How to Incorporate Voice of the Customer Feedback Into Your Operations

Most companies believe they understand their customers. They’ve got sales teams talking to prospects daily. Customer service reps fielding customer calls. Account managers checking in regularly. And from all of those interactions, a narrative forms about what customers want, what they value, and what drive their decisions. This is tribal knowledge, and it’s valuable. But it’s also slow, incomplete, and often wrong.I’ve spent years helping organizations solve problems, and one pattern keeps showing up: leaders making strategic decisions based on assumptions about their customers that have never been systematically validated. They’re confident in their understanding because people on the front lines tell them what customers care about. But when you actually analyze what customer are saying, not what your team thinks they’re saying, the picture often looks different.

The gap isn’t always dramatic. Tribal knowledge can point roughly in the right direction but even when it does, you’re still waiting to see how customers respond to changes you’re implementing based on those assumptions. You change something (or launch a product/service), cross your fingers, and hope the market validates your hypothesis.

What if you could validate before you build?

The Opportunity Most Companies Are Missing

Here’s what’s changed: many companies are already recording customer calls. Some are even monitoring them in basic ways, checking for compliance, reviewing escalations, spot-checking quality. But very few are leveraging those recording to systematically understand what’s actually happening in those interactions.

We now have AI tools that can analyze hundreds or thousands of customer conversations and surface patterns that would take humans weeks or months to identify manually. These tools aren’t perfect but in my experience can reliably get us 80% of the way there. The applications are broader than most people think, for example:

• Marketing and sales teams can understand how prospects actually talk about your industry or products/services
• Product development can identify which features customers value most and which create friction
• Operations can spot process gaps that customers experience but never formally complain about
• Strategic planning can validate assumptions about competitive positioning before committing resources

We’re not talking about replacing human judgement or tribal knowledge. We’re creating systems (planning, people, process, technology) that provide quantitative outputs based on qualitative inputs meaning we can turn subjective customer conversations into objective data that informs all aspects of our businesses.

A Real Example: from call recordings to process improvements

I’m working with a client who recently implemented this type of system. They were already recording calls but had no systematic way to analyze them beyond occasional spot-checks. We built out a solution using AI to analyze transcripts and look for specific patterns.

The prompts we created asked the tool to surface:

• Common customer complaints or points of confusion
• Missed opportunities where the team could have delivered more value
• Calls where interactions could have been handled better
• Moments where the team went above and beyond

Within the first analysis cycle, they identified several coaching opportunities. Not vague “be better at customer service” feedback, but specific examples of where customers were getting confused about their process, where handoffs were creating friction, or where team members were missing signals that a customer needed something different.

Because they had both the transcripts and the original recordings, they could verify the AI’s analysis and dive deeper when needed. The AI would output the date and time of relevant calls, and a human could go listen to the full recording to understand the complete context.

This led to several targeted process improvements that closed gaps they didn’t even know existed. It also helped them spot positive patterns, calls where team members handled difficult situations exceptionally well, which became teaching moments for the broader team.

The key difference from traditional quality monitoring? They weren’t just checking compliance or catching obvious problems. They were identifying systematic patterns across hundreds of interactions that revealed how customers actually experience their business.

Your Problem Statement is Everything

I wrote about why weak problem statements keep the same issues coming back last week, but here’s where most companies go wrong: they get excited about the technology and want to “implement AI” or “leverage customer feedback” without first defining what problem they’re actually trying to solve.

This is the same mistake leaders make when they jump to solutions before clarifying the actual problem. The technology doesn’t matter if you don’t know what you’re using it for. When I work with clients, we start with a clear problem statement. Not “we want to better understand our customers,” that’s too vague. Something like:

“There is gap between what we think is important to our customers and what is actually important to them.”

Let me give you an example. You might believe cost is the primary driver for your customers. But when you systematically analyze customer conversations, you discover that many customers are willing to pay more to get their product faster. So which is it, cost or speed? What percentage of your customer base values one over the other? What should be your primary value proposition?

Another client I’m talking with right now has exactly this gap. They want to better understand their customers, but they’re starting too broad. We’re working to narrow the problem statement to something more specific like:

“We don’t have a clear picture of how our customers talk about our industry or products. We can’t clearly define the language they commonly use, the specific problems they’re trying to solve, and what differentiates our solution in their minds.”

Once we have that clarity, the AI prompts become straightforward:

• “How do our clients talk about our industry or products? Give me 3-5 exact quotes related to this.”
• “What specific problems are customers trying to solve when they engage with us?”
• “When customers compare us to alternatives, what criteria do they mention?”
• The problem statement directly informs what we ask the system to surface.

Moving beyond surface-level insights

One trap I see companies fall into is collecting feedback but only capturing surface-level information. Customers say they want “better quality” or “lower cost” or “faster delivery.” Every company hears these things. They’re table stakes.

The deeper question is: what do customers actually value most about working with you? Why do they choose you over your competitors? What trade-offs are they willing to make?

Surface level: “Customers care about cost and quality.”
Deeper insight: “Customers are willing to accept minor cosmetic defects if it means two weeks faster delivery, but will not tolerate any functional defects regardless of timeline.”

That second statement is actionable. It tells you exactly where to focus your quality efforts and how to structure your production schedule. The first statement tells you nothing you didn’t already know.

Here’s an example from my own business. Many consultants operate under tribal knowledge that says clients primarily care about rates, what you charge per hour or per project. And cost does matter. But when I talk to prospects and clients, the real drivers are speed to value and risk mitigation. They want someone who can deliver results quickly and won’t create more problems than they solve. As long as they perceive value and believe I can deliver, cost is rarely the deciding factor.
If I had based my positioning on the tribal knowledge from other consultants, I’d be leading with competitive rates instead of emphasizing rapid implementation and proven methodologies that reduce risk.

From Reactive to Proactive Problem Solving

The real power of systematic VOC analysis is the shift from reactive to proactive operations.

Without these systems, you’re typically learning about customer needs in one of two ways:

1. Customers complain directly (reactive)
2. You implement changes based on tribal knowledge and see if customers respond positively (slow feedback loop)

With systematic analysis, you can:

• Identify emerging patterns before they become problems
• Validate assumptions before committing resources
• Prioritize improvements based on actual customer impact rather than internal opinions
• Spot opportunities your competitors are missing because they’re not listening systematically

Let me give you a specific example of how this changes prioritization. Say your leadership team is debating whether to invest in improving your scheduling system or upgrading your shipping logistics. Both require significant resources. Both would deliver value. But which should you do first?

Traditional approach: You debate internally, maybe send out a survey, perhaps ask a few key accounts. Someone’s opinion wins, usually based on who argues most forcefully or has the most organizational capital.

Systematic VOC approach: You analyze your call recordings with prompts like “Give me examples where customers reference lead time, shipping, delivery, schedule, or timing.” The AI surfaces that 67% of customer friction points relate to scheduling uncertainty, while shipping speed is mentioned positively in 73% of calls. The data tells you: customers are generally happy with your logistics but frustrated by scheduling. Clear answer on where to focus first.

This helps you move from the scatter shot approach so many leaders take, chasing whatever problem seems urgent this week, to prioritizing based on what will actually increase value for your customers.

The Dynamic System: Solving One Problem, Then Moving to the Next

Here’s what makes this approach powerful over time: the system can evolve as your business needs change.

Once you’re confident you’ve addressed one problem, maybe you’ve validated that delivery speed matters more than cost and you’ve adjusted your operations accordingly, you can shift your problem statement and focus on a different aspect of your business.

You might start with: “We need to understand what customers value most in our offering.”

Then move to: “We need to identify operational gaps that create customer friction.”

Then: “We need to understand how customers talk about our competitors and what we do better.”

Each problem statement informs new AI prompts, new analysis, new insights. The system isn’t static. It’s a continuous feedback loop that keeps you connected to what customers actually experience and value.

Who’s Ready for This and Who Isn’t

I’ve noticed a pattern in which companies are implementing these systems successfully. It’s not necessarily the biggest companies or those with the most sophisticated operations. It’s companies that are already experimenting with AI and automation, leaders who understand the power of these tools and are grounded in real problems to solve.

The companies that struggle fall into two camps:

First, those who aren’t ready to record calls or collect systematic feedback at all. This entire approach requires some baseline data collection. If you’re not willing to implement that infrastructure, none of this matters.

Second, those who are so far down the AI hype path that they’ve lost sight of actual problems. They want to “implement AI” but can’t articulate what business outcome they’re trying to achieve. They’re building custom tools because they can, not because those tools solve a specific problem better than alternatives.

The discipline that grounds all of this, my business included, is the problem statement. Sure, I can build an AI agent. But what exactly do I need it to do? How will it help my clients? Just because I learned how to build something doesn’t mean I have a legitimate problem that tool is best suited to solve.

This is the same principle I apply everywhere in my work: don’t skip steps. Don’t jump to solutions before you understand the problem. And don’t implement technology just because it’s available.

Getting Started: What You Actually Need

If you’re considering implementing systematic VOC analysis, here’s what you actually need:

Infrastructure:

• Call recording capability (if customer conversations are your primary feedback source)
• Storage for recordings and transcripts
• AI tools capable of analyzing text at scale

Process:

• Clear problem statement defining what you’re trying to learn
• Structured prompts that translate your problem into specific queries
• Human review process for validation and deeper investigation
• Regular cadence for analysis and reporting

Mindset:

• Willingness to have your assumptions challenged
• Commitment to acting on insights, not just collecting them
• Understanding that this is ongoing, not a one-time project

The companies that get value from this aren’t necessarily the biggest, those with the best technology or most data. They’re the ones who are clear about what they’re trying to learn and willing to change based on what they discover.

Beyond Just Listening: Creating the Feedback Loop

Here’s what separates companies that just collect feedback from those that actually use it to improve: the feedback loop.

Collecting data is step one. Analyzing it is step two. But step three, actually implementing changes based on what you learn, is where most organizations fail. They generate insights, acknowledge them, maybe even discuss them in meetings. But then nothing changes.

The discipline required to close that loop is the same discipline required everywhere in operations: clarity about what you’re trying to accomplish, consistency in how you approach it, and accountability for following through.

This is where the problem statement becomes essential again. If you’re vague about what you’re trying to learn (“better understand customers”), you’ll be vague about what action to take. If you’re specific (“determine whether speed or cost is the primary value driver for our customer base”), the action becomes obvious once you have the answer.

Final Thoughts

Most of your competitors are sitting on the same data you are. They’re having customer conversations every day. Some are recording those calls. A few are even analyzing them occasionally.

But very few are systematically extracting insights and using them to drive strategic decisions. Very few have built the infrastructure to turn qualitative conversations into quantitative data. Very few have the discipline to define clear problems, analyze specifically for those problems, and act on what they learn.

What will you do with this opportunity?

That’s it for today.

See you all again next week!

Dave