Klaus Kohlmayr, Chief Evangelist & Development Officer @ IDeaS estimates that a typical hotel has to make roughly five million pricing decisions every year and that it is not humanly possible for any revenue manager to get every decision right, every day, without the support of an automated system like a Revenue Management System (RMS). Yet, less than 10% of independent hotels have an RMS in place.

The data fragmentation in hospitality exasperates the problem. Data - guest data, comp set and market BI data -  lives in multiple "data islands" that do not talk to each other: PMS, CRS, WBE, CRM, ORM, CMS, DMS, Social Media, and BI. Practically, no hotel company can boast a single view market, comp set, pricing decision or guest data that is being constantly refreshed and enriched with real-time, "live" data feeds from ALL touchpoints with the traveler and their Digital Customer Journey.

Quite often, different teams at the property or corporate use different sets of data in their day-to-day operations, creating a total "data integrity mess," which directly affects the property's guest acquisition and retention efforts. 

In addition, the pandemic and inflation have made historic pricing data completely irrelevant. I also believe comp set pricing data has also diminished in value - how sure are you that your competitors are smart in their revenue management (RM) practices and use the right RM tools and not just plagiarizing each other's rates in a suicidal downward spiral?

In the current complex environment I believe the role of forward-looking demand and BI on future demand have been elevated to unprecedented importance.

I am not advocating that hotels should hire data scientists to make sense of the data avalanche described above. There is a nationwide shortage of data scientists plus they are very expensive. The average salary for a data scientist in the U.S. is $154,000/year (Glassdoor). Expedia employs 365 data scientists at an average salary of $135,000; Facebook - over 500 of those at $152,000/year.Very few hotel companies can afford that. 

So, how do you avoid data overload and pricing analysis paralysis? In my view, the ONLY solution to this pricing data conundrum is implementing an AI-powered cloud RMS.

An RMS, supported by a seasoned human revenue manager and utilizing real-time market, travel demand and comp set analytics, website and digital marketing analytics, and online reputation/consumer sentiment data feeds to optimize performance can achieve a near-perfect real-time pricing in response to market dynamics, and significantly enhance GOPAR (Gross Profit per available room).

Now is the time to convince management and ownership that only a cloud AI-powered revenue management system (RMS) can help the property maximize revenues and successfully compete in the super complex post-crisis marketplace.