John Andrews

And if you think about that, if you think about that process on the planning side, right, what we're helping retailers figure out is very specific as they're making their strategic planning decisions, if they're figuring out how much inventory dollars to put towards

You know, following the trends and understanding where understanding where the demand is right.

Understanding should I be going bigger into women's clothes versus men's clothes at the macro level and then figuring out, you know, something just as specific as attributes of specific items for shoes.

Should I be going with.

riding boots versus ballet slippers and what colors are trending, et cetera.

There's, you know, as you mentioned a minute ago, the combinatorial explosion of all of the different decisions that need to be made and the attributes associated with that in terms of, you know, trends changing, the amounts that need, they need to spend.

It's incredibly complex.

The way that these decisions are made today is generally based on gut instinct and Excel spreadsheets, right?

And

Our tagline is bringing science to the art of retail.

It very much still is an art form, especially with fashion retails, which is the area that we've had the most success.

So it's a good question.

There's a, there's a few elements here, right?

When people think about training, one of the things they think about is, is machine learning, right?

In terms of, you know, the, the data that you're bringing through and how are you, uh, how are you optimizing that model?

One of the things that we're doing is, uh, understanding it.

Sure.

You know, you can, you can look at a product that you're looking to sell and, and, uh, train the model to look at images of other products you sold or other data, right.

To kind of come up with a model that's important.

But more specifically, it's getting to a deeper level of detail in terms of understanding customer choice and customer preference.