Sales Forecast Solution For Retail

The Client

Client is a leading retail establishment, renowned for its expansive footprint and comprehensive product selection. The company is distinguished by its commitment to accessibility, exceptional value, and customer-centric service.

Key Outcomes

The solution employed advanced data preprocessing, classification, and feature engineering techniques to enhance the precision of demand prediction for diverse retail products.
We leveraged Azure-based tools like Azure Databricks and MLflow, for a scalable and efficient model deployment process

Business Challenges

The client struggled with managing noisy and inconsistent retail data for reliable sales forecasting. The client faced a major challenge in getting accurate historical sales data, assessing promotion impacts and capturing seasonality trends. Additionally, they faced difficulty in considering external factors like holidays, economics, weather, and local events. Therefore, the client required a robust solution that integrated seamlessly with existing systems while maintaining scalability and real-time performance.

Business Impacts / Key Results Achieved

The client struggled with managing noisy and inconsistent retail data for reliable sales forecasting. The client faced a major challenge in getting accurate historical sales data, assessing promotion impacts and capturing seasonality trends. Additionally, they faced difficulty in considering external factors like holidays, economics, weather, and local events. Therefore, the client required a robust solution that integrated seamlessly with existing systems while maintaining scalability and real-time performance.

Strategy and Solutions

The client struggled with managing noisy and inconsistent retail data for reliable sales forecasting. The client faced a major challenge in getting accurate historical sales data, assessing promotion impacts and capturing seasonality trends. Additionally, they faced difficulty in considering external factors like holidays, economics, weather, and local events. Therefore, the client required a robust solution that integrated seamlessly with existing systems while maintaining scalability and real-time performance.

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