What is Dynamic Pricing?
In this series we will discuss many of the revenue strategies and products that are available to hotels today. Let’s start with a very basic understanding dynamic pricing.
Dynamic pricing is the continual adjustment of prices offered to guests depending upon the value these guests attribute to your products or services. Phrases such as flexible or open pricing are often used to denote dynamic pricing. We will use the term dynamic pricing in a broad sense and discuss why it is important for hoteliers in executing their revenue management strategy.
Why Should Hotels Price Dynamically?
Specifically in the hospitality industry, dynamic pricing refers to continual adjustment of prices based on the value of each type of demand for the remaining capacity available to sell. Determining the right prices to charge a guest for a product or a service is not as straight forward as it might appear on the surface. This task requires that a hotel company know not only its own operating costs and supply, i.e., the availability of rooms and function space etc., but also how much the guest values the product offering and what the future demand is. A hotel therefore needs a wealth of information about its guests and also be able to adjust its prices at minimal cost. Advances in big data and distribution technologies have dramatically increased the amount of information that hotels can gather about guests, and have provided universal connectivity to guests making it easy to change the prices.
Dynamic Pricing in the Presence of Value Transparency
Moving from fixed pricing to dynamic pricing has caused a significant change in the way hotel rooms are priced and sold. Guests are now in a better bargaining position provided by the ever increasing value transparency. At the same time, the technology allows hotels to collect detailed data about guests’ buying behaviors and preferences. As buyers and sellers interact in the electronic world, the resulting dynamic prices more closely reflect the true market value of the products and services being traded. Conversely, dynamic pricing actually leads to increased uncertainty and demand volatility as it increases the number of reachable guests and competitors, as well as the amount of information needed to optimize the price. Therefore, some traditional forecasting and price optimization techniques fall significantly short of what hotels need for obtaining optimal prices.
Traditional models of decision-making (e.g., linear programming) that assume perfect information are unable to perform true optimization because such precise knowledge is rarely available in practice, and a strategy based on erroneous inputs – such as relying too heavily on the bookings coming from a hotel’s own website or regrets and denials – might be infeasible or exhibit poor performance when implemented. We need to deploy a powerful analytical framework that accounts for multiple dimensions of seasonality, day of week, length of stay, room type, special event period, and competitive intelligence of the demand as well as capturing and explicitly accounting for the uncertainty of the marketplace.
Online Reputation is the Newest Member of the Big Data Inputs for Optimization
While guests increasingly benefit from the newly abundant value transparency provided by ratings and reviews, hotels worldwide also take advantage of their online reputation data to identify opportunities to drive their rates and leverage their reputation performance beyond operations and directly for revenue management purposes. Best in class optimization for hotels today must account for a hotel’s online reputation in combination with that of its competitors in order to derive an optimal, reputation-impacted best available rate.
The Role of Dynamic Pricing for Managing Channel Costs
The potential to reduce distribution costs using OTAs is an important factor that cannot be ignored. It is necessary to maximize contributions to gross profit (revenue less distribution costs) rather than just the revenue obtained from the product sale. In addition to considering rates and timing of sales, dynamic price optimization systems need to help managers optimally manage revenue, by distribution channel, by revising and refining the revenue-management forecasting and optimization models to consider the relevant channel information and characteristics.
Conclusion
Dynamic price optimization needs to address all revenue generating outlets with the end goal of Total Revenue Performance Optimization. In Part 2 of our series, we’ll discuss the integration of revenue management and dynamic price optimization solutions to be applied to all revenue generating outlets.