Retail & Consumer Intelligence
Personalization at millions of interactions a day
A retail organization
Millions
customer interactions processed daily
01 / Challenge
The problem.
Customer data was fragmented, and marketing ran on broad segments and manual campaigns. The brand was leaving significant revenue on the table.
02 / Approach
How we built it.
We connected point-of-sale, e-commerce, loyalty, and marketing data into a single customer view. Then we deployed recommendation engines, dynamic pricing models, and campaign orchestration that react to each customer's behavior in real time.
03 / What we built
The system.
01
Customer intelligence platform
02
Single customer view across POS, e-commerce, loyalty, and marketing
03
ML recommendation engines
04
Dynamic pricing models
05
Real-time campaign orchestration
04 / Outcomes
What changed.
Measurable lifts in average order value and repeat purchase rates
Marketing moved from batch campaigns to always-on, event-triggered engagement
Customer lifetime value improved in stores and online