for a Meal Subscription Service
for a Meal Subscription Service
The main challenge was to accurately forecast daily demand for both inventory and operational management for the client. Existing solution, used aggregated pre-order data for each meal and ship date, predicting final demand based on historical data and pre-order curves. The model also considered key features, such as whether a meal was user-selected or automatically added to the cart, to predict specific meal orders.
However, it had significant limitations as it did not account for individual user preferences or user-level features, such as the likelihood of ordering with a promo code, which are crucial for precise forecasting. To efficiently manage inventory and operations, the client needed a highly accurate demand forecasting model for the next 1-28 days.
Customer-Level Predictions Powered by customer data, this stage focused on predicting individual customer behaviors and their likelihood to confirm orders. This granular approach aimed to provide a more detailed and accurate forecast.
Aggregate Demand Adjustments Built on the predictions from the first stage, this phase incorporated additional factors such as seasonal patterns, common trends, promotional activities, and other relevant features to refine and adjust the aggregate demand forecast.
Gathered and integrated customer data from various sources.
Developed and trained models for customer-level predictions and aggregate demand adjustments.
Utilized advanced data processing techniques to handle large volumes of data.
Combined the customer-level predictions with aggregate adjustments to produce accurate demand forecasts.
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Hello,
Thank you for your interest in Stellan’s Data Services. After you fill out this form, one of our team members will get in touch with you at the requested time to discuss your tech initiative.
We can’t wait to deliver the best results to you!
Hello,
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We can’t wait to deliver the best results to you!
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