---
title: How do Orita segments differ from my Klaviyo segments? Why can't Orita segments be built in-house?
description: Standard segmentation is binary and manual. Orita scores every profile nightly using every Klaviyo event; predicting both likelihood to engage and likelihood to purchase because of an email.
---

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# How do Orita segments differ from my Klaviyo segments? Why can't Orita segments be built in-house?

Standard segmentation is binary; if someone clicked an email in the last 30 days, they're in. If not, they're out. So to compensate for potentially missed profiles, marketers broaden time windows to 90 days, 180 days. They start layering in site activity, purchase behavior, email opens, more conditions, more complexity. And all of that is still just manually predicting what behavior signals intent.

Orita does predictive pattern recognition at a scale and precision that no manual segmentation logic (or other AI segmentation solution) can match. It looks at every event from every profile in your Klaviyo account, factors in historical buying patterns, seasonal engagement cycles, and weighs all of those signals together into a single propensity score for every contact, benchmarked against your entire audience and tuned to your brand's own behavioral patterns.

There are a lot of ‘propensity to buy’ models, like RFM, out there. Orita is unique because it is focused on two things:

▪ Propensity to engage with an email

▪ Propensity to purchase because an email was received.

[WATCH](https://www.youtube.com/watch?v=jAnOVSGOkPg) - Why you should use machine learning for customer segmentation

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