7Shifts Inc raises $10mn from US investors
Saskatoon technology startup 7Shifts Inc announced this week that it has raised US$10mn in venture capital from a consortium of four US-based investors. The funding represents more than double the mount previously raised by the company, the Saskatoon Star Phoenix reports.
7Shifts is the developer and operator of a business organization solutions application that saves business owners time and money when scheduling employee shifts. The app has been used to schedule nearly 100 million shifts for the 16 million restaurant workers in the US, and collectively saved restaurateurs over $200 million in labor costs. The rapidly growing platform is used by more than 250,000 restaurant professionals and 10,000 restaurants, including large, growing franchises such as Bareburger, Panera, Honeygrow, &pizza, and Smoke's Poutinerie.
The funding was provided by Napier Park Financial Partners, with participation from Teamworthy Ventures, existing investor Relay Ventures and former CEO of Snag (Snagajob), Peter Harrison.
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“As someone who grew up in a family of restaurant operators, I know first-hand the pain points restaurant managers face in managing staff,” said Jordan Boesch, CEO of 7shifts. “From the beginning, 7shifts has been intently focused on delivering innovative products to make life easier for restaurants, managers and employees alike.”
He continued: “It’s one thing to predict labor needs, but it’s another to fill the shift with an available and qualified worker– both need to be met for restaurant operators to be successful. Our predictive scheduling algorithm leverages machine learning to enable managers and operators to automatically create data-driven and labor-optimized schedules that exceed the accuracy and trustworthiness of manually generated schedules, while also adhering to state-wide labor regulations. By leveraging POS integrations for real-time data, 7shifts can accurately project future sales with up to 95% accuracy given historical sales, seasonality, weather trends and other external factors. From there, we use machine learning to accurately predict future labor needs and create optimized schedules.”
