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How Starbucks Forecasts Demand at Scale with Facebook Prophet and Azure Databricks

Available On Demand

Performing fine-grained forecasts on day-store-SKU is beyond the ability of legacy, data warehousing based forecasting tools. Demand for products varies by product, store and day, and yet traditional demand forecasting solutions perform their forecasts at the aggregate market, week and promo group levels.

With the introduction of the Databricks Unified Data Analytics Platform, retailers are able to see double-digit improvements in their forecast accuracy. They can perform fine-grained forecasts at the SKU, store and day as well as include hundreds of additional features to improve the accuracy of models. They can further enhance their forecasts with localization and the easy inclusion of additional data sets. And they’re running these forecasts daily, providing their planners and retail operations team with timely data for better execution.

In this webinar:

  • Learn how to perform fine-grained demand forecasts on a day/store/SKU level with Databricks
  • We’ll show how to forecast time series data precisely using Facebook’s Prophet
  • Also, learn how Starbucks does custom forecasting with relative ease
  • How to train a large number of models using the defacto distributed data processing engine, Apache Spark™
  • Finally, we can then present this data to analysts and managers using BI tools to enable the decision making required to drive the required business outcomes


Rob Saker, Industry Leader – Retail at Databricks

Brendan O’Shaughnessy, Data Science Manager at Starbucks

Bilal Obeidat, Solutions Architect, Databricks