📊  When Your Data Doesn’t Tell the Truth

How Fin’s AI Categorization Turned Messy Data into Clarity

Every CX or ops leader knows the feeling. You pull up a report, and at first glance the numbers look fine. But something feels off. The story in the charts doesn’t line up with the reality on the floor. The problem isn’t the report—it’s the tags feeding it. When tagging is inconsistent, the data turns unreliable. And unreliable data makes reports lie.

That was the bind this team found itself in. They needed to spot product issues early—like which models were most likely to crack, which parts kept showing up damaged or missing, or which shippers were the least reliable. With that kind of clarity, they could protect revenue, get ahead of problems, and spare customers unnecessary pain. But with inconsistent tags, they weren’t seeing the full picture.

The pivot: Stop Wrestling with Tags. Start Tracking Reality 🔁

Here’s the move: instead of drafting yet another tagging framework with sprawling branches that were painful to manage and impossible to scale, I scrapped tagging altogether and leaned on Fin’s AI categorization.

Instead of stopping at routing, I used Fin to capture structured details—issue type (faulty, damaged, missing part, etc.), product, part, specific issue, even SKU. And when a conversation involved multiple products or issues, the system flagged that complexity so nothing slipped through the cracks.

The difference? Agents no longer carried the burden of tagging in the middle of helping customers. They just confirmed the attributes before closing the conversation. And because attributes were required, the reporting was clean—by design..

Bridging the historical gap 🕰️

That solved the problem going forward: we could finally trust the trends being captured in real time.

But then came the next question…

What about the past?

That was exactly what my client asked me once the new setup was in place:

“Can we do this retroactively?”

The short answer was no—AI categorization can’t backfill old tickets. But by connecting the Intercom MCP to Claude, I could still dive into historical conversations and surface similar insights. That deep dive uncovered the long tail view: seasonal spikes, recurring model-specific problems, and the real drivers of inbound volume.

The discovery inside the discovery 💡

That’s where things got interesting. My analysis showed nearly two-thirds of inbound conversations clustered around a recurring issue across a handful of models.

Armed with that insight, I enhanced their Compassionate Macros* library. These weren’t canned replies—they were built from the best real-world agent responses. The most trusted tones, the clearest instructions—those touches were blended into model-aware, proactive responses that every agent could use.

The result? What once took hours of back-and-forth now took seconds—and attributes were captured automatically in the background, feeding clean reporting without extra effort.

The impact 📈 

Agents: less cognitive load, faster resolutions.

Customers: quicker answers that felt consistent and personal.

Leaders: trustworthy trend data they could finally act on—whether with suppliers, product teams, or ops.

One Intercom setup now served CX, Ops, and leadership alike. Not just ticket handling, but a system that made the whole business smarter.

Zooming out 🔍

The real problem was obvious but messy: tags weren’t giving us data we could trust.

By the end, Fin’s AI categorization was doing the heavy lifting, and agents just had to confirm. Clean data, lighter workload, better reporting—and a setup that scaled without the monstrous branching tagging used to demand.

And the best part? Together, we built a system that was finally working for the people—not the other way around.

When your tools do the heavy lifting, your team can focus on what really matters—listening, solving, and building trust.

Note: This project was built using a version of AI Categorization that’s no longer available in its original form. The exact features shown here may look different in newer versions, but the lesson stands: when systems capture structured details automatically, reporting becomes trustworthy by design.

*💙 Compassionate Macros © is a framework I’ve developed to help teams build response libraries from their best real-world agent replies—so answers feel both consistent and human

If this sparked ideas for your team, I help companies get more out of Intercom + Fin.

Feel free to reach out — I’d love to hear what you’re working on.