Understanding Contact Center KPIs
What AHT, FCR, SLA, occupancy, abandonment and ACW actually measure, how they interact, and how to build a KPI framework that survives executive scrutiny.
Working notes from the practice — how the metrics behave, where the data misleads, and how to build analytics that holds up under scrutiny.
What AHT, FCR, SLA, occupancy, abandonment and ACW actually measure, how they interact, and how to build a KPI framework that survives executive scrutiny.
Three telephony metrics that explain most outbound performance problems — what each measures, what moves it, and how to read them together.
A reference architecture: star schema modeling, a disciplined DAX measure layer, incremental refresh, and an executive page that answers questions.
Disposition data is the most trusted and least reliable field in most contact centers. How drift happens, how to detect it, and how to govern it.
These are on the writing queue. If one of them is a live problem for you right now, get in touch — it is usually faster to talk about your specific case.
Extraction patterns, reporting API behavior and how to build an analytical store that outlives platform retention limits.
Turning programmable voice events, call resources and webhook payloads into a usable analytical dataset.
The checks worth automating: duplicates, timezone drift, orphaned legs, retroactive edits and reconciliation against platform reporting.
The transformation path from raw call detail records to measures an operations leader can act on the same day.
Where AI adds genuine coverage, where it adds confident noise, and how to benchmark the difference before relying on it.
What belongs on an executive page, what belongs one click away, and why most executive dashboards get this backwards.
Tell us what you’re trying to solve. We’ll help identify the data, technology and analytics required to move forward.