{"id":2565,"date":"2015-11-22T17:54:44","date_gmt":"2015-11-22T22:54:44","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/doordash-delivery-made-better-by-data\/"},"modified":"2015-11-22T17:54:44","modified_gmt":"2015-11-22T22:54:44","slug":"doordash-delivery-made-better-by-data","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/doordash-delivery-made-better-by-data\/","title":{"rendered":"DoorDash; Delivery Made Better by Data"},"content":{"rendered":"

DoorDa<\/a>sh<\/a>\u00a0is both an online ordering and delivery company that\u2019s using data to make delivery better and faster for customers and more efficient for delivery drivers.\u00a0 The company aggregates large volumes of delivery data and analyzes how different factors affect delivery results.\u00a0 Using machine learning, DoorDash continually optimizes and improves its algorithm, making the model smarter and more efficient after each order and creating more value for the customers, merchants, and drivers.<\/p>\n

DoorDash creates value for all 3 players in their multi-sided market.\u00a0 At its core, the DoorDash model allows customers to place food orders through the DoorDash app or site, which sends an order to merchants to prepare the ordered items in time for a DoorDash delivery person to pick up and deliver them to the customer.\u00a0 While it sounds fairly straightforward, there are many complexities involved in making it work seamlessly.<\/p>\n

Tony Xu, the co-founder and CEO of DoorDash recently said in an interview<\/a> that \u201cTo make one delivery successful there\u2019s fifteen small things you have to get correct.\u201d The issues range from routing and timing drivers to avoid waiting at the restaurant for the order, to getting it from the merchant to the customer while it\u2019s still hot and fresh, to forecasting demand volume to make sure there are enough drivers on staff, to batching orders coming from the same or nearby restaurants and going to nearby locations. \u00a0Solving these problems involves capturing data on the time it takes to prepare different menu items, traffic and weather conditions, individual driver effectiveness, and a host of other items, and using them to make predictions and forecasts.\u00a0 They collect data on each order and constantly recalibrate the model to continuously increase accuracy and efficiency.<\/p>\n

The more accurate and efficient the model becomes, the more value DoorDash can create.\u00a0 Reducing delivery times, giving customers accurate predictions, and ensuring the food arrives hot and fresh all increase the utility for customers to keep them ordering.\u00a0 Reducing waiting time at restaurants and allowing stacked pick ups and drop offs allows delivery drivers to make more deliveries (and money) per shift to keep them coming back for more deliveries.\u00a0 DoorDash creates value for merchants simply by operating, as it allows them to access customers it otherwise couldn\u2019t reach without hiring their own delivery drivers.\u00a0 Increasing the number of orders moving through the system allows DoorDash to capture some value through a share of the delivery fee and some revenue sharing on the merchant side.<\/p>\n

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DoorDash may face some challenges as it grows into new verticals.\u00a0 DoorDash is currently primarily focused on the food delivery space because, as Prahar Shah, DoorDash\u2019s head of sales recently said<\/a>\u00a0\u201cWe\u2019ve specifically focused on food because we believe it\u2019s probably one of the hardest. When you\u2019re thinking of delivering a sweater, you\u2019re not thinking about whether the sweater is fresh when you get there.\u201d\u00a0 That being said, DoorDash has begun to move into other markets\u00a0by signing 7-Eleven as a merchant for its first foray out of the restaurant\u00a0space.\u00a0 As they\u2019ve said on numerous occasions, they are looking to deliver across several verticals, much like their closest rival, Postmates. \u00a0However, on their march toward connecting all customers with all local merchants, they may come to the same realization Postmates came to and double down on food rather than chase\u00a0a host of different markets. \u00a0Though, given the sophistication of DoorDash’s model and constant use of data, I have no doubt that they would be able to execute in whichever verticals they pursue.<\/p>\n","protected":false},"excerpt":{"rendered":"

DoorDash\u00a0is both an online ordering and delivery company that\u2019s using data to make delivery better and faster for customers and more efficient for delivery drivers.\u00a0 The company aggregates large volumes of delivery data and analyzes how different factors affect delivery […]<\/p>\n","protected":false},"author":108,"featured_media":2566,"comment_status":"open","ping_status":"closed","template":"","categories":[655,932,963,366],"class_list":["post-2565","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-data","category-data-driven","category-doordash","category-machine-learning"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-driven-value-creation-value-capture-and-operating-models\/","yoast_head":"\nDoorDash; Delivery Made Better by Data - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/doordash-delivery-made-better-by-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"DoorDash; 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