How HelloFresh is Using Machine Learning to Improve Customer Experience

HelloFresh, a fresh food and meal kit delivery company, is taking its use of machine learning to the next level to improve its customers' experience. The company had already built a substantial set of machine learning and prediction systems in-house, but this home-grown approach was reaching the limits of its capability. HelloFresh went looking for new options and recently chose Tecton's feature platform for real-time machine learning. The company was looking to make its use of machine learning more standardized at scale. A feature store was a key component of HelloFresh's planned approach. In machine learning systems, features are types of variables used as inputs for predictive models such as those for fraud detection or recommendation engines. Features can be things like how much a customer has purchased in the last 30 days, the current price of an item, whether the item is in stock, and many more. Without a feature store, up-to-date information has to be fetched from raw data systems and processed before it can be used, which slows everything down. Features can be abstracted from the raw data, providing a more consistent approach across different systems and easier sharing of high-quality features between teams. HelloFresh is currently switching its main production models to be fed from the Tecton feature store, which will enable the reuse of features built for business functions like Marketing, Procurement, or Supply Chain Management for a variety of new models.