Hoteliers are constantly looking for clues that will help them to set the best rates for their rooms. Despite all of the information sources available, many hotels miss out on revenue because of late or misinformed decisions. How can they use data to sell the right room to the right customer at the right moment and at the right price, without getting overwhelmed by all that information?
The (dis)advantage of smaller, independent hotels
It is often argued that large hotels and groups that have been in business for some years with a proper PMS system in place have an advantage: access to historical data. This includes room rates, occupancy percentage, ADR, GOPPAR and so on. They use it to forecast demand and set room rates accordingly. Smaller and mid-sized hotels without access to large datasets face a challenge to compete.
Interestingly, it currently takes a traditional revenue management system 1 to 1.5 years on average before it really understands a property’s tendencies – even in large hotels. The systems only produce very effective results around year 3 or 4. This poses another problem: revenue optimisation from day 1 is simply not possible, yet highly desired in the rapidly developing world of hospitality. The result? Hotels without extensive historical data struggle to optimise their revenue.
Standalone data is worthless
Even if you do have access to years of historical data, this does not directly translate in competitive advantage. There are a couple of reasons for that. First: just data is never enough. It must be structured and accessible before it can be used. Second, data is only valuable when turned into insights through analysis. Don’t forget the first rule of analytics: ‘garbage in, garbage out’.
Then, there’s data variety. In the past, revenue managers used just one or two types of data as a basis for their demand and rate projections: historic hotel data and competitor rates. This used to deliver ‘good enough’ results, but this approach is starting to lack sophistication.
Progressive hoteliers are now shifting to more tailored ways to optimize revenue. Rather than using solely one or two data types, they include a much larger set in their analysis: future competitor rates, flight frequencies and rates, weather and so on. This is also referred to as ‘alternative data’.
A hotel’s unique booking pattern
At Pace, we take this tailored approach one step further. Scientific analysis of booking data has revealed that every hotel displays a unique booking pattern. By looking at each reservation transaction individually and in real-time, accurate forecasts can be made with less historical data. External data becomes a reference, but not the core of demand projection and the time needed before reaching proper accuracy is significantly less through this method. Good news for those smaller and mid-sized independent hotels, formerly thought to be at a disadvantage!
This scientific approach to revenue management is a step towards the utopian scenario of revenue maximisation on Grand Opening day. But more importantly, no more money left on the table. More and more hoteliers realise that we are at the beginning of a new age of revenue management that will take big leap into the world of predictive, big data driven, revenue management strategies instead of the historical demand-driven practices of today.