Using ML and Analytics to Empower Food Delivery

The food delivery data analytics model helps businesses reduce delivery time by dividing the on-demand delivery cycle into granular stages.

FREMONT, CA: Delivery time prediction has long been a part of city logistics, but refining accuracy has recently become very important for food delivery services. In food delivery, single minutes can make a big difference. It is very important for customer satisfaction that the initial prediction is highly accurate and that any delays are communicated effectively. Machine learning clubbed with data analytics from past food orders, and user-level consumption patterns help food delivery services enhance customer experience. Industry leaders are blending these innovations to help optimize delivery times and gain maximum outcomes. Read on to know more.

[vendor_logo_first]The food delivery service's goal is to make delivery reliable, effortless, and affordable for end-users. Service providers should ensure that the food will be delivered seamlessly, which needs them to predict the future and balance between orders and delivery partners. The system must make three predictions, including the time of delivery, the time it takes to deliver the food, and the time it takes for the restaurant to prepare the order. Predictions are made more complex, given that the food delivery app doesn't have any insight into how long it takes for a restaurateur to prepare any given item.

It is becoming more challenging in situations like the COVID-19 pandemic, as it pushed food delivery service providers to operate on a low workforce. The use of machine learning in the food industry can help address this challenge by quantifying the time spent on past deliveries and forecasting the time spent on future deliveries.  Machine learning software provides advanced analytics solutions that allow food aggregators, cloud kitchens, and other businesses to build a sustainable ecosystem.

With ML in place, travel time can be estimated from the history of all travel times and all restaurants in the area, given all the jobs and all the available drivers. ML, combined with analytics, can also provide insight, with additional contextual clues in near real-time.

See Also: Top Machine Learning Companies