Solutions · 01
Available

Fewer returns and fewer empty chairs

We score every order and appointment by its real risk, based on your history, and turn that into actions with a cost and a payoff: size checks, charging for return shipping, stronger reminders or careful overbooking.

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Orders to review today
example
OrderRiskLevel
  • pedido_8817271%High
  • pedido_8812364%High
  • pedido_8802348%Medium

new customer · large order · 3 sizes of the same item

Where it fits

Fashion and footwear

The “three sizes of the same item” pattern triggers a size check before shipping.

Electronics and marketplaces

A 24-hour shipping hold when a new customer, a high order value and a steep discount coincide.

Clinics and appointment-based services

Stronger reminders and overbooking where a no-show is likely, without punishing everyone else.

Clothing racks in a fashion store

How it works with your data

  1. Upload your history

    Orders and returns (or appointments). CSV or Excel: we detect the entities and columns automatically.

  2. Honest validation

    We check against your own history and a traffic light tells you how much confidence each figure carries.

  3. Turn on actions

    Every action with its cost and legal note: size check, shipping charge, exchange before refund, shipment hold.

74

times out of 100 it tells a returned item from one that wasn’t, in a blind test on 805,620 real order lines from a public dataset (Online Retail II, UCI). The “returned before” rule gets 57; chance, 50.

57/100

what the usual rule (“this customer has returned before”) gets in that same test: barely better than a coin toss.

10%

control group left untouched: the value is proven, not estimated

Want to see it on your own data?

We’ll show you, for free, what you could save with your own history. No integrations, no commitment.

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