How to Implement Artificial Intelligence When Re-shopping Hotel Reservations
Humans have certain advantages over machines. Hotel room mapping and cross platform hotel price monitoring are not one of them. The lack of standardization in the hotel industry regarding the precise names of rooms, amenities and room details have made cross-provider room mapping an almost impossible task for humans. In addition, prices fluctuate on a daily basis, which make it manually impossible to know when the best price will appear on each provider. In this article, we will discuss how to implement artificial intelligence when re-shopping hotel reservations.
Earlier I mentioned "room mapping is an almost impossible task for humans". Why only "almost impossible"? Because for some reason, a few travel companies still try to tackle this project internally using human resources. These companies end of spending a lot of money and human resources in 24/7 price monitoring for ALL reservations on ALL providers. Even then, they are limited to checking prices only on existing providers and they still incur many human errors and inconsistencies that are caused when trying to map the same rooms across different providers.
But room mapping is not the only problem when trying to increase your hotel booking profitability by manually repricing your reservations. Let´s say your company sells 1000 hotel reservations a month, and you source these reservations from 12 suppliers. Hotel prices are constantly changing, both on the same provider where you initially purchased the hotel and on other providers that offer the same hotel. Sometimes the price can drop at 8:48am on Provider A, and sometimes the price can drop at 4:22pm on Provider B. How on earth will your employee know when the prices tend to drop on each provider?
That´s where artificial intelligence comes in.
Why use artificial intelligence when re-shopping hotel reservations?
An AI-driven room mapping algorithm collects years of multiple data points from millions of hotel reservations and hundreds of providers and learns how to detect the exact same room across multiple providers, even if the name appears differently. This will not only speed up the process, but will also reduce human error and inconsistency.
One of the data points that is collected is when price drops tend to occur for each hotel on each provider. This allows the AI driven hotel repricing algorithm to pinpoint the best probability of when to scan for prices per each provider, in order to obtain the maximum profitability increase possible.
How can I use artificial intelligence when re-shopping hotel reservations?
Pruvo Revenue Maker is an artificial intelligence driven solution that automatically tracks hotel bookings you made on any provider, monitors their price 24/7 and helps increase your profitability by 36% by re-booking those reservations when their net price drops.
Pruvo Revenue Maker´s AI-driven room mapping algorithm, which is capable of not only comparing the same room over the different providers but also finds upgraded rooms (at a lower cost) for your customer.
In addition, we created a business model that removes all risk from the partner´s side:
Pruvo takes care of 95% of the integration, so you won´t have to add more tasks to your dev team.
We recently waived our setup fee, so there is no out of pocket expense to start increasing your hotel booking profitability today.
While certain tasks like selling and management are better performed by humans, other tasks like room mapping and hotel re-shopping are better performed by machines and artificial intelligence. So stop leaving money on the table, and start working with Pruvo, today!
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