TechHive 
Marketing Data Consolidation & Analytics Enhancement for a Fashion Retail Giant
A long-established fashion retailer, 80+ years in business, operating 250+ stores across the UK and Europe with in-house control spanning manufacturing, design, and distribution across offline and online channels.
Technologies Used
The Challenge
Customer data was scattered across disconnected systems with no single view of the customer. Shoppers with multiple accounts were being double-counted, skewing core business metrics, and the marketing team relied on costly external tools just to calculate them. Without a flexible, multi-layered data infrastructure, deduplication was effectively impossible and real-time insight stayed out of reach.
The Solution
We built a multi-layered data warehouse in Snowflake as the single, unified home for customer and order data, then replaced the client's external metric tools with custom-built logic run directly in Snowflake and Python. A full ETL pipeline across AWS, Snowflake, and BigQuery kept current and historical data flowing automatically. AWS Glue and Snowflake SQL powered new deduplication logic to count multi-account customers accurately, while SQL-based RFM models gave the marketing team behaviour-driven segments to target. Automated status logic and a direct CRM integration closed the loop, turning fresh insights into timely, personalised customer communication.
The Results
- 25% cost reduction — the in-house metrics engine replaced costly external tools.
- 100% duplicate elimination — centralised data ended double-counted customers for good.
- 33% more effective campaigns — custom metrics and RFM models sharpened marketing targeting.
- 46% faster data processing — automated ETL cut processing time significantly.
- 20% more personalised interactions — CRM integration enabled timelier, tailored outreach.
- 60% more data throughput — the new architecture scales comfortably with business growth.