stream rework points toward something much bigger: the value created by the contact center often shows up somewhere other than the contact center.
Go back to the port cover. Those calls were effectively invisible to the metrics the contact center owned because they looked like routine troubleshooting on its dashboards.
The real value surfaced as warranty claims avoided, in another department ' s profit-and-loss( P & L) statement, on another team ' s budget line, months later. The measurement that mattered wasn ' t a contact center measurement at all.
Accuracy raises a similar question: accurate against what standard? An AI agent can execute a designed flow with high fidelity and still be walking customers down a path that was never the best route to resolution.
Accuracy without a ground truth derived from real conversations measures compliance with our assumptions, not necessarily effectiveness. Containment has the same limitation.
The risk isn ' t the metric ' s existence, but what it counts. Containment records where the interaction stayed; it says nothing on its own about whether the customer ' s need was met.
I would put three questions at the center of measurement.
1. Was the need actually resolved? 2. Did it resurface? 3. How much effort did it take?
Resolution should be verified through conversation intelligence or contextual post-interaction follow-up rather than inferred from the absence of a transfer.
Organizations should also know whether the same issue, for the same customer, resulted in another contact within the next 24 or 48 hours, including through another channel, which requires journey-level visibility.
Finally, repeated explanations, escalation requests, and channel switching all provide important measures of effort, which can help distinguish a resolved contact from a survived one.
We ' ve run the experiment of optimizing around easy measurements before. Average handle time( AHT) was easy to measure and easy to report.
But when organizations over-optimized around it, agents could be pushed to rush customers, avoid research, and dodge escalations.
Easy-to-measure has beaten meaningful-to-measure before, with the costs eventually showing up downstream as repeat contacts, increased effort, and churn.
AHT is a good example: optimizing for shorter calls could make performance look better on paper while driving repeat contacts, greater customer effort, and churn.
Escalation was never inherently failure; it ' s adaptation, and metrics should reward it when it ' s the right call.
The same caution should apply to AI, because if we optimize primarily for keeping customers inside automated experiences, systems may become very good at containment without becoming equally good at resolution.
MEASURE THE DEMAND THAT NEVER OCCURRED
There is another measurement that is harder to see but potentially much more valuable: demand that never occurred. Reduction in downstream rework points directly toward it.
When customer intelligence helps an organization identify a confusing product feature, fix an unclear policy,
CONTACT CENTER STRATEGY
YET THAT AVOIDED INTERACTION MAY REPRESENT MORE VALUE THAN MAKING THE ORIGINAL CONTACT CHEAPER OR FASTER TO HANDLE.
improve a communication, or intervene before a known problem occurs, a future interaction may disappear entirely.
Preventable demand is difficult to measure because nobody gets credit for the call that didn ' t happen unless someone deliberately builds the baseline to claim it.
Yet that avoided interaction may represent more value than making the original contact cheaper or faster to handle. To address this, establish a baseline for repeat or avoidable contacts, link those interactions to their root causes, and track whether volumes decline after process changes.
That is why the measurement challenge matters so much today and especially going forward.
The difficulty isn ' t new technology arriving; it ' s the business change that several years of technology have forced, and the reckoning is with what we choose to count.
The port cover insight was available years ago to any organization willing to look at what customers were already telling them. What ' s changed is that looking has become far easier and far more widely understood as possible, which means the excuse for not measuring what matters is disappearing.
Get the measurement right, and the contact center becomes an intelligence source capable of helping the enterprise become more proactive, prevent problems, and generate value well beyond customer service.
Get it wrong, and we risk spending the back half of the decade unwinding a new generation of metrics that are optimized for the spreadsheet rather than the customer.
Scott leads CallMiner’ s strategy, growth, market expansion, and strategic partnerships, with a focus on accelerating adoption of conversation intelligence and AI-powered automation. With 30 years of experience spanning software strategy, product management, and marketing, he previously held senior leadership roles at MIVA and Corel Corporation.
OCTOBER 2026 43