Tie Predict is AI segmentation built on billions of data points. It scores every shopper on your Klaviyo list, and thousands more your ESP can't see, every day, so your best sends reach the right people while intent is still hot.
Email built on engagement history (who opened last month, who clicked last week) was designed for a world where your opted-in list represented your total purchase intent. That world has ended. Engagement scoring tells you what a shopper did. It cannot tell you what they are about to do.
Apple Mail privacy, signal loss, and list fatigue erode engagement metrics every quarter. The segment you send to keeps getting smaller, and less representative of who is actually about to buy.
A shopper browsing on a new device looks "unengaged" to your ESP because it has never seen that device. They are not cold. They are invisible.
The population of high-intent shoppers who do not appear in your engaged segment expands over time. Every program running on engagement-based segmentation alone has one.
Retention and growth leaders at DTC brands on what changed when they swapped engagement guesses for identity-backed intent.
"Instead of blasting broad segments, Tie Predict helps you hit the right people at the right moment, so you're driving better engagement, higher LTV, lower unsubscribes, and improved account health."
"We tested Tie Predict head-to-head against another solution and ended up capturing the same revenue while sending to 20% fewer people. Less volume, same return, and a list that's actually healthier for it."
"Every brand is running a similar playbook. Predict pulls you out of that and into something actually predictive. We're not just working with purchase history. We're getting scored on real behavioral signals, updated daily. It makes AI actually useful, instead of just marketing talk."
"Predict pulls from signals our Klaviyo data just doesn't have, and you feel the change fast. We saw an 18%+ lift in revenue per campaign."
Here are eight shoppers and everything engagement-based segmentation can see about them. Three will buy this week. Pick the three you would send your best offer to. Then run Tie Predict and see who was actually about to buy.
Every morning, Predict scores your full identified audience, your opted-in list, re-identified anonymous visitors, and suppressed profiles with cross-device signal, on three dimensions. Each lands on the shopper's Klaviyo profile as a property you can segment on today.
How likely this shopper is to buy in the near term. Your primary signal for campaign and flow segmentation.
How likely they are to open if you send. Protect sender reputation by holding back low-intent contacts.
How likely they are to click through. Built for product launches and time-sensitive sends.
The identity graph recognizes anonymous shoppers on your site and matches them to known profiles. This is the Tie ID foundation.
Each morning Predict reads the prior 24 hours of behavior across devices and sessions, and scores every profile.
Three properties land on each profile: tie_predict_purchase, tie_predict_open, tie_predict_click.
Swap "opened in last 90 days" for "tie_predict_purchase 7 or above." Your existing flows do the rest. No new dashboard.
Engagement-based segmentation asks what a contact did inside your ESP. Predict asks what a shopper is doing right now, across every device and session, then enriches it against an identity graph of hundreds of millions of profiles.
Every shopper is resolved to one real identity, then scored on billions of data points. The difference isn't a better model, it's the signal Predict can see, across three layers most tools never reach.
Opens, clicks, order history. What every engagement tool already has, and the ceiling of engagement-based segmentation.
Every product viewed, every session, across devices. Far more of what shoppers actually do than your ESP can capture on its own.
External intent no tool can see alone: shopping for similar products elsewhere, clicking ads for them, opening a competitor's email. The layer that actually moves the score.
Not modeled projections. These are outcomes measured against a control group, on the brand's real list. Every number here traces to a published Tie case study.
No replatforming. Predict layers on top of the Shopify and Klaviyo you already run. Entry is a proof of concept, measured with a holdout, so you see the incrementality on your own data before you commit.
You run a 90-day proof of concept against a holdout. At the end, you have a measured read of the incremental revenue Predict drove, on your list, not an industry benchmark.
In a 30-minute demo, we will show you how many high-intent shoppers your engagement-based segmentation is missing right now, and what it is worth to reach them before they leave.
