John Andrews

So let me give you a specific example, right?

A customer walks into a store and they buy a product, right?

Great, every retailer is gonna use that data and that information, that transaction level data to optimize their operations in some way, right?

But what if you knew in addition to what that customer bought, what if you also knew it was available to them when they made that selection, right?

What if you knew what their options were when they made that choice?

Said another day, what if you knew what they didn't buy in addition to what they bought, right?

I now have a sense of the customer preference.

I have a sense of what their intent was when they made that selection.

And when you look at every single interaction with a customer, when you look at the intent when they choose one product over another, and you can do that by understanding what was in inventory in a particular store.

You can do that by looking at browse information.

If you go to a retailer's website and you look at five product detail pages,

but then you only put two of them in your shopping cart, that's context.

We're now able to build up a model, a pretty robust model, we call our choice engine, right?

It's a choice model that allows us to then answer the question, not just what did somebody buy, but what would they have preferred to buy if given the choice across an assortment of products, right?

Because what's happened in the past isn't necessarily, wasn't optimized, right?

Maybe you didn't have

the best assortment in front of them, right?

I bought a purple button-down shirt, but maybe the blue one wasn't available.

And I would have bought that if it was part of the overall assortment.

So being able to normalize against that and then build out that model.