Discrepancy in Service Level Calculation Using Raw Analytics API Interval Data

NICE CXone API & Integrations
1

Why does this setting in my Python aggregation script yield a Service Level percentage that diverges significantly from the Genesys Cloud UI report? I am authoring a Terraform module that provisions a custom dashboard widget, and I need to validate the data source via the /api/v2/analytics/interactions/queues/summary endpoint. I am fetching raw interval data with intervalSize=PT1H and attempting to calculate SL by dividing offerCount by handleCount for intervals where answerTime < threshold. The API returns 200 OK, but the JSON payload shows offerCount includes abandoned calls, whereas the UI seems to exclude them or weight them differently. Is there a specific filter parameter in the request body, such as filters=[{"type":"callOutcome","value":"abandoned"}], that I must apply to match the native reporting logic? My current calculation logic is failing validation in the acceptance test suite.

2

This is caused by a fundamental misunderstanding of how the analytics engine aggregates interval data versus how it calculates service level metrics. You are dividing offerCount by handleCount, which is mathematically incorrect for SL calculations. Service Level is defined as the percentage of interactions handled within a specified threshold, not a ratio of offers to handles.

The /api/v2/analytics/interactions/queues/summary endpoint returns pre-aggregated metrics. You do not need to perform manual division on raw counts. Instead, you should query the specific metric serviceLevelPercent directly in your metrics parameter. If you are fetching interval data, ensure you are looking at the percent field within the interval response, not summing up counts.

Here is how you should structure your request to get the accurate SL data:

  • Use the metrics parameter to explicitly request serviceLevelPercent.
  • Set the metricIds to include the specific queue ID.
  • Avoid manual calculation using offerCount and handleCount as these represent different stages of the interaction lifecycle.
import requests

url = "https://api.mypurecloud.com/api/v2/analytics/interactions/queues/summary"
headers = {
 "Authorization": "Bearer YOUR_ACCESS_TOKEN",
 "Content-Type": "application/json"
}

params = {
 "metricIds": "serviceLevelPercent,answerRatePercent",
 "groupBy": "queueId",
 "intervalSize": "PT1H",
 "since": "2023-10-01T00:00:00Z",
 "until": "2023-10-02T00:00:00Z"
}

response = requests.get(url, headers=headers, params=params)
data = response.json()

# Extract service level percent directly from the response
for interval in data.get('intervals', []):
 print(f"Time: {interval['from']}, SL: {interval['metrics']['serviceLevelPercent']['percent']}")

The UI uses the same aggregated serviceLevelPercent metric. By fetching this directly, you eliminate calculation errors caused by misinterpreting raw counts. Always verify the metric definitions in the Genesys Cloud API documentation to ensure you are using the correct numerator and denominator for any custom calculations.

3

I’d suggest checking out at the serviceLevel metric directly in the aggregation response rather than calculating it manually. The API returns pre-calculated SL values based on your queue’s specific threshold. Using the raw counts introduces rounding errors and ignores the configured service level target.

{
 "metrics": [
 "serviceLevel",
 "handleCount"
 ],
 "groupBy": ["interval"]
}
4

Make sure you align your threshold parameter with the queue’s configured Service Level target. The API calculates SL based on handledCount within that specific time limit, not total handles.

{
 "metrics": ["serviceLevel", "handledCount"],
 "threshold": 20
}

Ignoring this causes significant variance.

5
  • Stop reinventing the wheel. 1. Request serviceLevel directly in the metrics array. 2. Pass the correct threshold value.
{
 "metrics": ["serviceLevel", "handledCount"],
 "threshold": 20
}
  • Manual calculation is a waste of CPU cycles. The API handles the logic.