Your best customers, finally as one person.
You see who your best customers actually are, put the right segment in front of the right campaign, and tell which channels make you money. Every order, ticket, and reward across your store, point of sale, email, loyalty, support, and ads resolves into one customer you own.
The same shopper becomes one customer you own.
The model resolves the same person across the store, the point of sale, email, loyalty, support, and ads into one golden record. Lifetime value sums every order she places, online and in the store, so your best customers stop hiding behind six separate logins.
Open a record and every order, ticket, and reward traces back to the system it came from. The figures fall out of that one record: lifetime value, full purchase history, and the next-best product the model scores for her from what it already agrees on.
Maya Lindqvist
One golden record, resolved across 6 channels
Online store
- Orders
- 31 online
- Online spend
- $3,910
- Avg. order
- $126
- Last seen
- 92 days ago
Point of sale
- In-store visits
- 11
- In-store spend
- $1,470
- Home store
- Pearl District
- Last visit
- Mar 2026
Email / marketing
- Subscribed
- Yes · 4 yr
- Open rate
- 58%
- Last opened
- 6 days ago
- Last clicked
- Spring drop
Loyalty
- Tier
- Gold
- Points
- 3,240
- Reward ready
- $25 off
- Joined
- 2021
Support
- Tickets
- 3 · all resolved
- Last contact
- Sizing, May
- Returns
- 2 of 42 orders
- Sentiment
- Positive
Ads & analytics
- First touch
- Paid social
- Sessions
- 186
- Attributed spend
- $640
- CAC payback
- Recovered
Segments — synced to campaigns
Top 5% by lifetime value · in front of the loyalty campaign
92 days since last order, well past her 41-day average
Queued for the winback flow with a category offer
Channel margin — revenue she drives, after returns & discounts
Paid social won the click but barely the margin. Email and direct carry her value.
The resolved customer builds the segment and points it at the campaign.
The same record that prices lifetime value drives the marketing. It groups your high-value loyalists, catches a customer slipping toward churn against her own pattern, and pulls the lapsed buyers worth winning back. Each segment lands in the email or ad flow built for it.
You get a packaged audience for every campaign, and the model keeps each list current as orders, tickets, and rewards change underneath it.
Segments synced to campaigns
Built from the resolved customer. The model keeps each list current.
You catch the customer slipping away and draft the offer that brings her back.
The model reads the resolved record for the early signs: a buying pace that has fallen off her own pattern, an order that lapsed past her average gap, the support friction that often comes first. Each at-risk customer carries the reason she surfaced, weighted by lifetime value, so you work the ones worth saving.
For each cohort an agent drafts the winback offer against what she actually bought and writes it to the flow in your marketing system, where a person approves it, capped at one send per customer. It drafts from the record that already agrees and acts under your permissions, so what you approve is grounded.
At-risk cohort, detected off the record
Falling frequency, lapsed orders, and support friction, each traced to the customer.
Your agent opens a ticket and sees the whole customer behind it.
The model reads the support inbox and pins the resolved customer beside the open ticket: lifetime value, every order, and the prior tickets that set the context. Your agent answers a top-decile customer with a stalled return knowing exactly who she is, and an agent drafts the reply from the order and refund record and writes it back to the ticket for a person to approve in your helpdesk.
The friction on that ticket does not stop at the inbox: it feeds her at-risk score, so the same record that runs support hands her straight to the winback queue.
Return on the spring jacket still not refunded
I sent the jacket back two weeks ago and have not seen the refund. This is the second time a return has stalled.
Refund on order #9043 is released today, with a $25 credit for the delay. The model drafts it from the order and refund record and writes it back to the ticket.
Order history
- Orders
- 42 · $5,380
- Last order
- 92 days ago
- Open return
- #9043 · $148
Standing
- Tier
- Gold · top 5%
- Prior tickets
- 3 · 2 returns stalled
Friction → at-risk signal
This ticket raised her risk; she enters the at-risk cohort and the winback queue.
True margin by channel and product, after returns and discounts.
The model reads revenue net of the returns and discounts that ate it, by channel and by product, against the ad spend that drove it. The marketplace clearing volume at a thin margin and the best-seller that quietly loses money both stand out against the rest.
When the marketplace clears volume at a third of the store's margin, the model flags it and drafts the budget shift toward the channels that actually pay; finance approves before any spend changes. Merchandising and finance read the same numbers as a product, current across every channel they sell on.
Margin by channel
Revenue net of returns and discounts, read against ad spend.
Reorder the winners before they sell out, clear the overstock.
The orders that resolve into customers are the same orders that read as demand: no second feed, no separate import. Sell-through and stock cover read from your store and point of sale, current, so the hoodie about to stock out and the beanie sitting on overstock both surface against forecast demand.
An agent drafts the purchase order off that one picture and writes it back to your system, and merchandising approves it before it goes to the supplier.
Stock cover & reorder signals
See it on your own customer data.
Tell us where your customer data lives today. We will show you the one record built from it, the segments it drives, and the margin it reads, run for you.