Call Center Monitoring for Microsoft Teams: essential use cases to understand and improve your calls
A practical guide to clarify usage, balance call flows, and diagnose Teams calls.
Microsoft Teams has become a cornerstone of modern call centers: flexible, fully integrated with Microsoft 365, and easy to deploy at scale.
But once the environment is live, a recurring challenge emerges: operational visibility.
Why do some agents receive far more calls than others?
Where do call abandons come from?
Why does a queue become saturated on certain days?
And above all: how can we explain variations in call quality depending on the site, the time of day, or the user?
To answer these questions, Microsoft Teams call center monitoring must be structured around four essential use cases:
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Tracking agent usage and activity
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Understanding Call Queue & Auto Attendant flows
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Diagnosing calls end-to-end
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Monitoring sites and subnets to stabilize call quality
These use cases form the foundation of effective operational control.
Tracking usage: understanding the real activity of your agents
In a call center, everything starts with understanding daily usage: how many calls are processed, by whom, when, and from which sites.
Usage monitoring makes it possible to visualize:
- call volume per agent
- activity variations by time of day
- distribution by team or queue
- oversolicited agents
- underutilized agents
- global or local activity trends
These insights help rebalance workloads, anticipate peaks, and align staffing with actual demand.
Some Teams observability solutions — including MS Teams Observability by Phenisys — provide consolidated dashboards that surface this activity in seconds.
Understanding flows: analyzing Call Queues and Auto Attendants
Once usage is clear, the next challenge is understanding how calls actually flow through Microsoft Teams.
Every Teams-based call center relies on two core components:
- Auto Attendants (AA): call menus
- Call Queues (CQ): waiting queues leading to agents
An incoming call typically follows a sequence like this:
These flows shape the service experience: routing logic, queue fluidity, agent availability, waiting times, and abandonment rates.
To operate a Teams call center effectively, it is essential to track:
- the most frequently selected Auto Attendant options
- the busiest queues
- priority rules
- call abandons
- average waiting times
- number of connected agents
- transfers between queues
- overload periods
A clear visualization of these flows helps identify bottlenecks and adjust call distribution efficiently.
This is what some Teams observability solution — including the solution developed by Phenisys — are designed to provide.
Diagnosing calls: analyzing full end-to-end quality
Next comes the question IT and Telephony teams encounter most often: How do we explain poor Teams call quality?
Diagnosis requires consolidating all relevant data points around the call.
Participant View: everything needed to understand an agent’s call
A modern diagnostic view brings together:
- IP address and subnet
- originating site
- media protocol (UDP/TCP)
- reflexive IP (critical for remote agents)
- inbound/outbound network conditions
- connection type (LAN, Wi-Fi, VPN…)
- device and OS
These insights help isolate the root cause:
- unstable Wi-Fi
- high-latency site
- packet loss on a subnet
- TCP fallback
- degraded remote connection
The network metrics that truly impact Teams call quality
The most decisive factors are:
- latency,
- jitter,
- packet loss.
Comparing these metrics across sites or subnets makes it easy to spot issues:
This enables teams to quickly identify:
- degraded sites
- problematic subnets
- unstable regional links
- routing anomalies
Advanced diagnostics: reconstructing the entire call (distributed trace)
With MS Teams Observability by Phenisys, you can go further by reconstructing the call using the Correlation ID, producing a full “distributed trace”:
- each participant,
- each media stream,
- each metric,
- each network transition.
This is the most effective way to understand complex degradations.
Monitoring sites: stabilizing quality across your entire call center operation
Teams call centers rarely operate from a single building.
Most are multi-site, sometimes multi-country, with hybrid or remote agents.
Multi-site monitoring helps you understand:
- which sites are healthy
- which sites show degradation
- which subnets require attention
- how local conditions impact calls
Here is an example of a useful geographic view:
This type of map helps identify:
- high-latency regions
- structurally weak buildings
- unstable carrier links
- priority sites for remediation
Phenisys’ solution also provides site compliance indicators to quickly assess the overall health of each location.
This matters because call quality issues in Teams are rarely global — they are almost always geographical.
Conclusion: a Microsoft Teams call center becomes manageable through four pillars
Monitoring a Microsoft Teams call center isn’t about collecting random metrics.
It’s about connecting four essential dimensions:
- real agent usage
- AA/CQ routing flows
- end-to-end call diagnostics
- site and subnet performance
This unified understanding makes it possible to interpret imbalances, optimize call distribution, reduce abandons, and improve perceived call quality for both agents and customers.
When these four use cases come together, Teams call centers gain clarity, operational efficiency, and the ability to anticipate issues rather than react to them.
FAQ – Microsoft Teams Call Center Monitoring
How can I track agent activity in a Teams call center?
By monitoring call volume, peak hours, and distribution across teams or sites — the foundation of effective Teams Call Center Monitoring.
How can I understand Call Queue and Auto Attendant flows?
By visualizing call paths: priorities, abandons, overloads, and agent availability — key for Teams Telephony monitoring.
Which metrics explain Teams call quality?
Latency, jitter, packet loss, and media protocol. These are central to Teams call quality diagnostics.
How do I analyze a Teams call end-to-end?
By correlating data via a Correlation ID (distributed trace), as offered by some Teams observability solutions — including Phenisys.
How can I detect sites that degrade Teams call quality?
Through multi-site Teams monitoring: site-level KPIs and performance maps highlight problematic locations instantly.
