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Dunnhumby, the global leader in customer data science, has launched its new web-based application on Microsoft Azure, enabling data scientists to deliver customer insights faster, driving profitability and customer loyalty.

Dunnhumby Model Lab automates many of the repetitive, time-consuming tasks for data scientists, allowing them to focus instead on the modeling that delivers the greatest value. The application uses machine-learning technology, hosted in Azure to achieve high performance, reduce run time, and allow data scientists to quickly explore many algorithms.

Model Lab is designed to solve complex retail challenges, such as understanding customer churn and predicting propensity to purchase and in what channel, in-store versus online. The tool helps retailers and brands build loyalty and profitability by focusing on the shopper experience.

Azure enables data scientists to take advantage of Model Lab through a simple subscription and to get them up and running virtually instantly. The Azure-based service gives users the benefit of always working with the latest software, with no need to worry about updates. New features and experiments are provided automatically, so users can experience the very latest in advanced machine-learning technology.“dunnhumby Model Lab already empowers dunnhumby’s data scientists, and it has been integral in enabling them to create millions of models for retailers and brands around the world rapidly and efficiently,” said Kyle Fugere, Head of Innovation and Ventures at dunnhumby. “We are democratizing customer data science for everyone and making Model Lab available on Microsoft Azure means that it is now accessible for retailers, brands, and businesses, large and small.”

Azure Business Lead at Microsoft UK said, “Putting the power of machine learning into the hands of more people is key to empowering every person and organization to achieve more. dunnhumby, with its experience in data science, combined with Microsoft Azure, will deliver value to companies looking to use advanced machine learning within their business.”