Hotel Pricing Strategies
Modern technology entails dozens of changes in the way one can operate a modern hotel but in many cases also in the way we think. Today is the future’We are living it now. We process and, in parallel, we create it.
State-of-the-art technologies in Hotel e-commerce can significantly shape the way a hotel structures its room rates. At the same time, new methods of demand analysis data coming through the web can reform buyer-seller relations, something that could affect other sectors of the global e-commerce too.
Marketing up until recently was primarily based on sales data analysis. For example, if a particular product is promoted via a privileged positioning in a department store, we can roughly estimate that 1000 people will see it and may consider purchasing it.
Ultimately, only 50 of these people will eventually proceed to buy it. Traditionally, Marketing conclusions are derived by analyzing the number of sales (50) and possibly the time, gender and other demographics of the people who bought the product. The key to a successful analysis, however, lies in studying the 950 people who did not proceed to purchase the item. Why didn’t they? How many amongst them thought that buying it wasn’t necessary and how many found it expensive (or inexpensive)?
If the merchant proceeds to analyze and assess the needs of those 950 people who left the shop and their financial spending threshold, this could prove a very beneficial process for both the consumer and the merchant. For instance, if the product had been priced 5% lower this could have generated 20% more sales by yielding to an ideal win-win situation for both parties involved (consumer and merchant).
On the other hand, when it comes to a hotel website web technology can offer highly sophisticated tools to measure consumer behavior. For example, we are in a position to know how many visitors have proceeded to enter the booking engine in order to check availability and rates. It is at this stage where we can then monitor the exact search parameters that specify “Demand” such as:
(a) Customer Specific Location
(b) Language
(c) Desired dates of Stay
(d) Number of Guests and Number of Rooms Requested
(e) Length of Stay
(f) Date and Time of search
(g) Referral Site.
The three Demand Tables below, belong to the same hotel for three different periods. The information of the length of stay as well as the total demand offer us very valuable information for our pricing strategy.
Studying the above demand tables extracted by Webhotelier Booking engine, we can have different promotion and advertising to different countries as well as different offers according to the demand data.
This kind of information is currently available in state-of-the-art booking engines used by hotel sales departments, enabling them to offer the most appropriate rate tailored to every individual availability search, thus, succeeding in making a sale with maximum profit.
Since the industrial revolution, we have seen that increased demand lead to mass production which resulted in reduced product prices. However, the travel industry is full of cases where limited supply can result in extreme price variations. For example, the same airline seat may vary from 50′ to 1.500′, depending on the relevant supply- demand ratio at any given time.
The use of demand analysis combined with dynamic pricing strategies can be one of the most effective tools in yield management. This can be achieved by targeting specific market sectors with the most effective offers.
*One method used by hotels that want to avoid overbooking when there are still contracted rooms to sell via the OTAs while all their rooms have been booked is that they set their rates at outrageously high levels. Despite this, reservations can still occur. What this actually means is that the hotelier had not properly analyzed and foreseen the demand in order to have more available rooms thus yielding to a better turnover.
The Chart below, belongs to a hotel in Rome.
Demand can be volatile and can vary significantly depending on factors such as the seasonality and country of origin. Climate and other cultural factors determine the type of holiday that each target group seeks. The island of Santorini in Greece is a good example. As the graph shows below, demand from the US peaks during the month of June whereas demand from Australia peaks during July. Demand from Italy peaks during a particular fortnight in the middle of August and demand from Germany reaches its highest levels in September. As far as other nationalities are concerned, we see that demand from Asia peaks in the months of October and November.
The three Demand charts below, extracted by Webhotelier booking engine, belong to the same hotel for different requested months. (dates of stay and not dates of request) You will see the different demands behavior and needs per country for the same hotel. Country/Period Demand Behavior Analysis
The above example leads us to conclude that if a hotel in Santorini designs their sales strategy based on the demand analysis above, then they can truly accomplish selling at higher rates all year round and thus succeed in increasing their annual turnover by acquiring a better engagement with their appropriate target group.
Artificial Intelligence and Neural Networks
Undoubtedly, not all hoteliers possess adequate knowledge that would enable them to carry out the necessary analysis in order to manipulate their rates and thus gain optimal profit on an annual basis. This is precisely why it is necessary to develop tools that can assist hoteliers with ready-made options and/or suggestions, for example, a real-time price elasticity of demand measurement system which monitors the responsiveness of the demand or of the sales after a change in price.
Analysis Factors
A modern marketing process leading to correct product positioning would include the following four steps:
1. Product Definition
2. Competitive Benchmarking
3. Destination / Individual Demand Analysis & Forecasting
4. Distribution Management
The Future: Dynamically auto-adjustment pricing.
Hotels can increase their turnover by using real-time dynamic pricing models which are based on demand-driven data. A hotel booking system should be able to automatically adjust rates even several times within the same day according to current supply and demand as well as historical data. In this way, hotels will succeed in selling the right room, to the right person, at the right time, for the right price.
The static pricing model (low season, middle season & high season) is already obsolete for the modern hotel and its use has diminished. The future is already here. The tools are here. It is totally up to the modern hotelier to win their “ticket” to the future and make it work to their advantage.
