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I started my career as an agent. Funny enough, a lot of the metrics we used to run contact centers back then are the same ones we use today.<\/p>\n
AHT. FCR. CSAT. NPS.<\/p>\n
I don’t think they’re going away, and I don’t think they should. They all tell you something useful.<\/p>\n
The problem starts when a metric stops being an indicator of success and becomes the goal itself.<\/p>\n
Measurement drives behavior<\/h3>\n
If I tell an agent the most important thing is getting off the phone in six minutes, they’ll optimize for six minutes.<\/p>\n
If I tell an AI system its job is to maximize containment, it’ll maximize containment.<\/p>\n
Neither is doing anything wrong. They’re doing exactly what we asked. We just never connected those targets to resolution, retention, or revenue.<\/p>\n
Now picture every part of the operation chasing something different:<\/p>\n
- \n
- The AI agent is optimized for containment: no handoff to a person<\/li>\n
- Routing is optimized for speed: the next available agent<\/li>\n
- WFM is optimized for utilization: every agent kept busy<\/li>\n
- The business wants retention: keep the customer from leaving<\/li>\n<\/ul>\n
Every team can hit its target while the customer journey gets worse. And most organizations aren’t set up to see it. Today, only 22% of contact center leaders<\/a> say their AI environment is fully integrated into an end-to-end strategy.<\/p>\n
Contained isn’t resolved<\/h3>\n
Containment is a useful measure. It can lower operating costs and clear routine interactions quickly. The trouble starts when it becomes the only thing being measured, because the downstream effects go unseen.<\/p>\n
Let’s say a customer is disputing a charge. They start in self-service. The AI can’t solve it, but it keeps trying because it was designed to maximize containment. Eventually, the customer gives up.<\/p>\n
On the dashboard, that looks like a success. The conversation never reached an agent.<\/p>\n
Then the customer calls back the next day with the exact same issue, and this time they want a person.<\/p>\n
You haven’t eliminated the work. You’ve created another interaction, added frustration, and probably made the human conversation harder.<\/p>\n
When I see that 49% of customers don’t see AI actively improving the customer experience<\/a>, this is usually what I think leaders are missing.<\/p>\n
My simplest advice is this: follow the customer, not the interaction.<\/strong><\/p>\n
- \n
- Did they come back an hour later?<\/li>\n
- Did they call the next day?<\/li>\n
- Did they switch channels and start over?<\/li>\n
- Did they demand a human until they got one?<\/li>\n
- Did they abandon without their issue resolved?<\/li>\n<\/ul>\n
If they did, you didn’t remove the effort. You just moved it.<\/p>\n
Customers already vote with their feet here. Only 17% would consider abandoning a phone call with a person, compared with 38% for a chatbot and 40% for a self-service portal or app<\/a>.<\/p>\n
And a handoff to a person isn’t a failure. If AI recognizes that a human is better placed to help and passes the context along, that can be a much better outcome than keeping someone contained for another five minutes.<\/p>\n
“The goal shouldn’t be to keep the customer in AI. The goal should be to use AI to get the customer to the right outcome.”<\/em><\/p>\n
Give every efficiency metric a partner<\/h3>\n
If I could make one practical change to most scorecards, it would be this: never look at an efficiency metric on its own.<\/p>\n
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