Uber Eats is trialling a pool system which will give consumers discounts by batching together multiple orders from a single restaurant for delivery to different consumers in close proximity to one another.
The initiative, which is being piloted in India, allows a single delivery driver to pick up all the orders at once, and then distribute them to neighbours or co-workers nearby.
Uber Eats incentivises consumers to pick the same restaurant in rapid succession by offering a discount.
Tech Crunch reports the initiative is being explored as a way of strengthening Uber’s position as an advertising platform, a la Google and Facebook.
Uber Eats head of product Stephen Chau told Tech Crunch: “There’s a bunch of different ways we can work with restaurants over time. If we have all the restaurants on the marketplace and we give them tools to help them grow, then this will be a very efficient marketplace. They’re going to be spending those ad dollars somewhere.”
He added: “Generally people are coming in with an intent to eat but there are many, many options available to them. We’re giving you a discount on the food delivery by using machine learning to understand these are some restaurants it might make sense to order from. When multiple people order from the same restaurant, delivery drivers can pick up multiple people’s food.”
Consumers are offered a promotional discount on a carousel on the Uber Eats home screen, which has a countdown timer to when the offer will expire and refresh.
As well encouraging consumers to choose more quickly, the timer ensures orders come in close enough together so the batched orders are not waiting around before delivery.
The development has been interpreted as part of a strategy to monetize eating out choices by becoming a paid-for marketing platform for restaurants.
By amassing data on consumer preferences, platforms like Uber Eats are able to be increasingly direct and sophisticated when targeting ads.
As these aggregators build up more customers, they are able to pit suppliers and operators against each other to further drop their consumer price-point, or pay more for ads.
By building trust among consumers about its restaurant recommendations, it is thought operators will eventually pay directly to be ranked higher in platforms such as Uber Eats.