Analytics api interval data service level calculation logic failing

NICE CXone API & Integrations
1

Looking for advice on how to correctly calculate Service Level percentage using raw Analytics API interval data.

i am writing a spring boot service to aggregate queue performance metrics. the docs say to use the /api/v2/analytics/queues/interactions/summarize endpoint. i am passing intervalSize=PT1H and filtering by conversationType=voice.

the response json looks like this for a single interval:

{
 "id": "12345",
 "data": [{
 "groupBy": "queue",
 "count": 50,
 "serviceLevel": 0.8,
 "waitTime": {
 "mean": 12.5
 },
 "abandonCount": 2
 }]
}

here is the java code i am using to compute the overall SL for the day:

Double totalWeightedSL = 0.0;
Double totalInteractions = 0.0;
for (DataPoint dp : response.getData()) {
 Double sl = dp.getServiceLevel(); // returns 0.0 to 1.0
 Integer count = dp.getCount();
 totalWeightedSL += sl * count;
 totalInteractions += count;
}
Double finalSL = totalWeightedSL / totalInteractions;

the result i get is 0.82 but the UI shows 85%. i think i am missing something about how serviceLevel is defined in the api. the doc says: “serviceLevel is the percentage of interactions answered within the service level target”.

is serviceLevel in the json already a percentage (0.8 = 80%) or a fraction? also, should i be summing the abandonedCount? if i include abandoned calls in the denominator, the number drops.

i tried adding targetWaitTime=PT30S to the query but it didn’t change the serviceLevel field value.

also, sometimes the api returns null for serviceLevel if there are no interactions. my code throws NullPointerException.

please help. i have been stuck on this for 2 days. the timezone is Asia/Kolkata so i am using timezone=Asia/Kolkata in the query params. is that correct?

thanks.

2

use sum(target("offerCount")) divided by sum(target("answerCount")) in your aggregation query, not the raw interval counts which are volatile.

{
"id": "12345-67890",
"groupBy": ["queueId"],
"timeGroup": "PT1H"
}

the summarize endpoint handles the SLA logic internally if you include metrics=["serviceLevel"] in the request body, bypassing manual calculation entirely.

3

The summarize endpoint aggregates correctly, but manual calculation requires summing offerCount and answerCount across all intervals before dividing. Do not average the interval percentages.

{
 "metrics": ["offerCount", "answerCount"],
 "groupBy": ["queueId"],
 "timeGroup": "PT1H"
}

Client-side logic must iterate the response array, summing both fields. Then divide total answered by total offered to get the true service level percentage.

4

You should probably look at at how the platform handles HSM template validation when aggregating metrics, as similar strict schema rules apply here. The suggestion above regarding summing counts is correct, but you must ensure your Architect Data Action mappings handle the nested array structure correctly to avoid parser failures. When pulling raw interval data, the JSON response often contains volatile counts that require client-side iteration. Use a Spring Boot service to fetch the data, then iterate through the response array to sum offerCount and answerCount before calculating the final percentage. Do not average the interval percentages, as this skews the Service Level calculation. Ensure your OAuth scopes include analytics:read to access these endpoints. The PureCloudPlatformClientV2 SDK can simplify this by handling pagination and rate limiting automatically. Always verify the message status webhooks if you are correlating this data with outbound campaigns, as timing mismatches can cause discrepancies in your aggregated metrics.