The retail giant manages a deluge of customer data from stores, POS devices, online transactions, items bought, and more. This amount of data was kept in various databases for at least five years and was organised by brands, transaction type (online or in-person), account management-related information, and operational data for specific products. Although data is operating in a “logical” silo, the retailer was unable to see an integrated customer view because it was difficult to map the appropriate data points from each database to feed into a customer intelligence engine.

Our client is a multinational retail business that generates revenue exceeding $4 billion. The company operates a diverse range of brands through various channels and adaptable business models. Customers can purchase personalized apparel from a wide selection of brands, rent outfits, or even order custom-made clothing tailored to their individual specifications.

To gain a Customer 360 perspective, the retail company needs to analyze customer data from various sources. This includes store data, POS units, online transactions, and product purchases. However, this data is stored in different databases categorized by brands, transaction modes, account management, and product operations. Despite operating in separate silos, the retailer faces challenges in visualizing a comprehensive customer view and mapping the relevant data points for their Customer Intelligence engine. They aim to analyze customer journeys, sales values across categories, and transaction dynamics, predict behaviors, forecast product demands, and design effective marketing campaigns.

1. Big Data ETL tools lack a user-friendly GUI interface.
2. Manual assessment of data quality after transformations are challenging.
3. Real-time data transformations further complicate the process.
4. There is a need for automated validations to ensure data integrity.