Revenue Management technology is getting more sophisticated with every year.
NB: This is an article from Beonprice
After finding its way from the airline industry into the world of hotels, hoteliers have continuously improved their pricing strategies, from weekend or seasonal rates via BAR levels to completely open rates.
Each of these steps went hand in hand with an improvement of RevPAR and modern algorithms are able to accurately predict the demand for up to 360 days ahead to make sure that at any point in time a booking will capture the highest possible revenues.
Additional characteristics like a detailed segmentation or the ability to put restrictions in place and to evaluate group requests seem to top off the value proposition of a Revenue Management System.
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RMS – ¿Quo Vadis?
The optimization of RevPAR does not have to be the end of this journey though.
While a well designed RMS can already make the business unit of revenue management thrive to success, the question really is whether an RMS can help on the enterprise level to achieve larger goals.
Especially for growing hotel organizations it is key to build a strong base of loyal guests, since this drives down customer acquisition costs significantly and increases the customer lifetime value. Only those organizations who are able to successfully create such a situation will be able to grow within an ever-competitive environment and gain market share.
How to Increase Guest Loyalty?
There are many studies from the hospitality industry examining what factors have a direct impact on the loyalty of guests. A UK-based study by Ramanathan (2011) analysed 664 hotels and identified five key factors: brand image, cleanliness, room quality, service quality and price fairness. Interestingly, it was found that the factor price fairness cannot be compensated by any other factor, meaning that if a guest does not perceive the paid price as fair, chances this person will stay at the hotel again are close to zero, no matter how well the hotel performed on the other factors.
These findings are supported by other studies across the globe. Al-Maslam has studied guest experiences among 584 guests in Damascus, Syria, concluding that price fairness has an impact on both customer satisfaction and brand loyalty. The exact same correlations were found by Gumussoy and Koseoglu (2016) in a study in Turkey and by Consuegra et al. (2007).
This leaves us with the question whether a hotel technology can help achieving price fairness or not.
Factors Influencing the Perception of a Fair Price
In the process of deciding for a hotel, guests have many sources of information available. While price is a decision criterion in itself, it is also giving the guest a clue about the quality he or she can expect from the hotel.
Price is rarely an isolated factor. Looking for a room, a guest would typically come across many offers at different prices, thereby getting an idea of the hotel quality compared to other options.
Information about the objective quality of a hotel, like room size, proximity to different key points in the city or the availability of room service is easily available on different platforms and OTAs.
In an era of high transparency thanks to a connected online world, reviews with scores and specifications add information about the subjective quality and round off the necessary information to make a decision.
All these factors thus create a certain expectation of quality of the stay at the hotel, which will later be put into relation with the price paid and therefore result in perceived price fairness or unfairness.
The challenge is to quantify this mix of factors and therefore make it useful for technology to work with.
How the Hotel Quality Index Ensures Price Fairness
The Hotel Quality Index (HQI™) is an index that is – as the name gives away – measuring the quality of a hotel (objective & subjective) and then places the Hotel Quality Score in an index with the prices of competing hotels.
The goal is to identify the willingness to pay a certain price for a certain quality. This willingness to pay differs per segment and depends on previous experiences of the segment in that category, the budget available, and other options available – that are substitute products. Based on the client mix we can then calculate the probability to sell a room at a certain price. Other factors, such as current occupancy and demand forecast, then help to determine the optimal price for a room in order to capture exactly the right amount of people who are willing to pay this price. Guests who are willing to pay a certain price based on the information available, will perceive this price as fair, which means that they are more likely to become loyal guests.
Conclusion
Revenue Management Technology has made great steps over the recent decades, but there is more an RMS can achieve.
Currently, most revenue management systems take into account the necessary data points to recommend rates that are supposed to lead to the highest possible RevPAR. Only by taking into account additional data points such as objective and subjective quality and then putting them into relation with competitors’ prices, will enable the algorithm to enlarge the value proposition of an RMS and take it to the enterprise level.
So yes, an RMS can do more. If done right, it can help to increase the loyalty towards the hotel brand by matching the price with the guest’s expectations of quality and thereby drive down your customer acquisition costs.
