Dynamic pricing, the process of using data to more accurately segment consumers and automatically offer them differentiated prices based on various factors, is being deployed from both within and outside the travel industry. Early adopters include many players in the airline industry, Airbnb and Amazon. So, with so many waking up to dynamic pricing, what are some of these early adopters doing and what are the main lessons?
Airbnb has already built in sophisticated dynamic pricing algorithms to those hosts that select to use them. Property owners can choose to set a price manually or utilize the dynamic pricing algorithms provided by Airbnb to automatically determine the cost per night. The three key factors involved in the automated pricing of Airbnb space are seasonality, day of the week and special events. On a broader level, the company reputedly works across more than 70 categories to create the price. It lists the following as some of those criteria:
This allows Airbnb to maximize bookings for all available dates. The lesson here is to monitor the widest possible purview of variables when considering pricing and to take special consideration of events occurring in destinations.
Amazon has also built heavily on its data advantage, making it a leader in dynamic pricing in the retail industry. Amazon has access not only to one of the world's largest online marketplaces but also an entire ecosystem of sellers who it can monitor to find bestselling products and pricing information.
This allows it to find key products that bring people to its site and price them in a manner that brings in a sale but also reinforces the customers view of Amazon as the best value marketplace. Meanwhile it looks for price inelastic goods and maintains margins on these to make up for low margins or even losses on other products.
Amazon does this through regular pricing adjustments that sometimes are made on an hourly basis depending on demand.
It was also one of the first to move into the realm of personalized offers. The user is confronted with suggestions related to what they have purchased and what they are currently looking at. These suggestions are dynamically put together based on various factors such as past purchases the user has made, as well purchases that others have made with a similar interest profile.
The core lesson here is to think about how pricing can impact long-term loyalty and price aggressively to draw in first-time customers.
Although more segmented pricing has potential rewards, it also does not come without risks. Tinder, the popular social match-maker and a fast-growing player in the platform economy has applied a pricing practice that may have been correct from a data perspective but came unstuck against legislation and consumer rights.
As with all pricing, it is a case of supply and demand, with older consumers more willing to pay for the service and were thus being charged more for their Tinder Plus and Tinder Gold services. Older consumers are operating from a smaller pool of potential partners, frequently have higher earnings or wealth and are less common on the Tinder app, making finding a partner harder, thus meaning it makes more sense from a user perspective to pay to level the playing field and improve their probability of generating successful matches.
However, Tinder ran afoul of ethics and the law with their strategy, settling a class action lawsuit for USD17.3 million in January 2019 through the California lawcourts.
It will be paramount for travel companies to ensure that their pricing practices are based on segmentation using context and behavior, and on supply and demand, as opposed to factors which can be legally challenging due to being discriminatory.
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