All Case Studies
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Retail Chain — Customer 360 & Recommendation Engine
Omnichannel customer data unification, customer segmentation, and personalized recommendation engine. 18% increase in basket size and 32% improvement in loyalty.
Problem
- Customer behavior was split across stores, e-commerce, and campaign systems.
- Segmentation and recommendation flows were not fed quickly enough by current data.
- Marketing teams needed manageable control over personalization outputs.
Approach
- Customer, transaction, campaign, and product data were unified in a Customer 360 model.
- Segmentation, recommendation scoring, and campaign targeting flows were designed.
- Model outputs were exposed through dashboards and integration APIs for business teams.
Outcome
- Customer visibility became more consistent across channels.
- Personalization recommendations supported improved basket and loyalty metrics.
- Marketing teams gained more control over segment and recommendation outputs.
Technology Stack
Data IntegrationMLRecommendation
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