Uber Pricing Model

Back in early 2012, Uber’s Boston team noticed a problem. On the weekends, customers were unable to book rides. Post midnight, both the riders and the drivers were rushing back home which gave rise to a situation of market disequilibrium. This not only resulted in dissatisfaction amongst customers, but also low reliability. The team then came up with an idea to incentivize the drivers. By offering them extra money, the company was able to increase on-the-road supply of drivers by 70-80%, and more importantly eliminate two-thirds of the unfulfilled requests. Proving that the supply curve was highly elastic and drivers were indeed motivated by price.

While this model looks like a perfect solution to help customers in need, by motivating drivers and driving supply, this is not the complete picture. After regulations were passed by the Indian government allowing surges only up to a cap and in specific midnight hours, Uber started modifying its dynamic pricing model, as a replacement.

New-age startups such as Uber make use of the enormous customer data they have access to. The company works on building a loyal customer base. The algorithm closely analyses the specific locations individual customers are at, at specific hours of the day. Once this relationship is established, incentives such as regular discounting schemes, upgrades are provided as part of the company’s “habit-forming strategy”. The key factor on which the company charges high prices is the “customer willingness” to pay them.

Uber employs various behavioural scientists, who constantly add weights to different conditions wherein consumers’ propensity to accept higher fare is high. For example, having low battery, travelling to work place induces a sense of urgency. Traveling between a fancy neighbourhood and a city center during peak commuting hours, might cost a premium rate, because the company expects people will pay for it. A ride booked from a corporate card will be cheaper than that booked by a personal card, because an untapped working individual is a potential customer for the company.

The analytics team also keeps a close check on the bounce rate to discover if the customers are toggling between different online taxi-providers. To prevent loss of customer base, personalised offers are sent through emails and pop-ups.

Keywords also have some role in determining the fare-price. Chances are that the same keywords used multiple times may increase the price.

The hike in prices is minimal, almost unnoticeable at a glance. However, when you look at the bigger picture, you’ll discover the big game companies like Uber are playing, using our data to their advantage.