Artificial intelligence has transformed the hotel revenue management system. The principles of revenue management are still the same, but everything is run through automation nowadays.

SwissCognitive Guest Blogger: Karan Iyer, Digital Marketing Specialist, aiosell

SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningThe next-gen hotel revenue management systems (RM) holds high potential because they are equipped with high-end programming and machine learning.

The inputs in the system are processed via machine learning to predict the future business. Machine learning (ML) technology is both supervised and unsupervised. The supervised machine learning utilizes the past data to predict the future hold of the business. It is used to decide the pricing, increase ROI, and prepare a chart of inventory demand for the near future. The unsupervised one researches various types of customers and their preferences.

Profit is yielded by dynamic pricing and maximum utilization. ML is a powerful tool for revenue managers. Supervised ML delivers correct pricing for the inventory. Unsupervised ML uses various inputs to determine customer demographics and segments. It helps in getting repeat customers.

The following table explains the effect of machine learning on contemporary hotel revenue management system:

Old Hotel Revenue Management System New Hotel Revenue Management System
The old systems and software are not well equipped with the technology to correctly predict business ROI. The financial upside potential needs to be calculated by the revenue managers manually. The latest one is enriched with integrated technology for hotels. It can predict the ROI. It can suggest better ways to reach a financial upside via dynamic pricing.
Inventory should be sold at the right time at the right price. The revenue managers used to do all these calculations themselves with old systems. The inventory is managed through an automated RM system. The rates are adjusted according to demand and supply.
The old system does the price calculations. But it is not empowered with ML to change the current best rates according to the changing marketing conditions. The modern hotel revenue management system is capable of calculating the timing of dynamic pricing. It considers factors like season or off-season, competitor’s price, market trend, etc. Then it comes up with the correct cost of the inventory. And it also changes when any of the circumstances mentioned above changes.
In the older system, the price is corrected in the software only. After that, revenue managers used to change the price on other platforms manually. This leaves a chance for pricing blunder because of human error. All the booking channels, like online and agents, are integrated at one platform in the new RM system. Thus, you don’t need to remember to change the price on various booking platforms.

With the hospitality industry facing major drawbacks and hit due to the pandemic, anything that can help with revenues is beneficial. It is time to upgrade systems to make the most meaningful data-led decisions.

The new-age hotel revenue management system laced with Machine Learning and other data-analytics is the need of the hour. It is ideal for channel management, inventory, dynamic pricing, and accounts handling. This Integrated technology for hotels empowers the revenue managers to run the property with higher ROI and profit margins.

About the Author:
Karan Iyer is an end-to-end digital marketer and blogger who inherently understands the hotel industry with his hospitality background. Karan knows how to convert the pain points and challenges of the hotel industry into business opportunities, and that’s what he writes about for his readers. He also shares industry trends, insights and news to help his readers stay up-to-date.

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