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How Data Helps Deliver Your Dinner On Timeand Warm

Guidebooks highlight San Francisco’s Hayes Valley neighborhood for its lively bars and restaurants, nurtured by the removal of an earthquake-damaged freeway and swelling tech industry salaries. At Uber’s headquarters nearby, data scientists working on the company’s food delivery service, Uber Eats, view the scene through a more numerical lens.

Their logs indicates that restaurants in the area take an average of 12 minutes and 36 seconds to prepare an evening order of pad thai—that’s 3 minutes and 2 seconds faster than in the Mission District to the south. That stat may seem obscure, but it’s at the heart of Uber’s bid to build a second giant business to stand alongside its ride-hailing service.

Uber is fighting other well-funded startups and publicly listed GrubHub in the fast-growing market for food delivery apps. Winning market share and making the business profitable depend in part on predicting the future, down to the prep time of each noodle dish. Getting it wrong means cold food, unhappy drivers, or disloyal customers in a ruthlessly competitive market.

The mobile apps of Uber Eats and competitors such as DoorDash list menu items from local restaurants. When a user places an order, the delivery service passes it along to the restaurant. The service tries to dispatch a driver to arrive just as the food is ready, drawing on a pool of independent contractors, like in the ride-hailing business. Meanwhile, the customer is shown a prediction, to the nearest minute, of when their food will arrive.

“The more detail with which we can model the physical world, the more accurate we can be,” says Eric Gu, an engineering manager with Uber Eats’ data team. The company employs meteorologists to help predict the effect of rain or snow on orders and delivery times. To refine its predictions, it also tracks when drivers are sitting or standing still, driving, or walking—joining the growing ranks of employers monitoring their workers’ every move.

Improved accuracy can convert directly into dollars, for example by helping Uber combine orders so that drivers carry multiple meals without any getting cold. Drivers get a small bonus for ferrying multiple orders on one trip. “We can save on delivery costs and pass back some savings to the eater,” Gu says.

Four blocks away, Uber rival DoorDash has its own team of data mavens working on an AI-powered crystal ball for food deliveries. One of their findings is that sunset matters. People tend to order dinner when it’s dusk, meaning they eat later in summer and shift their habits when the clocks change in spring and fall. Like Uber, the company keeps a close eye on sports schedules and weather patterns, while also tracking prep times for the dishes offered at different restaurants. Company data indicates that pad thai takes 2 minutes longer to prepare Friday through Sunday than during the rest of the week, because kitchens are busier.

Rajat Shroff, vice president of product, says DoorDash data also clearly shows the connection between accurate delivery predictions and customer loyalty. “That’s driving a big chunk of our growth,” he says. The company was valued at $7 billion this month by investors who plowed in $400 million of fresh funding.

DoorDash has also been working to better understand what happens in restaurants, for example by connecting its systems with Chipotle’s in-house software so orders can be sent in more smoothly, and DoorDash can track how they’re progressing. The company has built a food-delivery simulator in which past data is replayed to test different scheduling and prediction algorithms. Both DoorDash and Uber use their data to offer drivers more money to head to areas where demand is expected to be strong.

Analytics company Second Measure says credit card data shows that DoorDash overtook Uber Eats for second place in US market share in November, behind GrubHub. As of January, the company says, GrubHub took 43 percent of food-delivery sales, compared with 31 percent for DoorDash and 26 percent for Uber Eats. DoorDash is a customer of Second Measure.

Still, DoorDash says it gets orders to customers in an average of 35 minutes. That’s slightly slower than the 31 minutes Janelle Sallenave, head of Uber Eats for the US and Canada, says her service averages for the US.

Uber’s data scientists have a potentially big advantage over their competitors: the rich live and historical traffic data from the company’s ride-hailing network. The company is also digging more deeply into its data on restaurants and Uber Eats drivers.

One project involves analyzing the language on restaurant menus. The goal is to have algorithms predict prep times for dishes it doesn’t yet have good data about by pulling data from menu items that involve similar ingredients and cooking processes.

Chris Muller, a professor at Boston University, says the data-centric view of dining taken by Uber Eats and its competitors is helping to drive a major upheaval of the restaurant business. “This is the biggest single transformation since we saw the growth of fast casual” chains like Chipotle that promise speedy meals of higher quality than fast food.

Joe Hargrave, who grew a farmers’ market stand into five Bay Area taco shops, is living through the food app transformation. He designed his Tacolicious stores for people who share his love of good food you can eat with your fingers while watching baseball. Now, more of his customers are eating their tacos at home, and delivery has become a lifeline.

Orders via apps including DoorDash and Caviar make up about 12 percent of Hargrave’s business, he says. They’ve helped revenue grow 8 percent over the past year, even while in-store business shrank by roughly a quarter. He appreciates what the apps do, but accommodating the delivery boom hasn’t been easy.

“I’ve spent my whole career trying to figure out how to put the best product in front of people,” Hargrave says. “Now I’ve been thrown this curveball where I have to put it in a box.” Tacolicious switched its register system to better handle delivery orders without compromising in-store service. There’s now often a person in each restaurant working exclusively on packaging and checking delivery orders.

Muller and Hargrave say the app-and-algorithm approach to dining can squeeze conventional restaurants and could even put some out of business. Uber’s standard cut of each order is 30 percent, a significant bite in a traditionally low-margin industry. Even restaurants like Tacolicious that accommodate delivery services must also serve people who walk in the door.

That’s one reason Uber is encouraging the development of “virtual restaurants,” which operate out of an existing restaurant’s kitchen but sell only via its app. Uber said last year that it was working with more than 800 virtual restaurants in the US; many operate during hours when a restaurant’s main business is slack or closed, allowing more efficient operation and use of the property.

Uber and DoorDash also work with so-called dark kitchens, operations that serve only via delivery apps and can be more efficient and predictable than conventional restaurants. DoorDash operates a 2,000-square-foot kitchen space in the Bay Area that it rents to such operators.

Muller likens the arrival of Uber Eats and others to how online travel sites shook up the hotel industry, forcing hoteliers to adapt their business models to a market where consumers are more engaged, driving more visits, but at lower prices.

How lucrative this new form of restaurant business will be is unclear. Uber has previously said its service is profitable in some cities, but financials released for the last quarter of 2018 didn’t offer detail about Uber Eats. In all, the company said it lost $940 million, 40 percent more than the previous quarter. In the third quarter of 2018, the company said Uber Eats accounted for 17 percent, or $2.1 billion, of its worldwide gross bookings.

GrubHub has been consistently profitable since it went public in 2014 and sold $1.4 billion worth of food in the final quarter of 2018, an increase of 21 percent over the previous year. But it also reported a small loss after a big jump in marketing spending. GrubHub’s management told investors that competition wasn’t harming growth, but analysts interpreted the company’s results as showing how the rise of DoorDash and Uber Eats will put all the delivery apps under pressure.

Uber and DoorDash both declined to provide more detail about their businesses but are rapidly expanding their reach. DoorDash says it covers 80 percent of the US population, and Uber Eats claims to have reached more than 70 percent, in addition to serving more than 100 cities in Africa, Asia, and Europe. Sallenave, the Uber Eats head for the US and Canada, predicts eating via app will become the norm everywhere, not just in urban areas. “We fundamentally believe we can make this business economically viable, not only in large cities but also in small towns and in the suburbs,” she says.


Read more: https://www.wired.com/story/how-data-helps-deliver-your-dinner-on-time-warm/

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