The Uber Effect: What Happens When Big Data Makes Price Elasticity Transparent

Shelly Dutton

The law of demand is clear: The quantity demanded rules the direction of a price change. Even though the theory is simple, setting the right price for your product or service can be one of the toughest things marketers do. This one decision can directly affect the business’ bottom line and dictate whether the business grows or ceases to exist. Or in Uber’s case, disrupt not just an entire industry, but how people worldwide view the relationship between data and price elasticity.

While most people do not view it as fair, dynamic pricing does tap into the buyer’s psyche and highlights how and why customers are compelled to purchase a product and service during the time of a price surge. Companies such as Uber are using Big Data to determine what customers want and when optimal demand will happen. And this is not just happening on a weekly, monthly, or seasonal basis – it’s going on at every moment of every day.

During a recent episode of the NPR podcast “The Hidden Brain,” Keith Chen, a behavioral economist at the University of California, Los Angeles (UCLA), and Uber’s head of economic research, provided some insight into this phenomenon.

How Big Data and price surges are changing the consumer experience

In general, most customers are price sensitive, looking for the best deal possible for the greatest value. Unfortunately, this experience is not that straightforward for all markets. Some price changes – even the smallest of increases – can have an immediate, dramatic impact on sales revenue, while others seem to have no effect at all. Take beef, for example. When the price dramatically rises, demand wanes because people can easily substitute chicken or pork. However, for bread and cereals, the U.S. Department of Agriculture revealed that it takes a 25% price increase to induce a 1% fall in consumption.

While these examples reflect pricing that is typically stable for a time period of a week or so, what happens when pricing fluctuates hourly or at any moment when demand spikes? How do customers react then?

“Just like traditional economics would predict, as you raise the price, surge-pricing starts to dampen demand. When you go from a surge of 1x, meaning no surge, to 1.2x, you actually see a very, very large drop in demand,” said Chen. “And when we first started surge pricing at Uber, going from 1x to 1.2x would result in 27 percentage points drop in people who would request our services.”

As time passes and people get used to such fluctuations, the dynamic pricing model doesn’t affect demand as much. They may not love it, but it’s no longer such a shock anymore.  And then the price increases incrementally, leading to another drop in demand. However, there is one surprising behavior that is defying this fundamental rule – consumer respect for the perceived use of algorithms.

Chen observed, “there is a very, very strong round number effect [when it comes to pricing]. When you go from 1.9 to 2.0, you see six times larger of a drop in demand than you saw from going from 1.8 to 1.9. So the amount more that you’re paying for the trip is the same between those two steps, but 2.0 just feels viscerally larger to people. Everyone understands they’re paying twice as much for this trip as I would have.”

However, something happens once the multiplier moves from 2.0 to 2.1: People take more rides. But it’s not that a customer would rather pay 2.1 times versus just double the rate. According to Chen, “when you tell someone that their trip is going to be two times more than it normally costs, they think, ‘Wow, that’s capricious and unfair.’ Like, Uber must have seen it was raining and just decided to mess with the customer.” But if the price of the trip is set at 2.1x more than the standard rate, customers tend to believe that there must be a smart algorithm in the background at work. It doesn’t seem quite as unfair.

Although customers can always refer to their mobile device to find another business to get the same service at a lower price, real life can set in and lessen the options available. For example, people are more likely to accept a surge price if their smartphone’s battery is low. Plus, if they are stuck in a location with nowhere else to go, they are likely to enlist the service that will pick them up the fastest – regardless of price.

What does this mean for the customer experience? It all comes down to maximizing the profit of every customer interaction – no matter the situation. Businesses that create pricing models based on real-time data are most likely to sell their offering at the greatest possible value.

It’s no longer a matter of whether to adopt a digital business model, but how to adopt it while keeping your customer base happy. Learn more about 4 Ways to Digitally Disrupt Your Business Without Destroying It.