UC-052
Pricing & Customer Intelligence
Runs pricing experiments and a generative service assistant over an anonymous customer-journey graph, delivered in two phases.
30-36Build Duration
19-35xIndicative ROI
The Challenge
A dominant competitor is moving to real-time dynamic pricing while the retailer has no customer-analytics or loyalty layer despite ~1B transactions a year. Customer service is still handled manually.
How It Works
- Phase A: builds an anonymous customer-journey graph and deploys a generative service assistant.
- Runs reinforcement-learning pricing experiments on the online channel first.
- Phase B: switches on in-store dynamic pricing once electronic shelf labels are deployed.
What It Removes
- Roughly 1B transactions a year that go unanalysed
- Price moves made without a live experiment
- Service queries handled one by one by staff
Input Data RequirementsOnline and in-store transaction data, loyalty and customer-service records, product and price files, electronic shelf-label systems
Output FormatCustomer-journey graph, pricing experiment results, generative service assistant, Phase-B-ready dynamic pricing
“We can test a price and see what it does before the competitor's shelf labels force our hand.”