River Island and Snowflake: Navigating Demand Throughout the Year

Retail goes through peaks in demand many times throughout the calendar year. Some, like the Golden Quarter (a period that includes both Black Friday and the December holidays) are predictable. But other times it can be harder to know when a spike in demand will hit.

River Island wanted to see if it could use data to predict and prepare for busy periods both online and in-store. It began experimenting with predictive models, powered by machine learning and Snowflake’s Data Cloud, to gain new insights it can use to offer a better service to its customers.

River Island’s CIO, Adam Warne, and his team analysed customer footfall during the summer heatwave. They noticed that rather than spending time outside in the hot weather, people were seeking shelter in shops. And the team used this insight to make sure stores had the right amount of stock and staff to serve them.

That’s not the only way River Island is harnessing detailed insights. You can read this article to discover more examples of how Warne is making the most of River Island’s data, including:

• Using radio frequency identification (RFID) tagging to create “smart” changing rooms to give customers the same product information they would get online
• Reducing carbon emissions throughout the entire supply chain with specialist systems that give sustainability teams full transparency
• Managing disruption and ensuring the right stores have the right products thanks to granular data on stock levels

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