Chapter 5, Problem 1.2ASC

Uber and the Sharing Economy

First there was Zipcar and BikeShare, where customers could conveniently rent a vehicle or bike near their home or place of work; then came Airbnb, Uber, and Snapgoods, where ordinary people could use their assets (homes, cars, household goods) to bring in extra cash. Now a full-blown sharing economy exists that supplies income to providers and new functionality to users (e.g., Dogvacy, TaskRabbit, Getaround, Lyft, LendingClub, Fon, and Turo).

Take Uber, for example, with its easy-to-use apps, quick service, and aff ordable prices. Uber acts as a broker, connecting people who need rides with people who can provide them. Add cool cars (UberBLACK and UberLUX), larger vehicles (UberSUV and UberHAUL), ride sharing (UberPOOL), and new modes of transportation (UberTAXI and UberCOPTER). Look for new logistical opportunities to deliver packages (UberRUSH), groceries (UberFRESH), and restaurant meals (UberEATS). Use Uber data to recommend services or activities, send messages or predict behavior, and when technology allows, send out an autonomous vehicle coordinated to a customer’s daily schedule. That’s Uber’s plan.

Innovations such as shared services are transforming industries. It may seem simple for a company like Uber to establish an online marketplace where providers and customers match needs, but it can actually be quite complex. Companies that run this type of business are data- and technology-driven, and they are constantly looking for ways to use data to improve their services or gain a competitive edge. Operating in 330 cities and 59 countries, Uber’s one million active drivers transport millions of customers each day. Uber maintains a vast database on its drivers, wherever they are located, so that passengers can be matched quickly with available drivers. Data about passenger preferences is collected, too, for future use. And both the driver and the passenger can submit reviews of the service encounter.

Fares are calculated automatically, using GPS, street data and, of course, Uber’s own algorithms that make adjustments based on trip time (because time, not distance, drives fares) and other factors. “Surge pricing” kicks in during busy or difficult times to incentivize drivers to become active. Sometimes the results are extreme, as when one passenger paid $137 a mile on New Year’s Eve in New York City, or when a Washington, DC, passenger paid $640 for a $50 trip during a snowstorm. New York City has since negotiated a cap on surge pricing during inclement weather at 3.5 times the normal rate. In the meantime, Uber has applied for a patent on its special surge-pricing model.

Compare Uber and Airbnb. What kind of support (and tools) does each company give to its providers? What is different about the types of customers, service, company culture, and expectations for service?