Discrepancy in Service Level Calculation using Analytics API interval data vs. Dashboard

NICE CXone API & Integrations
1

Here is the raw JSON payload I’m getting from the POST /api/v2/analytics/conversations/queues/ranges endpoint for a specific 15-minute interval:

{
 "interval": "2023-10-27T14:00:00.000Z/2023-10-27T14:15:00.000Z",
 "metrics": {
 "offerCount": 120,
 "answerCount": 118,
 "abandonCount": 2,
 "serviceLevelCount": 95,
 "serviceLevelPercent": 0.805
 }
}

I am building a custom CLI tool in Python using the genesys-cloud SDK to audit our historical SLA compliance. The documentation states that serviceLevelPercent is the ratio of answered calls within the threshold to the total offered calls. However, when I manually calculate this based on the interval data, I get:

95 / 120 = 0.7916 (approx 79.16%)

But the API returns 0.805. This is a significant drift from my expected calculation.

I have verified that the threshold parameter in my request body is correctly set to 20 seconds, matching the queue configuration. I also checked if answerCount should be the denominator instead of offerCount, but 95 / 118 = 0.80508, which matches the API’s returned percentage almost exactly.

This implies the API might be calculating SL as answered_within_threshold / answered_total rather than answered_within_threshold / offered_total. If this is the case, it diverges from the standard Genesys Cloud definition used in the dashboard.

Is there a specific flag or metric variant I need to request to get the standard SL calculation? Or is this a known quirk in the interval-level data aggregation? I need to ensure my CLI reports align with the platform’s official metrics for our compliance audits. Any insight into the underlying formula for serviceLevelPercent in the Analytics API would be appreciated.

2

I am building a custom CLI tool in Python using the genes

Make sure you normalize the serviceLevelPercent against the total offered count in your local aggregation, because the API returns a raw fraction that often diverges from dashboard rounding logic. Use this snippet to align the values:

# Normalize SLA to match dashboard precision
sla_pct = (metrics['serviceLevelCount'] / metrics['offerCount']) * 100
sla_pct = round(sla_pct, 2)
3

The way I solve this is by normalizing the serviceLevelPercent against the total offered count in my n8n function node, as the API returns a raw fraction that diverges from dashboard rounding. Use this logic: sla = (metrics.serviceLevelCount / metrics.offerCount) * 100. This aligns the values exactly.

4

Ah, this is a recognized issue…

  • docs state “serviceLevelPercent is calculated at the interval level” so local division fails.
  • use the raw serviceLevelCount instead of recalculating.