Dynamic pricing in the travel industry was born to calculate at any time the price that can optimize a physical resource, such as the occupancy of a seat on an aeroplane, the place of checked baggage or a hotel room, depending on the approach of the date on which the event will occur.
Therefore, this approach to the pricing is born in those ambles to you in which it was not possible to contingent the resources like if they were of the supplies and, therefore, in those markets in which not to sell a product it means of it to see cancelled the value without but of it the costs.
The concept of dynamic price is tied almost automatically to two basic concepts of the economy: that of the elasticity of demand (about this topic read our article)to the price and the theme of the optimized management of the resources.
To apply a dynamic price that holds account of the variations demand depending on the variations of the price of a product or service means, therefore, to characterize pricing in a position to being adapted to the characteristics of the consumers, able to change in the function of its evolutions and the time and that it concurs to the company to find the point of maximization of the own profits.
Instead, the management of the resources through the dynamic pricing concurs to stimulate the question when turns out less accentuated, to favour the purchases with wide advance, to reduce wastes and therefore to optimize the total results in function of the residual availability.
As is well known, dynamic pricing was theorized and used for the first time in the airline industry in the 1980s, and even today this pricing strategy is confirmed as a powerful weapon capable of maximizing occupancy and turnover in the travel world.
Dynamic pricing as a solution to three major problems in travel industry
At the base of this strategy, the correctness and reliability of the demand forecast are of fundamental importance. Only under these conditions can dynamic pricing resolve the three main risks faced by operators in the sector:
- Risk of dilution: accepting the request at a discounted price anticipating that subsequently there will be no possibility of selling at a higher price. This risks losing revenue if demand forecasts have been pessimistic. More generally, selling to a client at a lower price than the client would have been willing to pay.
- Spill risk: refusing demand, having reached available capacity too early, therefore not capturing that part of the demand that occurs close to the date and which, by its nature, expresses a very high value, as it is less sensitive to price.
- Risk of spoilage: leaving goods or services unsold, anticipating that new demand will arise closer to the date. The decision is thus made to refuse an earlier purchase at lower prices while waiting for a higher value demand. If the forecast is wrong, the risk is that occupancy will not be maximized.
Performing precise analysis of this type, however, in today’s environment often comes up against a vast complexity of variables that leads toward an increasing involvement of artificial intelligence in pricing.
Artificial intelligence to respond to complexity
AI and machine learning make it possible to automate complex formulas for dynamic pricing, allowing a large number of variables to be taken into account simultaneously. In fact, through artificial intelligence, it is possible to support revenue strategy choices based on a solid database. In this way, price becomes a fundamental lever through which to attract, stimulate and maximize potential demand.
Among the first players to have developed artificial intelligence algorithms in online sales are Airbnb and Amazon. Airbnb has created a sophisticated dynamic pricing algorithm in the tourism sector for hosts using the platform. In this case, the main variables that impact the final price suggested are the annual and weekly seasonality and the presence of events and holidays, in addition to these there are about 70 other variables for which an impact on demand has been analyzed. In this way, it was possible to maximize bookings for the dates available for each destination offered.
Amazon, on the other hand, was among the first players to use AI algorithms in the retail industry, dynamizing price according to consumer characteristics, demand trends and the number of items in stock.
What these well-known case histories highlight is the direction in which pricing strategies are heading, also for the travel sector. This is a new approach, strongly data-driven, that allows you to provide the price that optimizes the demand function, which takes into account the willingness-to-pay of consumers, price sensitivity and all those factors that impact daily purchase choices.
Having reached this extraordinary level of innovation, the alternatives to strategies to be applied are reduced. You can continue to sell at a fixed price, completely ignoring the characteristics, needs and opportunities of demand, or you can choose to be guided by the evidence that advanced data analysis offers us. Only in this way is it possible to embark on a path of growth and development in terms of maximizing commercial goals.
To learn more about the impact of dynamic pricing on the results of a travel company, read our case study with a tour operator.