Hoteliers across the world will agree that knowing the number of guests they have at any given point is vital to operations. Occupancy forecasting enables managers to plan the number of staff on-shift and understand what tasks are required in order to keep the hotel running efficiently and profitably. It also helps hotels predict times throughout the year that will bring them higher or lower than normal demand, occupancy, and revenue.
However, forecasting hotel demand has always been challenging, for reasons we’ll explore below. Making an accurate forecast can feel like a moving target that is nearly impossible to hit.
The challenges of forecasting for hotels
Some of the key factors that can influence hotel demand and make forecasting a challenging task include the ever-changing landscape of seasonal trends, the impact of local events and attractions, as well as external factors such as economic conditions and global crises like the recent COVID-19 pandemic.
Unique demand patterns per hotel:
Common factors affecting demand include seasonality, events, weather, etc. Beyond that, each hotel likely has its own demand patterns, unique seasonal impacts, and local events that impact occupancy rates and other parts of their business. For example, consider a hotel near a city airport. This will have significantly different demand than a hotel in the city centre. The airport is likely to have business people, who will not stop in for dinner and are likely to eat at a restaurant in the town. City-center hotels, on the other hand, are likely to have families and couples, who may certainly stop in for dinner and thus drive demand for waiters in the hotel restaurant. Therefore, forecasting needs to be different for each location and demand driver and that can be challenging for managers trying to forecast by hand.
COVID-19:
Future trends and demands are also hard to predict for hotels at this moment as the entire industry is trying to recover from the recent COVID-19 outbreak. For example, new health and safety measures are in place limiting travel. It’s difficult to know how long that will go on for. With occupancy rates dropping to such a large extent, hotels suddenly need to adjust their staffing plans. Another element affecting the number of staff required by hotels is the health and safety measures in place. Sometimes, entire wings of the hotel must be shut down for 2 weeks in order to truly ensure there is no risk of virus transmission. This, of course, changes the number of staff required to clean and operate those rooms.
Not leveraging data to its full potential:
Many hotels actually have huge amounts of historical data available, but they are barely using it to its full potential. Hoteliers will base forecasts on a similar period of last year and average those results. This helps them to predict occupancy. However, this does not help them with more detailed forecasting. For example, if a hotel has 90% occupancy but the 10% of empty rooms are family suites, that impacts the number of guests in the hotel. This has significant, forecastable, effects on breakfast and dining, as well as things such as how many housekeepers are needed. Information like this is rarely used to the hotel’s advantage.
As you can see, with all these various factors at play, it is critical to make accurate forecasts so that hoteliers can leverage demand ‘peaks’ and handle the ‘dips’ through smart staff allocation and optimizing hotel operations, while still leaving adequate time to make adjustments to the workforce if necessary.
Solving the hotel forecasting problem: Quinyx' AI
With the introduction of new solutions like our AI and machine learning, labor demand forecasts are more accurate than ever. These intelligent algorithms learn from past data to continuously improve the quality of the results and they open up more possibilities to scale and complete automation. Here’s what makes us unique:
Hyperlocal forecasting
One hotel brand is likely to have multiple hotels in multiple locations across one city. Each specific location needs to have a different forecasting method to make accurate forecasts, simply because each location has different environments.
Remember the example of the city airport hotel vs. the city center hotel. With hyperlocal forecasting, Quinyx takes their unique differences into account and creates reliable forecasts for each and every demand driver.
Recovery forecasting
Our algorithms enable hotels to start the vital process of recovery forecasting. This includes taking into account operational changes such as concierge services, which might get paused due to contact restrictions or the cancellation of events and banquets, making certain employees available to fulfill other tasks.
It enables hotels to predict how many staff they need active day by day, as well give insights on how many new people need to be hired or even let go. It gives hoteliers the advantage of knowing when they might need to hire people back, too, when demand picks up.
Quality checks
Our labor demand forecasting algorithms are equipped with automatic quality checks and corresponding retraining to ensure that forecasts are not only accurate on the data set it is trained on now, but also produce high-quality results with future data sets that may be different because of changing conditions and another forecasting algorithm may produce better results.
Labor demand forecasting is a proven solution for those who are looking to automate their scheduling process, maximize operational value and minimize labor costs. With hyperlocal and accurate labor demand forecasting, under and overstaffing are officially things of the past.
Start using AI for forecasting
There is no better time than right now to take the leap. We can already agree that accurate forecasts are vital in hotels because not only do they support smart decision making and underpin corporate strategies, they also impact the success of your operation and overall business.
Quinyx’ dynamic monitoring of forecasts and labor demand algorithms can quickly spot inaccuracies in your forecasts. We can help you to find which demand drivers affect which aspect of your business – for example correlations between guests in house and F&B demand.
Forecasting your demand is only the beginning though – request a demo down below to learn exactly how AI-driven demand forecasting can be beneficial to you and to get a full understanding of our other solutions.
