Know which clients are drifting before they're gone.
For salon and barbershop chains. Every month, we score every client you have: who's coming back, who's slipping, and who's due. The scores land in the booking and marketing tools your team already uses.
Send us a year of visit history. We'll send back your client list, scored.
You already know how this goes.
A first-time client books, sits in the chair, and never comes back. Nobody notices for three months.
Your lapse report kicks in at 90 days. A client who comes in every three weeks has been gone for two months by the time she shows up on it.
A coupon goes out and redemptions look great. Half of those clients would have booked anyway. You paid to discount a visit you already had.
A stylist leaves, and her regulars leave with her. You find out at the end of the quarter.
Two locations, same market, same prices. One keeps its new clients and the other doesn't, and the reports can't tell you why.
Every client in your book, scored every month.
Five scores on every client, refreshed monthly.
Second-visit odds
For every first-time client, so the follow-up goes to the ones you can still win.
A drift flag
Measured against that client's own visit rhythm, not a blanket 90 days.
A due date
When each client is likely to need the next visit.
A value forecast
Who's on track to become one of your top clients.
Offer response
Who a promotion will actually move, and who would have booked anyway.
Location comparisons
For the brand: tell a struggling shop apart from a young one or a tough market.
We've been predicting who comes back for thirty years.
A multi-location barbershop brand
We already work inside one, on retention, promotions, and location performance.
A salon operator hiring across 15+ states
We scored licensed stylists on how likely they were to join and how much they'd sell, so the hiring team called the right people instead of thousands of cold ones.
AARP
Our founder's team scored 60 to 70 million people for membership likelihood and set how often to contact each one. It saved $8 million a year and was still in use five years later.
Doug Newell (our CEO) is a former VP of Analytics at American Express and has worked with 50 to 100 Fortune 500 companies.
Start with what you have.
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Start with what you have. *
You don't need a perfect data environment or a comprehensive 5 year AI strategy.
Send what you have.
POS and booking exports are fine as they are. Cleaning and matching messy records is part of the work.
We score every client.
You get your list back with the scores attached.
It refreshes monthly.
New scores go wherever your team works: the CRM, the email tool, or the weekly sheet that your location managers already read.
The clients you're about to lose are already in your data.
Find them while you can still bring them back.