What product analytics can't tell you about work

The product analytics market has spent the better part of a decade building tools that answer narrow questions well. Whether the software is creating friction, whether employees are adopting new tools, and where bottlenecks show up in a documented process. These are real questions, but there’s a more important one sitting behind all of them that the market still hasn’t built a serious answer for: Why does work take as long as it takes, and what would it look like if it didn’t?

The category gets you partway there

Product analytics tools, digital adoption platforms, task miners, process miners—each of these was built with consumer-facing logic at its core. Funnels, journeys, conversions, rage clicks. These metaphors were developed to understand how external users move through digital products, and they’ve been repurposed, with varying degrees of success, to analyze how employees move through internal tools.

The repurposing only goes so far.

Consider a proof of concept at a large contact center. By every standard product analytics metric, the environment was clean. Zero friction, no rage clicks, no thrash cursors, no long screen load times. The tools looked fine. But human agents were copying and pasting an average of 50 times per case, and half their working time on every single case was manual data transfer between systems that had never been integrated. The tools weren’t the problem. The work was the problem. And the entire category of tools designed to evaluate that environment had no way to see it.

That’s not a failure of any single product. It’s a structural limitation of what the current category was built to do.

The questions customers are actually asking

When enterprises get serious about workforce productivity, the questions they bring aren’t primarily about software performance. They want to know:

• Why does a process that should take 20 minutes take two hours?

• Why does a quarter of all tickets require rework?

• Why do certain workflows require senior resources when they shouldn’t?

These are throughput questions, adherence questions, process quality questions, and at their core they’re financial questions because every hour of unnecessary process time carries a cost, whether organizations can see it or not.

Product analytics addresses one dimension of this. It can tell you whether a tool is contributing to the slowdown, and that’s worth something. But it’s one dollar for every five that enterprises will spend to understand the nature of the work itself. The process, the orchestration, the hand-offs, the breakdowns that happen with no digital friction anywhere in sight.

The contact center agents had clean tools and broken work. What they needed wasn’t better instrumentation of their software. They needed visibility into why each case required a human being to act as the integration layer between a dozen disconnected systems. That’s a different question entirely, and the current market doesn’t have a good answer for it.

Product analytics is aspirin. It addresses real symptoms and it helps. But the nature of work itself, the process beneath the tooling, the invisible labor that no click map or session replay will ever surface—that pain is still largely untreated.

Why assembling a stack doesn’t close the gap

The natural enterprise response to this problem has been to assemble. A product analytics platform for tool instrumentation, a digital adoption platform for guidance and training, a task miner for low-level activity capture, a process miner for higher-level workflow mapping. Four tools, four data models, four partial answers to one complete question.

The data quality issues compound the problem. Process miners run on log data, which has gaps everywhere, while task miners work from something closer to analog-quality input. AI is interpreting visual recordings of work rather than the underlying digital activity itself. Each tool sees what its collection mechanism allows it to see, and none of them were designed to see the whole picture.

What enterprises end up with is four partial views that someone has to reconcile, and that reconciliation falls to a person. Which means the stack designed to surface invisible, unnecessary labor has quietly created its own version of it. If you’re going into the woods and you can only bring one knife, you want the one that handles more than one problem, not because it’s the best at any single job but because you’re not going to face only one kind of problem out there. Enterprises never do.

The gap that workforce technology hasn’t closed

The workforce tech category has made real progress, and enterprises genuinely have better visibility into their tools than they did five years ago. Some bottlenecks that were previously invisible are now findable, and that matters.

But the contact center agent is still out there, copying and pasting, acting as the human integration point between systems that should’ve been connected long ago. Invisible to every tool in the stack because no tool in the stack was built to see that kind of work. The question worth asking isn’t which point solution answers its narrow question best. It’s what kind of foundation makes the full productivity question answerable at all.

That’s a different evaluation. And it’s one the market is only beginning to have.