Case Study
How TUBR was able to implement dynamic pricing on to boost online restaurant bookings.
The Challenge
Customers often compare prices across multiple sites, making them highly price-sensitive. Setting prices too high can drive customers away, while setting them too low can erode margins. Therefore, efficient pricing strategies can be one of the most difficult elements to develop and implement due to:
- Fluctuating Demand: Travel demand can change rapidly due to factors like seasonality, economic conditions, or unexpected events (e.g., pandemics, natural disasters). Customers’ booking behaviours, such as last-minute bookings or booking well in advance, can vary widely. Pricing strategies must adapt quickly to these changes and windows to remain competitive and profitable.
- Competitor Actions: Competitors’ pricing strategies can change frequently, requiring booking sites to constantly monitor and adjust their prices to remain competitive.
- Data Overload: Booking sites have access to vast amounts of data, including historical pricing, customer behaviour, and competitor prices. Analysing this data to set optimal prices can be complex and resource-intensive.
- Algorithm Complexity: Developing and maintaining complex pricing algorithms that can dynamically adjust prices based on multiple variables is a significant technological challenge.
TUBR’s approach at a glance
TUBR applied its patent-pending demand modelling to the booking platform’s data in order to add dynamic discounts in real-time, incentivising more customers to book with them.
