As life returns to normal across the world, a new research in the INFORMS journal Manufacturing & Service Operations Management predicts demand for multiple types of hotel rooms based on guest characteristics, travel attributes and room features. This methodology delivers insights on segmentation, classifying each guest into segments (or a mixture of segments) based on their characteristics.
Researchers say that being able to understand the true consumer demand is critical to be able to offer right products to the right customer. However, instances where customers may choose not to purchase due to high prices or lack of interest in the available offerings, can lead to a distorted view of future demand. Moreover, each customer is unique and a one-size-fits-all policy may not be effective when facing a customer population with varying preferences. The latest study suggest a method that overcomes both challenges simultaneously.
The findings of the study can help providers formulate more efficient marketing policies and offer personalized recommendations that are more likely to be accepted.
This model will become part of Oracle Hospitality’s Applied Artificial Intelligence platform PRIME and will be used to select the optimal personalized offers for rooms and products. It is also intended to be used within the predictive analytics part of the price differentiation optimization tool to find the optimal surcharge for premium rooms based on the characteristics of the booking guest.
“Oracle Hospitality focuses on leveraging such models to drive specific positive business outcomes, greater revenue, increased guest engagement and reduced operational friction. Our goal is always to help our hospitality customers improve revenue performance,” says Jason Bryant, vice president of Oracle Hospitality Nor1.