What dynamic pricing leaders can teach the travel industry

Dynamic pricing promises major change and is already being used by a variety of brands says a new report.

Brands are waking up to the possibility of dynamic pricing and driving rich rewards from doing so finds the new Dynamic and Personalized Pricing report from EyeforTravel, which is free to download now. 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:

•                     Lead-times

•                     Search behaviors both within and outside the listings market, with prices rising as search popularity increases

•                     Seasonality

•                     The popularity of the listing at any given time, including how many users view and their dwell time on the listing

•                     Listing amenities, such as WiFi, air conditioning or a pool

•                     Number of bedrooms and bathrooms.

•                     Prices paid for bookings in the past with adjustments made if host-set prices differentiate from the algorithm

•                     Host review scores over time.

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.

To find out more, download the Dynamic and Personalized Pricing report for free now and receive:

• Detailed analysis from a report written by a pricing expert and consultant.
• A breakdown of modern pricing practice in travel to help you get to grips with the current environment and how it is changing.
• Key rules to create your own advanced pricing regime.
• Real-world examples of advanced pricing practices from Airbnb, Amazon and more.
• How to integrate dynamic pricing into increasingly complex distribution networks.

Why dynamic pricing will transform travel distribution

Smarter pricing structures will allow travel brands to create more relevant prices for their customers, boosting revenue and loyalty finds a new report.

Pricing within the travel industry is evolving to match consumers and what they are willing to pay far more accurately as a result of a better understanding of impacting variables and more ability to deploy granular prices across distribution networks. The upshot of this, according to a new report, is that travel brands can continuously adjust their pricing to suit their customers and thus maximize the revenue they get for each route or room. To explore the step change in capabilities and how they can be implemented, you can download the Dynamic and Personalized Pricing report completely for free here now.

Currently, most revenue management systems (and revenue managers) take into account things such as historical demand curves, events and conferences, seasonal factors and overall route capacity, etc. However, with the changing ability of systems to factor in a greater depth of information in very short timeframes and from an almost unlimited number of sources, the calculation of demand is starting to change.

Firstly, brands can bring in more information surrounding the destination and target market. For example, for a short leisure trip, a system could factor in the weather at different locations and use that as a component of the algorithm to calculate anticipated demand. Then there are customer personas that are growing increasingly detailed and only really limited by the amount of available data.

This is allowing micro-segmentation and fundamentally changing the way offers are created to the point where each individual could receive a different offer when considering all the possible combinations.

With micro-segmentation, the industry is moving away from the simplistic leisure versus business traveller divide and the other common segmentations that have been used to date. The idea is to apply a finer grained mesh – perhaps with dozens or even hundreds of micro-segments as opposed to five or ten. With this systematic approach, applying factors such as willingness to pay and demand-based factors to individual offers provides considerable price-point differentiation.

The fundamental processes can also be applied to ancillaries, in what is termed complete offer creation that creates the next level of differentiation. Complete offer creation involves bundling additional products that are probabilistically calculated to be desired by the consumer. In the case of the airlines, this could mean an offer in which the travel, wireless connectivity, a checked bag and a warm meal is included in the overall offer price. If the brand can add value to the customer’s request by addressing an additional need or desire, and the offer is within the consumer’s willingness to pay, an expanded offer can not only generate additional immediate revenue but also a higher level of customer satisfaction and potentially additional follow-on revenues.

This creates a huge range of possible combinations that means, effectively, pricing is coming down to the individual. The value created from this investment into more complex and granular pricing is leaps in revenue through maximizing the pricing level in each demand period and minimizing the amount of inventory left unused.

To find out more, download the Dynamic and Personalized Pricing report for free now and receive:

• Detailed analysis from a report written by a pricing expert and consultant.
• A breakdown of modern pricing practice in travel to help you get to grips with the current environment and how it is changing.
• Key rules to create your own advanced pricing regime.
• Real-world examples of advanced pricing practices from Airbnb, Amazon and more.
• How to integrate dynamic pricing into increasingly complex distribution networks.