Draper
AI Customer Decisioning for Omnichannel Retail
An omnichannel decisioning brain for the holding's customers: it decides who to contact, what to offer, when, and on which channel — consent first, with measured lift.
- Banners holding customer data
- 17
- Live pilot (Hoka: 3 stores + cl.hoka.com)
- 1
- Data law, native to the model
- 21.719
The problem
A holding with 17 retail banners knows the same customer in fragments. Each brand messages on its own, nobody measures the incremental effect of any given message, and Chile's data-protection law (Ley 21.719) demands consent handling that generic tools don't ship with.
How it works
Draper unifies the customer into a golden record, decides the next best action per person — who, which offer, when, which channel — and dispatches with human approval from its own operations console. Every decision lands in a ledger with holdouts, so incremental revenue is proven by measurement, not faith. Consent is native: Ley 21.719 lives in the data model, not as a patch.
What it does
- 01
Golden record
One unified customer record across the group's banners, instead of 17 partial versions.
- 02
Decisioning and dispatch
AI proposes the play; a person approves before any message reaches a real customer.
- 03
Measured lift
A decision log with holdouts and an outcome ledger: incrementality is demonstrated, not declared.
- 04
Consent-native
The consent and preference spine is designed on Ley 21.719 from day one.
Limits
A live pilot, not a full rollout: the data scope covers 17 banners, but the live operation today is Hoka — 3 stores and cl.hoka.com. Lift is reported against holdouts, not as attributed revenue.
Related projects
- ChispezaVoice AI for Retail Hiring and Sales Training
- CifraAI Cash-Flow Intelligence for Multi-Entity Retail
- IRISMultimodal AI for Retail Store Operations
Status and figures: August 2026
If you recognize this problem in your business, let’s talk. For an implementation with my team, the advisory work lives at Menlo & Oak.