Young companies such as RelayRides, which lets owners rent out their vehicles for hours at a time, and Airbnb, an online rental-lodging service, rely on a delicate balance of supply and demand to be successful not an easy challenge for firms with far-flung national aspirations.
As a result, a new crop of specialists and businesses is helping firms manage what some are calling the sharing economy.
One such company is Domo, an Internet-based cloud business intelligence service. It was hired by RelayRides to analyze how and where owners and renters request service.
RelayRides operates in cities across the United States, including Boston, Atlanta, Los Angeles and Washington. Owners upload their cars profile and a fee, and renters request the car, subject to the owners approval.
Were trying to build an online marketplace, said Andrew Mok, RelayRides director of business intelligence. Theres always a bit of the chicken-and-egg problem do you add cars first or renters?
By using Domos analytics to track where renters search for cars and where cars are available, RelayRides tailors its marketing to the side of the equation that needs help, he said.
If were seeing theres a lot of search volume in Phoenix, in combination with the fact that the approval rate is low, it means that area is supply-constrained, he said.
Before its contract with the American Fork, Utah-based Domo, RelayRides had a full-time employee manually gathering data, giving the company less time to react to such imbalances, Mok said.
RelayRides also learned that processes matter. For instance, Mok noticed owners abandoning the online-registration process when asked to upload a photo of their car because they did not have one on hand. The drop-off came after they had already typed in other important details, such as license plate number, make and features of the car. So RelayRides implemented frequent reminders, prompting car owners to finish their profiles. That change doubled the sites conversion rate of registrants to actual users, Mok said.
San Francisco-based Airbnb, letting hosts worldwide rent out their homes, rooms, and other forms of lodging for a few nights at a time, collects similar data internally. Several years ago, for instance, Airbnbs analytics head Riley Newman noticed a spike in searches for Lyon, France, in December, corresponding with a French festival of lights.
None of us had heard of the Fete des Lumieres but the surge of demand signaled a growth opportunity, Newman wrote in an email. So the next year we knew to prepare for this and were able to forecast the volume of supply needed to meet demand relative to general growth of the business. This is one example of many; because were a travel business, seasonality is a very strong pattern that enables us to stay ahead of the growth curve.
During the past year, Airbnbs data team has tripled in size, reaching about 15 people. The team is now building a price recommendation algorithm, notifying hosts when their listing is priced significantly above or below similar properties in the market.
Lyft, a San Francisco-based mobile app that lets users request rides from drivers willing to chauffeur them for a fee, also collects data internally, but not yet in real-time, founder John Zimmer said.
Lyft operates in Washington, Chicago, Seattle and Boston, among other cities. Drivers are asked to place pink, fluffy mustaches on the car to identify themselves to potential riders.
On the supply side, we see where drivers are available, and can look at their locations. We can match that against demand, which can be seen from app-opens to ride requests and ride completion. Were constantly looking at that, predicting the future of both those 1/8 supply and demand 3/8 curves, Zimmer said. Because were constrained by our teams time, the time we spend as a team will be focused on one side or the other.