Enhancing Sales Forecasting with Data Science

Problem Statement

A leading company faced challenges with its sales forecasting team spending significant time on data retrieval and manual analysis. The team was often bogged down by inconsistent data across various products, leading to suboptimal forecasting accuracy.

The company needed a solution to streamline the forecasting process, allowing the team to focus on strategic decision-making rather than data fetching and processing.

• Dedicated data science team for multi-algorithm approach.
• Holt-Winters, Neural Networks, ETS, and ARIMA used for forecasting.
• Tailored forecasting models for different product categories.
• Rolling forecasts and comparative trend analysis provided.
• Seamless data interaction through Copilot integration.
• Focus on high-margin products to improve sales performance.