One of the leading global hotel chains with a host of properties, operating under separate brands
The client set a business objective to strengthen its revenue management function and drive revenue growth across all its properties. To achieve this, the client had to first identify factors that really impacted revenue growth.
For this, the client had to undertake an extensive evaluation of a large number of metrics for all its properties, spread across diverse locations, and varying in their level of service and amenities. Given the diversity in metrics, it was clear that the client had its task cut out.
The WNS Solution
Revenue Per Available Room (RevPar) has long been a foundational metric of hotel revenue management. However, WNS' team of analysts and travel domain experts identified Revenue Growth Index (RGI) - a hotel's share of room revenue and how the hotel measures up against its competition - as a more accurate metric to assess revenue growth.
WNS mined the client's vast storehouses of data and identified more than 5,800 variables that contribute to RGI. The team then deployed a three-stage clustering model to identify the specific drivers of RGI:
Variable Clustering: After consolidating 5,800 different variables, WNS zeroed in on 100 key variables, which influence RGI
Hotel Clustering: Applied those 100 key variables to the hotel chain's operational and demographic attributes to categorize hotels into groups with similar properties. Each group consisted of approximately 700 hotels
Driver Clustering: Identified key and distinct RGI drivers for each hotel cluster to understand the distinct profile of each hotel
Identification of RGI drivers led to:
Determination and elimination of operational bottlenecks within specific properties to drive revenue
Formulation of sharper marketing and promotional strategies that better-target the client's customers