Retail

Customer Analytics & Personalization

Major Retail Chain

Timeline: 10 months
Team: 7-10 specialists

KEY IMPACT

Achieved 35% increase in customer lifetime value and 45% improvement in inventory turnover.

The Challenge

A major retail chain with 200+ stores needed to understand customer behaviour across online and offline channels. They had fragmented customer data across e-commerce, point-of-sale systems and loyalty programs. Marketing campaigns were generic, inventory decisions were based on historical averages and they were losing market share to more data-driven competitors.

Our Solution

Built an integrated customer data platform on Databricks combining online and offline touchpoints. Created a unified customer 360-degree view, implemented real-time personalization engine for product recommendations, developed predictive models for inventory optimization, and built automated marketing campaign tools with A/B testing capabilities.

Results & Outcomes

35% increase in customer lifetime value

45% improvement in inventory turnover

Personalized experiences for 5M+ customers

25% increase in marketing campaign effectiveness

20% reduction in overstock and stockouts

Technologies Used

Databricks
MLflow
Delta Lake
Kafka
Tableau

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