Hotel Room Display Ordering via Dynamic Customer Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The hotel industry faces inefficiencies in revenue management due to traditional one-size-fits-all pricing policies, which fail to account for the heterogeneity of guests, leading to suboptimal room category and rate plan combinations being displayed to customers.
Innovation Solution
A computer system that uses a trained prediction demand model to determine optimal Lagrangian coefficients and prices, employing iterative gradient searches and linear programming to optimize the display ordering of hotel room choices based on individual customer attributes, thereby maximizing revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional one-size-fits-all pricing policies are used, then device complexity is reduced, but revenue is suboptimal due to failure to account for customer heterogeneity
Solution Approach 1:
The patent segments customers into different clusters based on their attributes and behavior patterns. By dividing the homogeneous customer base into heterogeneous segments, the system can apply tailored pricing and display strategies to each segment, thereby increasing revenue without requiring complete customization for every individual customer.
Solution Approach 2:
The system dynamically changes pricing parameters and display ordering parameters based on customer cluster characteristics. By adjusting these parameters according to segment-specific demand models, the system optimizes revenue while managing complexity through parameterization rather than structural reconfiguration.
2Productivity
If personalized pricing and recommendations are implemented, then revenue is maximized, but device complexity increases due to need for customer profiling and dynamic optimization
Solution Approach 1:
The system performs preliminary customer profiling and clustering before the actual booking process. By pre-segmenting customers and pre-calculating optimal pricing and display parameters for each segment, the system reduces real-time computational complexity while maintaining personalized revenue optimization capabilities.
Solution Approach 2:
The patent introduces an intermediary layer (the clustering and demand modeling system) between the customer and the pricing/display system. This intermediary processes customer attributes and generates segment-specific recommendations, thereby managing complexity by isolating the computational burden from the core transaction system.
3Productivity
If traditional display ordering is used, then ease of operation is maintained, but revenue is lost due to suboptimal room category and rate plan combinations being displayed
Solution Approach 1:
The system applies local quality by customizing the display ordering and pricing for each customer segment rather than using a uniform approach. Each segment receives a tailored display of room categories and rate plans that matches their specific preferences and price sensitivity, thereby increasing revenue without requiring full customization for every interaction.
Data Source
AI summary
Embodiments optimize display ordering of reservable hotel room choices for a hotel. Embodiments receive a trained prediction demand model for the hotel, the trained prediction model including estimated coefficients, and receive a total inventory of hotel rooms for the hotel. Embodiments determine optimal Lagrangian coefficients from the estimated coefficients using a first iterative gradient search and determine optimized prices per customer based on the estimated coefficients and the optimal Lagrangian coefficients using a second iterative gradient search. Embodiments determine an offer order optimization per customer based on the optimal Lagrangian coefficients and using linear programming. Embodiments receive a request for a hotel room from a first customer, the request including one or more attributes. Based on the one or more attributes and the optimized prices per customer and the offer order optimization per customer, embodiments display an optimized ordered list of hotel room choices.