E-Commerce Customer Intelligence: Unifying Multi-Channel Data

The Challenge

An e-commerce retailer selling through their own website, two marketplace platforms, a mobile app, and physical stores had customer data scattered across five separate systems. Marketing campaigns were based on incomplete pictures, inventory allocation was suboptimal, and customer service agents couldn't see the full purchase history during support interactions.

Our Approach

We designed a Customer Data Platform (CDP) that unified data from all channels into a single customer view. The architecture used Kafka for real-time event ingestion, PostgreSQL for the customer master, and Elasticsearch for fast querying across billions of interaction records.

Identity Resolution: Customers often had different identifiers across channels. We implemented probabilistic matching using email, phone, address, and behavioral signals to merge fragmented profiles into unified identities with 94% accuracy.

Real-time Segmentation: Marketing teams could define dynamic segments based on any combination of attributes and behaviors. Segments updated in real-time as new events arrived, enabling triggered campaigns within minutes of qualifying actions.

Recommendation Engine: Collaborative filtering combined with content-based signals generated personalized product recommendations served via API to all channels. The model retrained daily on the unified interaction history.

Results

• Customer lifetime value increased by 23% within 9 months
• Email campaign revenue improved by 45% through better segmentation
• Inventory allocation optimized, reducing overstock by 18%
• Customer service resolution time decreased by 30% with unified view

The platform processes 2M+ events per day and serves recommendations with p99 latency under 50ms.