If you’ve ever bought an expensive umbrella from a street vendor on a rainy day, you’ve experienced dynamic pricing. The vendor sets his price by evaluating multiple factors – his inventory levels and pace of sales, the weather, the customer profile (tourist or local), competition levels, the day of the week (holiday or not), and traffic at the location. These days, dynamic pricing is becoming increasingly prevalent, as consumers experience it when buying everything from plane tickets and sporting event tickets to taxi services and even ski tickets.
It makes sense – there are many factors that go into pricing for all types of goods and services. Take skiing for example: weather and snow conditions can change consumers’ perceived value of a day on the slopes. Likewise, it matters to sports fans which opponent their team will face in any given game, and as a result, they’re willing to pay more for some games than for others.
Dynamic pricing has always existed. Whether it is changing prices based on store location or markdowns, retailers have always used price as a key lever to increase margins. What’s changing now is the availability of vast volumes of data related to the digital “footprints” that consumers leave behind as they interact with retailers, and the ability to analyze that data. This data can help retailers anticipate consumer behavior and determine what elements have the biggest impact on price elasticity.