
"name": "Legal_Discovery_Digital_Export",
"type": "INTERACTION",
"columns": [
"interaction_id",
"channel_type",
"start_time",
"routing_prediction_score",
"queue_id"
],
"filter": {
"type": "INTERACTION",
"query": "channel_type:in(email,messaging)"
}
}
In my experience managing fifteen trunks across APAC and EMEA regions, I have observed that digital channel metadata often lags behind voice channel metadata in standard export templates. The routing_prediction_score is calculated post-interaction and may not be immediately available in real-time snapshots, but it should persist in historical bulk exports if the field is explicitly requested. If the field remains null after explicit inclusion, verify that the interactions were indeed routed through a predictive strategy. Organic or direct routing methods do not populate this score. Additionally, check the last_modified_time of the export job relative to the interaction completion time to ensure the analytics pipeline has fully processed the predictive metadata before the export snapshot is taken.
TL;DR: Add explicit column mapping in the export definition.
The bulk export jobs often drop advanced metrics by default. It is not a bug, it is a schema choice. The system optimizes payload size for digital channels. If routing_prediction_score is missing, it is likely excluded from the default column set.
Try updating the export configuration. Use the API to patch the job definition. Add the field explicitly:
{
"columns": [
"interaction_id",
"channel_type",
"routing_prediction_score"
]
}
I saw similar behavior during load tests. The API returns full data, but batch exports truncate unused fields. Check the genesyscloud_bulkexport_job resource. Ensure the column list is explicit. Do not rely on defaults. The documentation mentions this optimization for large datasets. If the field is still null, check the interaction metadata. Sometimes the score is calculated post-routing. Verify the timestamp of the export. It must include the routing event. This usually fixes the null value issue.
This looks like a potential performance bottleneck if you start forcing these heavy metadata fields into every bulk export job. While adding the explicit column mapping fixes the missing data issue, it significantly increases the payload size for digital channel exports. In our load testing scenarios with JMeter, adding just a few non-essential metrics to the export schema caused a 20% increase in API response times and triggered rate limits much faster than expected. The platform optimizes default exports for throughput, so bypassing that for compliance audits can impact concurrent job processing. If you need this data for legal discovery, consider running these exports during off-peak hours or using a dedicated API endpoint for high-volume pulls rather than modifying the standard bulk export configuration. It helps maintain system stability while still capturing the required chain-of-custody metadata without clogging the main export queue.