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Sep

Harnessing data – the difference between steaming spaghetti and cold noodles

It’s hard to imagine a more data-driven company than Uber Eats, the food delivery company founded by the ride-hailing company that has spectacularly disrupted the taxi industry.

Harnessing data – the difference between steaming spaghetti and cold noodles

Uber Eats is available in New Zealand’s major cities – you’ve likely seen an Uber Eats deliverer picking up takeaway orders as you dine at your favourite restaurant or spotted the distinctive black insulated bag on the back of a bike as the rider races to deliver hot meals to hungry customers.

As we found out at the Marketing Leaders’ Summit in Auckland last week, the key to the success of Uber Eats is combining data in different ways to be useful. As Andy Bowie, the Country Manager for Uber Eats told the summit, data points in isolation are not that useful. How you consider multiple feeds of data together, interpret them and make them serve a purpose is the differentiating factor, particularly in a low-margin, time sensitive, highly-competitive business like food delivery.

Deep in data

Wired magazine recently looked at just how data-driven Uber Eats and its competitors are.

“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,” write’s Wired’s Tom Simonite.

“The company is also digging more deeply into its data on restaurants and Uber Eats drivers.”

Uber Eats monitors factors like weather patterns, the predicted prep time of meals and the activity of individual drivers, to get food to customers as quickly as possible. The real key to efficiency is combining orders, so that a driver or rider can collect meals from a handful of restaurants and deliver to several locations in one trip.

The advanced analytics required to make Uber Eats work at the ambitious scale the company demands needs to be very sophisticated. But Bowie told us that technology aside, underpinning everything is the strong culture Uber and its food-delivery spin-off have built around the utilisation of data.

Learning from insights

There is an incentive to learn from data insights, because they deliver the answers to shaving seconds and minutes off delivery times and keeping delivery people more productive, so they can earn more money during an Uber Eats shift as well as keeping the food hot for their customers.

The company doesn’t rely solely on the data its network generates but overlays external data sources to provide richer insights. They undertake qualitative research – surveys and focus groups and pay particular attention to behavioural statistics and drawing on the input of users in the design of their services.

Often the bad stuff that doesn’t work helps lead to better decisions and less repeated mistakes, says Bowie.

Better decision making

Harness the data, he advises, but “sense check it” to help make better decisions.

Those companies that take a data-centric view of the world increasingly have a competitive edge. For Uber Eats, it could mean the success or failure of its delivery service on a global scale as it faces other ambitious rivals on the bustling streets of cities all over the world.

Whether you are in the food delivery business, banking or selling mobile phone plans, the same principle applies. Your business generates data. But are you harnessing that data to glean the insights that will allow you to make better business decisions?

This post is part of the Data and AI blog series. At Intergen we know how to help you make better decisions by unlocking data’s potential to deliver valuable insights about your business. We have great experience in helping organisations leverage core Microsoft platforms including Azure, Power BI, Dynamics 365 and Office 365 to make business analytics easy to use and understand. Talk to us to find out how we can help you get started.

#datareimagine

Posted by: Adele Marshall, Practice Lead | 19 September 2019

Tags: Power BI, Data Intelligence, Data Analytics, AI, Artificial Intelligence, #DataReimagine, #AIReimagine


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