This solution demonstrates how retailers can leverage Snowflake's native time series functions and ML.FORECAST capability to predict sales using competitor pricing data. Built around an athletic shoe retailer scenario, the solution shows how to combine irregular competitor pricing updates with daily sales data to create accurate forecasts.

Athletic shoe retailer sales forecasting scenario

Three core data tables: purchases, product sales by day, and competitor pricing

Sample data generation for realistic retail transaction patterns

The solution demonstrates how to incorporate irregular competitor pricing data into sales forecasts—addressing a real retail challenge where competitor prices change at unpredictable intervals.

Shows practical application of Snowflake's TIME_SLICE, ASOF JOIN, and windowed aggregation functions for retail analytics scenarios.

All analytics and ML forecasting happen within Snowflake using native functions—no data movement or external ML platforms needed.

Provides a complete framework that can be adapted for different retail categories, time periods, and external data sources.

This solution provides a practical foundation for retailers looking to implement time series forecasting within their existing Snowflake environment, with specific focus on incorporating competitive market factors into sales predictions.