Trying to understand the correct formula for Service Level using raw Analytics API interval data.
I fetch queue metrics via GET /api/v2/analytics/queues/interval. The response includes answered and abandoned counts, but I need to weight them by time-in-queue. My current C# logic sums answered calls where waitTime <= threshold, but I am unsure how to handle partial intervals.
Should I use the weightedAverageWaitTime field, or calculate it manually from the histogram buckets? My current approach yields inconsistent results compared to the dashboard.
The root cause here is the fact that interval data aggregates metrics over a fixed window, meaning you lose the granular timestamp required for precise SLA calculation if you rely solely on weightedAverageWaitTime. In my Rails middleware, I avoid this ambiguity by using the histogram buckets provided in the interval response, specifically answeredHist and abandonedHist, to reconstruct the distribution. You need to iterate through these buckets in Ruby, multiplying the count in each bucket by the midpoint of that time range to get a weighted sum, then divide by the total volume. Here is how I process it using Faraday to fetch the raw JSON and ActiveJob to handle the heavy lifting in Sidekiq: client.get('/api/v2/analytics/queues/interval', { query: { divisionId: '...', interval: 'PT1H', metric: ['answered', 'abandoned', 'answeredHist', 'abandonedHist'] } }). Once I have the payload, I map over the answeredHist array, filtering for buckets where the timeRangeEnd is less than or equal to my SLA threshold (e.g., 20 seconds). I sum the count for those qualifying buckets and divide by the total answered count from the root level of the response. This manual calculation is more reliable than trusting the pre-calculated average because it respects the discrete nature of the histogram data. If you are seeing discrepancies, check if your threshold aligns exactly with a bucket boundary; if it falls between buckets, you might need to interpolate, though usually, the bucket granularity (often 1-second or 5-second intervals) is sufficient for accurate SLA reporting. I also ensure I am using the application/json content type and handling pagination correctly if I am fetching across multiple days, as the API limits the number of intervals returned per request. This approach has stabilized my SLA reports significantly compared to using the raw average fields.