Industry
Retail & E-commerce
Technologies
Python, Apache Spark, AWS, Power BI, Kafka, Snowflake
About Our Client
Our client is a multinational retail chain operating over 500 stores across North America and Europe, offering a diverse portfolio of fashion, home goods, and electronics. With rapid expansion, the client wanted to strengthen its data-driven decision-making capabilities and unlock deeper insights into sales, inventory, customer behavior, and supply chain performance.
Challenge
The client’s existing BI tools were limited to static reports, slow manual data aggregation, and siloed insights across departments. Leadership struggled to get a unified, real-time view of business performance, which limited their ability to optimize pricing, promotions, inventory levels, and customer targeting. They needed an advanced analytics platform to handle massive data volumes, deliver predictive insights, and support better operational and strategic decisions.
Solution
Celestial Infosoft designed and developed an end-to-end data analytics platform that unified the client’s data sources and unlocked advanced insights across their retail operations.
Key solution features:
- Centralized Data Lake: Consolidated data from POS, ERP, CRM, supply chain, and e-commerce systems.
- Real-Time Data Processing: Streamed transactional and customer data using Kafka and Spark for near-instant analysis.
- Predictive Analytics Models: Implemented machine learning models for demand forecasting, customer segmentation, churn prediction, and pricing optimization.
- Interactive Dashboards: Delivered role-specific dashboards with Power BI to enable executives, store managers, and merchandisers to monitor KPIs and trends.
- Self-Service Analytics: Provided business users with tools to explore data, generate reports, and create ad-hoc queries without IT involvement.
Results
The advanced analytics platform delivered major business gains:
- 30% improvement in forecast accuracy for sales and inventory.
- 15% increase in promotion ROI due to better targeting.
- 25% reduction in stockouts and overstocking.
- Faster, data-driven decision-making across all business levels.
- Improved customer satisfaction through personalized offers and optimized store assortments.
Technologies and Tools
Python, Apache Spark, AWS (S3, Redshift, Lambda), Power BI, Kafka, Snowflake, TensorFlow
