Learnings From Leftfield: Revenue Management Lessons Retailers Can Teach Hoteliers
By Sanjay Nagalia Chief Operating Officer & Co-Founder, IDeaS - A SAS Company | September 29, 2013
While on the surface, hoteliers and retailers may seem very different, these two industries actually have a surprisingly amount in common. No it's not that both work long hours, deal with often challenging customer situations and have to do it all with good grace and a smile on their faces. But they do share the same business goal when it comes to revenue management, having to maximize revenue from limited inventory over a fixed time horizon by selling to customers with different needs.
For example, in the hotel industry, certain guests are willing to pay a premium for their room to be available at a specific time, whereas others will accept purchase restrictions in exchange for a discount. Business travelers have different needs compared to leisure travelers, including products they demand (Internet, workspace in the room) and their price sensitivity. Similarly, fashion conscious consumers are willing to pay a premium to be the first to wear a new piece of clothing or a designer's new range. Price sensitive fashion customers on the other hand, will wait for the product to be discounted and are willing not to be the first to wear it.
While there are undoubtedly some leading hoteliers who can match retailers in their ability to maximize revenue from their consumers, in general the science of revenue management is considerably more advanced in retail than within hospitality. Hoteliers can learn a lot from their retail counterparts to help them adjust
Know Your Customer
Retailers invest considerably in understanding their customers' needs and buying behavior. To do this they maintain in-depth and refined customer demographics and even psycho-graphic information. Doing so allows them to effectively price inventory and promotions that appeal to their customers' interests and inspires them to make a purchase. Take the retail giant Target for example.
An article in Forbes revealed that through using data from customer purchase information, Target was actually able to predict the likelihood of a female customer being pregnant and at what stage of pregnancy, just by analyzing her purchases. Their predictions were so accurate, Target even figured out through their analysis that an American teenager was likely to be pregnant and posted her pregnancy related coupons, before her father was even aware of the pregnancy.
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