Case Study
How TUBR was able to identify the levers to pull that influence bookings.
The Challenge
With the increased use of comparison sites and growing economic constraints, hotels are faced with increased pressure to increase revenue while reducing wastage from inefficiencies. Just some of the challenges the industry face include:
- Competition: During low peak periods, competition among hotels intensifies as they all aim to attract a limited number of guests. This can lead to price wars, further reducing profitability.
- Discounting Dilemmas: Hotels often resort to discounts and promotions to boost occupancy, but this can devalue the brand and erode long-term pricing strategies.
- Operational Costs: Even with low occupancy, hotels still face fixed operational costs like staffing, utilities, and maintenance. This can strain financial resources and affect profitability.
- Limited Data: Hotels may lack sufficient historical data or accurate models for forecasting demand during low periods, making predictions less reliable and increasing the risk of poor decision-making.
Addressing these challenges requires a combination of strategic decisions. However, these decisions are made infinitely more difficult without a clear understanding of demand trends and their influences. This makes it hard to plan and optimise resources efficiently.
TUBR’s approach at a glance
Accurately predicting demand is difficult due to fluctuating market conditions, economic uncertainty, and unpredictable events like weather changes or travel disruptions. TUBR’s machine learning algorithms were used to analyse the hotel’s historical bookings, overlaid with external datasets, to create forecasts that management could use to increase bookings during off-peak periods.
