Drew D'Agostino

Yeah, we're around like 4.2-ish.

Yeah, let's go.

Crystal is an adaptive selling platform.

So we use personality data and other behavioral insights to help people connect better with their customers and communicate more effectively.

So yeah, we've been around about seven years now.

We've got customers all over the place, but

primarily b2b and if it is consumer it's consumer like very high ticket consumer items really high touch sales processes which are like very relational so not so much transactional or e-commerce or anything like that so should we think of this like i mean it's not cookie oriented data or maybe it is it's more like first party more like a bombora sort of style tool

Yeah, in that lens, it's adjacent.

It's not competing with tools like that.

So Crystal is dealing with behavioral data and specifically like predicted behavioral data.

Give me an example.

So there's a lot of information out there about people.

So if you're looking at a LinkedIn profile or there's lots of enrichment services where you can get someone's job titles, interests, skills, anything that you could use to personalize communication.

So there's a lot of that data out there.

And it makes personalization on one hand possible, but on the other hand, really hard because it's impossible to do all that research and actually put that into action in an efficient way if you're a sales rep or if you're communicating with customers.

So what Crystal does is uses predictive models to take all of that information and then summarize...

and summarize the best approach for that person and then how you can do things like negotiate with them, discuss pricing, really follow them throughout the sales process.

So what we have is machine learning models that say, okay, based on all that we know about them, like the facts, here's how you should present your message and your process.

And that could be based on, we use a model called DISC.

So we've historically been this personality, kind of a personality app.