+45% satisfaction from 30 phone calls
What thirty same-day phone calls found that no dashboard could see.
One question has trailed the AI wave into every product conversation this year: why bother with customer discovery when you can build the thing in a weekend?
The answer got clearer, and it points the wrong way for most teams. Building got cheap. Building the wrong thing stayed expensive. Over half of new features fail to deliver the value teams expected, and AI just made it possible to produce those failures faster. The scarce skill moved from building to choosing what deserves to be built.
The discipline that survives is strict. Never ask customers what they want or what they would do. People answer politely and imagine a better version of themselves. The answers mislead you without a single lie in them. Ask instead what they did the last time the problem actually hit. It is even better to observe it if you can. Past behavior is the only evidence that holds up.
I have applied that discipline inside engineering and operations teams for over 15 years. This one engagement here shows exactly how it plays out.
The diagnosis
An IT operations manager at a bank ran 17 technicians across 12 office buildings, handling phone and printer incidents. Clients were unhappy and he knew it. Complaints reached him through directors, forwarded emails, hallway remarks. But he had no data on what exactly was broken and no way to prioritize. There was frustration everywhere, but he did not see any leverage.
I’ve met support and platform teams living that situation. Feedback reaches them constantly. They never get a signal they can act on.
The countermeasure
We built a structured Voice of Customer collection with the team. It contained seven questions, asked by phone, each anchored to one specific incident that had just closed:
Did the technician resolve exactly what you needed? Explain.
How did the resolution time feel to you? Tell me more.
Did the process waste your time? Elaborate.
Was your request fully addressed?
Rate this resolution, 1 to 10.
If under 10, what was missing to reach 10?
From your side, how should this ideally have been resolved?
Let’s focus on the design of the questions. There is nothing that asks for an opinion about “our service” in general. Question 5 rates one resolution, at one moment. Questions 6 and 7 pull out the hidden need behind the stated one: a client who asked for a faster printer fix actually wanted to know his ticket was not lost.
The mechanic matters as much as the questions. I called clients the same day their incident closed. I reached thirty of them over a few days. Same-day memory gives you concrete detail and zero recall bias. For me, it’s interviewing while the truth is still warm.
The case
The calls produced the first real baseline: satisfaction at 6.4 out of 10, 17% of incidents resolved within two hours (which was the SLA), 19 incidents per day (the volume). Two issues dominated the verbatims: clients never knew where their ticket stood; and too many incidents should never have happened in the first place.
Each fix answered one of the two findings. Clients didn’t know where their ticket stood, so the team put up a live board showing the status of every open incident, visible to technicians and managers at all times. We saw no more repeated calls from clients. Too many incidents were happening, so a structured root-cause cycle (PDCA) traced a large slice of the daily volume back to cabling problems. The team put preventive measures in place. On top of that, technicians started assigning work by impact instead of arrival order, and trained themselves on a “standard” diagnosis routine so resolution quality stopped depending on who picked up the ticket.
Three months later, client satisfaction stood at 8.7, a 45% jump. The two numbers underneath explain why it moved: two-hour resolution climbed from 17% to 31%, and daily incidents fell from 19 to 12. Those are exactly the two frustrations the calls had surfaced, and they were pushed in the right direction. All that happened with zero new hires, and zero new tools.
That engagement predates the AI wave, and since then, the lesson has sharpened. AI multiplied your capacity to produce. It added nothing to your capacity to know what is worth producing. Seven questions and thirty phone calls here still beat any dashboard at that job.
If you want to see where your own system leaks, the Delivery Scorecard takes two minutes. Ten questions, and you know where to look first. And if defining value from your customer’s side is the wall your team keeps hitting, I run a half-day working session with leadership teams. Reply to this email, or message me directly on Substack, and I’ll send you the outline.
P.S. AI made building cheap. It did nothing to the cost of building the wrong thing. That gap is where Voice of Customer earns its keep.