This week at Cisco Live in Las Vegas, our CEO Jason Forehand took the stage to share where we believe queue analytics is heading — and to introduce a concept we think changes how operations teams manage customer experience: the Queue Performance Index, or QPI.
For years, ISI Analytics has helped organizations understand what happens inside their call queues across Cisco and Microsoft environments. As adoption of queue analytics solutions has grown, we’ve watched the volume of queue data explode. What we haven’t seen is queue operations get any easier. If anything, the opposite has happened.
Here’s the conviction behind QPI — and behind the session: queue analytics is evolving from reporting to operational intelligence. The future isn’t more dashboards. It’s systems that help teams understand what actually needs their attention.
More data hasn’t simplified operations
Every platform now generates enormous amounts of queue data. Dashboards and reports keep multiplying. And yet operations teams still spend their days manually hunting for problems. Managers spend more time interpreting data than acting on it. Alert fatigue and dashboard overload have quietly become the default operating condition for a lot of teams.
More data was supposed to bring clarity. Too often, it brought noise instead.
And the challenge is compounding
Modern calling platforms — Webex Customer Assist among them — have made it easier than ever to stand up and run queues. But managing those queues well has become harder, not simpler. More queues. Distributed teams. Hybrid work. Fluctuating staffing models. Rising customer expectations.
Most teams are now scaling queue complexity faster than they’re scaling operational visibility. The result is predictable: service issues are often discovered only after customers have already felt the impact.
Why static thresholds fall short
Part of the problem is rooted in how queues have traditionally been monitored. A static threshold treats every queue the same way — but no two queues behave alike.
Consider four queues side by side. A VIP support line running at a 1.2% abandonment rate is healthy. A billing queue sitting at 5.7% and trending upward is a problem worth acting on. A technical queue at 3.1% might simply warrant a closer look. Apply a single fixed rule across all of them and you generate false positives on the queues that are fine, while potentially missing the ones quietly drifting toward trouble. Worse, you train your team to tune out alerts altogether.
Healthy performance, in other words, is contextual.
Introducing the Queue Performance Index
That context is the idea behind QPI. The Queue Performance Index is a dynamic queue health score that learns how each queue normally behaves and surfaces when customer experience is genuinely at risk.
Rather than measuring every queue against a fixed line, QPI evaluates missed calls, abandonment, wait-time stress, and behavioral patterns over time — and weighs them against what’s normal for that specific queue. The goal is straightforward: reduce operational noise, prioritize the queues that need attention most, and help teams act earlier.
Join Next Week’s Office Hours: A Deeper Look at QPI
Want to dig deeper into the ideas behind the Queue Performance Index? Join our next Customer Success Office Hours session on June 10, where the ISI Analytics CS team and CEO Jason Forehand will walk through the topic of this blog and discuss how proactive queue intelligence can help teams focus on the queues that need attention most.
How QPI thinks
What makes QPI different is that “normal” is learned independently for each queue. Instead of asking “did this queue cross a threshold,” it asks a more useful question: “is this queue behaving in a way that suggests customer experience is at risk?”
Priority is based on likely customer impact, not raw metric movement.
In practice, that means QPI is designed to help teams distinguish operational noise from operational risk — which is the single hardest thing to do when you’re staring at a wall of dashboards.
Working the right problem first
Picture the difference. On one screen, the traditional experience: multiple dashboards, competing alerts, red and yellow indicators everywhere, and a manager trying to work out where to even begin.
On the other: a prioritized view — the queues most likely to be affecting customers right now, ranked, each paired with plain-language context like “abandonment increased during the lunch staffing window” or “this queue is behaving differently than its typical Tuesday.”
One of those experiences has you searching for the problem. The other points you straight at it.
The shift from reactive to proactive
- From manual investigation to prioritized action
- From static reporting to adaptive intelligence
- From alert fatigue to focused signals
- From reactive staffing to earlier intervention
Teams should spend less time searching for problems and more time solving the right ones.
Why this matters for Webex and Microsoft environments
We think this shift is especially relevant for organizations running across modern cloud calling platforms.
Solutions like Webex queue reporting and analytics and Microsoft Teams queue analytics already give teams access to rich operational data.
Queue intelligence is what makes that visibility scalable as environments grow.
These platforms provide the foundation. Operational intelligence sits on top as the layer that helps teams actually manage, prioritize, and act on everything they can now see. Together, they enable a more proactive, informed approach to queue operations.
For organizations evaluating this broader shift, we’ve also outlined why customers are moving toward unified call queue analytics — and what they expect beyond traditional reporting.
We’re looking for design partners
QPI is a concept we’re actively developing, and we’re looking for early design partners to help shape it.
If you lead queue operations, work in IT or collaboration, or manage complex queue environments — especially across Webex or Microsoft — we want to hear how you’d use something like this, and where today’s dashboards fall short for you.
- Where do dashboards fall short for your team today?
- What operational decisions are hardest to make with the data you currently have?
- What would proactive queue intelligence look like in your environment?
If you’re at Cisco Live in Las Vegas this week, come find us at our booth — we’d love to show you what we’re building, talk through your queue challenges, and hear what proactive queue intelligence would mean for your team.
The best queue managers shouldn’t spend their day searching for problems. They should spend it solving the right ones.
That’s the future we’re building toward — and we’d genuinely like your input on it.
Interested in helping shape QPI as an early design partner?
Reach out to our team, and we’ll get started!