![]() ![]() In addition, the new ML model uses the image uploaded by the dasher. The store status variable was constructed based on the status of the past DSRC reports: for example, if a dasher has completed a pick-up within 15 minutes, the store is probably open despite the DSRC. The first attempt to create a model for inferring the operational status of a store was to calculate the conditional probability of the marketplace being closed when the DSRC report is filled: Probability(Store is Closed | DRSC report) ![]() For these reasons, DoorDash has developed an ML model that can understand the status of the merchant based on some variables, including the time of the last delivery and the analysis of the photo uploaded by the Dasher. This is a long, unscalable, and inefficient process. When a DSRC report is received, DoorDash contacts the merchant to understand its status in order to update it on the platform. The process starts with a submission of a photo of the closed marketplace, and then they are refunded for the loss of time and can continue with another delivery. When a Dasher finds a marketplace closed, through the ‘Dasher Report Store Close’ (DSRC) on the Dasher App, the Dasher can report a closed marketplace. Each marketplace on DoorDash works independently, and the information on working hours is important to inform the customer that their order can’t be delivered and to avoid other orders being submitted to a closed marketplace. Understanding the merchant’s operational status and the ability to receive and fulfill orders is crucial for the DoorDash platform. DoorDash introduces an ML model to predict the operational status of a store in order to increase the user experience and save thousands of order cancellations. ![]()
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