Demo: Sensible AI Clustering

SensibleAI Clustering Analysis Let's assume I'm an FP&A lead at a manufacturer and retailer of golf clubs and equipment. Our board just kicked off the 2026 planning cycle and has asked me to find another 100 basis points of operating margin without cutting growth investment.

Within one stream, every regional location gets benchmarked against its regional average. South against south, west against west. But here's the problem with that. The stores within a single region don't actually operate the same way.

Footprint size, distance to the nearest competitor, etc. are all over the map, even within the same region. Take these two stores for example. Both in our south region, one is 30,000 square feet with a competitor across the street.

The other is 6,000 square feet with no competitor for 12 miles. They have the same regional target, but those two stores are comparable on anything that drives costs. Now let's watch what happens when we cluster these stores by how they actually operate.

Three real peer groups emerge from the data stores and up ground with their operational twins. Not by where they are in a map, but by how they run. Every feature we use to cluster footprint, competitive isolation, density, product mix was chosen specifically because it drives performance.

Let's zoom in to one cluster. High density, large apparel stores. They are all operationally similar. But this dashed ring inside the cluster marks the top quartile performance in that peer group. How we define that performance matters.

Performance here is the blend of three commercial metrics. Revenue per square foot, revenue per visitor, and gross margin. Better operators will naturally rise to the top. In this store right here, bottom of the cluster on performance runs at 29%, almost double its peers.

Just by closing that gap by 50% means a $935,000 opportunity. If you repeat that process across every store and every cluster, we'll find approximately a $10 million saving opportunity in SG&A across the business.

Let me show you what this will look like inside OneStream. Let's start by looking at performance scores. This is how we tell clustering analysis what makes one store or one group better than another. We can set our performance scores by just sliding up and down these sliders.

SG&A is intentionally not in that definition. This is purely how well each store monetizes its footprint, converts its traffic, and holds its margin. Now let's look at the learnings this enables inside a benchmarking workflow.

We pre-built and ran this workflow configured with the accounts for our OneStream Cube and performance score we just looked at. So here's my answer to the board. Closing just the gap to the third quartile of top performers, the most conservative opportunity is a potential savings of $6.4 million just on this one payroll account.

So our EDEO3 is the headline here. It's a dense market large apparel store, and it has a total opportunity identified of $2.6 million. And there is one thing that most benchmarking tools leave out. For every account where this store has a gap, the system has already drafted plain English guidance for how to close it.

For something like office supplies, It says that a vendor consolidation play that other high-and -density large apparel stores have already run could work for you. This means that my controller doesn't start with just a blank page.

They start with a draft that they can challenge or run with. For a specific store, a specific account, they get my specific recommended action, and a human will still own the call. The system doesn't post the entry.

It gives the team a starting point. So back to the board's question. Another 100 basis points of margin for 2026. From this single workflow against one cost area, I have millions of dollars that I can defend with action plans for every store and every account.

And this is just one of the three workflows available within Clustering Analysis. So there's three things to take away here. One, this analysis ran in minutes, not months. Two, it's finance-owned. That means no data science team and no custom build.

And three, every dollar and every opportunity ties back to a specific entity with a defensible action. From regional averages to operational truth, benchmarks to decisions. That's smarter capital allocation.