Food & Delivery Goes Anti-Surge
Manhattan’s Gebni, a new app has two objectives: reduce waste and reduce the price of meals.
The price of delivery is based on real-time demand by using an algorithm to discount prices during off-peak hours in order to increase sales.
They told Food Tank that lower prices should allow lower income customers to order delivery which also boosts the customer base for restaurants. According to the company, an increase in sales should lead to a reduction in food waste because unsold food is no longer being thrown away. In theory. I don’t know a lot of restaurants that premake a lot of food that could be wasted. Most restaurant waste is from the customers’ uneaten foods.
Mohamed Merzouk, one of the founders, told Food Tank that while he was working for the Pace’s Office of Student Development and Campus Activities as a graduate student he noticed that the bulk of event budgets went to food catering and that most of those events—although well attended most of the time—had lots of leftover food that had to be thrown out.
He then started thinking about ways to save more money on food orders for himself living on a students budget and since his orders were mostly during off times, he thought about having happy hour-like pricing for meals ordered online during known down times.
Another interesting feature is that the app has a Future Ordering feature that allows users to pre-order meals up to two weeks in advance and lock-in a guaranteed discount for doing so.
Currently, according to a recent Morgan Stanley study, the penetration of online food ordering is only $3 billion, or two percent of the entire market opportunity of $210 billion.
Gebni’s algorithm does not update prices of menu items as a whole but rather dynamically prices major ingredients that making up the item itself in order to help restaurants maximize margins on highly demanded ingredients to make up for the lower margins on discounted ingredients. The goal is to make less demanded ingredients more popular through discount incentives.