Genesys Analytics API: Service Level calculation mismatch with raw interval data

Trying to build a custom dashboard that pulls raw interval data from /api/v2/analytics/interactions/summary. The goal is to calculate Service Level (SL) percentage manually because the pre-aggregated reports are too slow for our real-time needs.

We’re targeting a 20-second threshold. I’m fetching the data for the last 30 minutes using the interval parameter set to PT1M. The response gives me answered, abandoned, and waitTime buckets.

Here’s the logic I’ve implemented in Python:

sl_percent = 0
answered_in_sl = 0
total_answered = 0

for interval in response['granularTotal']['intervals']:
 answered_in_sl += interval['metrics']['waitTime']['lessThan20s']['count']
 total_answered += interval['metrics']['answered']['count']

if total_answered > 0:
 sl_percent = (answered_in_sl / total_answered) * 100

The blem is the numbers don’t match the Genesys UI. The dashboard shows 82% SL, but my script calculates 76%. I’ve checked the timezones. We’re in Australia/Sydney (UTC+10). The API docs say the waitTime metric is calculated from the moment the interaction is queued until it is answered.

I’ve tried filtering by specific queue IDs and removing abandoned interactions from the denominator, but the gap remains. Is there a specific metric I’m missing? The waitTime object has lessThan10s, lessThan20s, etc. Am I supposed to use lessThan20s directly or is there a cumulative count somewhere?

Also, the waitTime count seems to include interactions that were abandoned after waiting more than 20 seconds but were eventually answered? No, that doesn’t make sense.

Can anyone point me to the exact formula Genesys uses for SL in the backend? The documentation just says “percentage of interactions answered within the threshold”. That’s not helpful when the math doesn’t add up. We’ve been debugging this for two days. Running the same query with different date ranges shows inconsistent variances. Sometimes it’s 1% off, sometimes 10%.

What am I doing wrong here?

Are you filtering by queueId or using a broader divisionId scope? The raw interval data often includes interactions that don’t count toward your specific SL metric if the filters aren’t tight enough.

The mismatch usually happens because the API returns total answered calls, but your SL target only applies to specific queue groups or skill targets. If you are just summing answered where waitTime < 20, you might be including calls that were answered by agents outside your target workforce or in different divisions.

Try narrowing the query with explicit queue filters. Here is how I structure the request body to ensure the denominator matches the numerator:

{
 "dateFrom": "2023-10-27T10:00:00.000Z",
 "dateTo": "2023-10-27T10:30:00.000Z",
 "interval": "PT1M",
 "groupBy": ["queueId"],
 "filters": {
 "type": "AND",
 "clauses": [
 {
 "dimension": "queueId",
 "operator": "EQ",
 "value": "your-target-queue-id-here"
 },
 {
 "dimension": "skillTarget",
 "operator": "EXISTS",
 "value": "true"
 }
 ]
 },
 "select": ["answered", "abandoned", "waitTime"]
}

Check the waitTime field in the response. It’s an average, not a count of calls under 20 seconds. You actually need to look at the serviceLevel metric directly in the response if available, or calculate it using answeredWithinTarget if your API version supports it.

If you are stuck with raw counts, you have to estimate. Take the total answered and assume a distribution. It’s not perfect, but it’s better than guessing. The analytics engine aggregates this differently than simple math.

I’ve seen this issue with US/Pacific timezone shifts too. Make sure your dateFrom and dateTo are in UTC. If you pass local time, the intervals shift and the SL drops artificially during handover hours.

Check the status field in the response. If it says partial, you are missing data. Wait for the aggregation to finish before pulling.