Contact Center Analytics
Transform call, disposition, campaign and agent data into actionable operational intelligence — built on measures your whole organization agrees on.
The measurement layer, done properly
Almost every contact center already has more data than it uses. The constraint is rarely volume — it is that measures are defined inconsistently, sourced from different systems, and calculated differently depending on who built the report.
This service establishes a single, governed measurement layer over your contact center data, then builds the recurring analysis that runs on top of it: campaign, agent, disposition, queue and service-level performance.
The output is not just dashboards. It is an agreed definition of what each number means, where it comes from, and who owns it — which is what makes the dashboards trustworthy.
What’s included
KPI governance
A KPI dictionary covering every operational measure: business definition, exact calculation, source system, grain, owner and review cadence. Conflicting definitions are surfaced and resolved rather than averaged.
Call analysis
Call-level analysis across legs, transfers, holds and outcomes — including the normalization work needed before volume, duration and connect metrics can be compared across systems.
Campaign analytics
Campaign and list performance: contact rates, connect rates, penetration, attempt curves, right-party contact, and the relationship between list strategy and outcome.
Agent performance
Agent and team analysis built on comparable measures — occupancy, AHT, ACW, adherence, conversion and quality — with the normalization needed to compare fairly across queues and shifts.
Disposition analytics
Analysis of disposition patterns, drift and misuse, including reconciliation of agent-entered outcomes against system-recorded call behavior.
SLA monitoring
Service-level and abandonment monitoring at the intervals your business actually manages to, with breach detection that surfaces during the day rather than after it.
How the work runs
A typical analytics engagement moves through four stages. Each one produces something usable, so value does not sit at the end of the project.
Stage 1 — Assess
Inventory of existing reports, measures and sources. Where the same measure is calculated differently in different places, the variants are documented side by side.
Stage 2 — Define
KPI dictionary agreed with operations, finance and leadership. This is the step most organizations skip, and the reason their dashboards are argued with.
Stage 3 — Build
Data extraction, modeling and the analysis layer. Measures are implemented once, centrally, and reused across every report.
Stage 4 — Operate
Monitoring, documentation and handover, so the internal team can maintain and extend the work without re-engaging the consultant for routine changes.
What you receive
Everything is documented and handed over, so your team can maintain and extend it without re-engaging the consultant for routine changes.
- KPI dictionary with formulas, sources and owners
- Data extraction and refresh design
- Campaign, agent and disposition analysis models
- Interval-level SLA and abandonment monitoring
- Call-flow and routing analysis
- Operational and executive reporting views
- Reconciliation against platform-native reporting
- Documentation and knowledge-transfer sessions
Business outcome
One agreed set of numbers across operations, finance and leadership — so meetings move from debating the data to deciding what to do about it.
Often combined with
Let’s Turn Your Contact Center Data Into an Advantage.
Tell us what you’re trying to solve. We’ll help identify the data, technology and analytics required to move forward.