Your KPI Improved. So What? Using AI to Put Your Analysis in Context

One of the first things we learn as analysts is to compare performance against history.

Is revenue growing?

Is conversion improving?

Are registrations higher than last quarter?

Is the completion rate better than last year?

We pull the data, calculate the metrics, build the chart, and find the trend.

And then someone in the meeting asks a very simple question:

“Is that actually good?”

That’s where things get interesting.

Imagine you’re analyzing a website form. You look at the historical data and discover that the completion rate is 68%.

Your analysis tells you that this is a significant improvement over your historical average. Maybe it’s even the best result you’ve seen in several years.

So you put it in your presentation.

Then an executive asks:

“How does 68% compare with other companies?”

Suddenly, your analysis has reached the limit of what your internal data can tell you.

You know how you’re doing compared with yourself.

You don’t necessarily know how you’re doing compared with the market.

This is one of the areas where I’ve found AI research agents particularly interesting.

Instead of simply asking AI, “What’s a good form completion rate?”, you can give it a research assignment.

Tell it what you’re measuring, what industry you’re in, who your audience is, what type of form you’re analyzing, and how you’re defining completion.

Then ask it to research relevant benchmarks, prioritize credible and recent sources, compare the different findings, and explain whether those benchmarks are actually comparable to your situation.

That’s a very different question from asking AI for a number.

You’re asking it to investigate.

And that’s important because there probably isn’t one universal answer.

A B2B form isn’t necessarily comparable to a consumer checkout form. A short registration form isn’t the same as a ten-field lead-generation form. Traffic coming from an email campaign may behave very differently from traffic coming from paid search.

Even the definition of “completion rate” can vary.

So the goal isn’t to find a magic benchmark and declare, “We’re good.”

The goal is to understand the context around your number.

Maybe your 68% completion rate is excellent compared with comparable organizations.

Maybe it’s average.

Maybe it’s below the benchmark you should be targeting.

Or maybe the available research isn’t comparable enough to make a confident conclusion.

That last answer is valuable too.

As analysts, we’re sometimes uncomfortable saying, “We don’t have enough information to know.”

But that’s much better than presenting a benchmark that sounds authoritative but isn’t really relevant.

This is where I think research agents can become a useful extension of an analyst’s work.

Your internal analysis answers one question:

How are we doing?

External research can help answer another:

How are we doing compared with the world outside our organization?

And together, those two perspectives can lead to a much better business conversation.

Imagine going into a meeting and saying:

“Our completion rate is 68%, which is seven points above our historical average. I also looked at external benchmarks for comparable B2B experiences. The available research suggests we’re performing around the middle of the observed range, although the studies vary in how they define completion. Based on the closest comparisons, there may still be an opportunity to improve.”

That’s a much stronger statement than simply saying:

“Our completion rate improved to 68%.”

The number is the same.

The story is different.

And this is one of the reasons I’m becoming more interested in AI research agents. They can help us expand the scope of an analysis beyond the data we already have.

But I don’t think we should blindly trust the research either.

The analyst still has an important job.

You need to ask where the benchmark came from. Is the source credible? How recent is it? Does it measure the same thing? Is the population comparable? Are there differences in geography, industry, audience, device, or acquisition channel that could make the comparison misleading?

AI can help you find the information.

You still need to decide whether the information makes sense.

That distinction is important.

The value isn’t in having AI give you another number.

The value is in using AI to help you ask a better question about the number you already have.

Because sometimes the most important question after finding an insight isn’t:

“What happened?”

It’s:

“Compared with what?”

That’s when your analysis starts moving beyond reporting performance and toward actually understanding it, so use AI to Start with Data, End with Value.