Raleon builds customer segments by starting with behavioral signals like repurchase readiness, not predefined rules. It pulls data from Shopify and Klaviyo, uses AI to enrich it with insights, then automatically tailors those signals per brand to create flexible, custom segments with more transparency than before.
What raw data does it ingest? What kind of math or algo happens underneath the hood? And essentially, what are we seeing on the front end? Because to me it looked like magic and I don't know what happened on the back end.
We've had to like scale back the magic a little bit and like remove some of the blackness from the black box. But like the way that we approach it is pretty different than others. So kind of what I was alluding to before, we start really not with the segment, but with the behavioral signal. So these signals are actually what make up a segment overall. So in an example of a signal, for instance, is like somebody who's ready to rebuy the same product. So the way that our AI then builds these signals is it all starts with, you know, with data. Kind of what you were getting at, Chase. So we pull data from Shopify, from Klaviyo, from your website. You know, we enrich it with our own AI insights from Raytheon, and we do it all automatically on a per-brand basis. So it's as specific and kind of pre-tuned, if you will, as possible. And the result that you get as a result then is like these very specific behavioral signals that can then be mixed and matched in more of these unique ways that we're not necessarily as used to into a segment.
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