Reducing accommodation forecasting error rates can save millions each year - here's how
Travel is one of the world's favorite past times. Whether it's to see family, for work or for a break, accommodation companies are constantly striving to better predict demand earlier in the booking curve. Yet the ability to know exactly what drives demand up and down is complex.
Demand forecasting, especially in the dynamic and fragmented post-pandemic recovery, is a strategic operation for businesses and is essential to stay ahead in the market. Continuous operational improvements will define leaders and losers in the competitive accommodation industry and core to that is improving demand planning. To improve forecasting, more and more hoteliers and accommodation chains rely on their data teams to reduce error rates, but data scientists need more than historical data and seasonality trends to build robust demand forecasting models.
How accurate is your accommodation forecast?
The key to profitability for accommodation companies is packaging and pricing their offerings to maximise yield. This comes down to forecasting accuracy, specifically how early in the booking curve land on the optimal offering as well as tailoring your marketing campaigns. Under-forecasting means your rooms can be snapped at a much lower rate than your competitors and you need to have maximum staff on for a less profitable period, while over-forecasting can lead to empty rooms, wasted food and beverage inventory and staff idling away behind the desk or in the halls. Prediction precision allows hoteliers to ensure optimal staffing, pricing and packaging.
A key challenge to overcome is demand anomalies. Most hoteliers rely on historical data and simple moving averages to predict demand per property, yet the world of travel changed drastically in 2020. While 2021 is looking better, you can no longer rely on your historical data to come close to what demand will look like. Understanding the external factors that impact people movement allows management to create operational efficiencies to save costs while yielding more per room.
Save millions each year by improving forecasts
PredictHQ released an accommodation demand forecasting report that reveals major demand catalysts for hoteliers, how to know they're coming and how to pinpoint how they impact your company and each property through some simple and efficient data science work. This report provides the essential details you need to get started. After reading, you’ll be able to:
Identify the highest impact drivers for your company and your properties.
Understand how to forecast demand with greater accuracy.
Uncover the four steps to take to reduce restaurant forecasting errors.
One of PredictHQ’s global hotelier customers found that these four steps can reduce forecasting error and could add an additional $90 million to the bottom line each year through labor optimization and pricing optimization. An increased flexible and dynamic approach to restaurant forecasting enables accommodation companies to recover faster and drive competitive advantage by knowing what will drive demand before a single booking is placed.