Personalized Demand Model for Hotel Room Pricing
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Solution Overview
Problem
Traditional revenue management solutions in the hotel industry are ineffective in segmenting guests based on individual attributes and room category features, leading to suboptimal pricing due to assumptions of homogeneity and neglect of price elasticity and external factors.
Innovation Solution
A personalized demand model using machine learning-based soft clustering and multinomial choice modeling to segment guests into distinct clusters based on attributes, incorporating unobservable variables like no-purchase cases and external factors, and applying a personalized pricing algorithm to determine optimal room prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional one-size-fits-all revenue management policies are used, then implementation simplicity is maintained, but pricing effectiveness and profit maximization deteriorate
Solution Approach 1:
The patent segments guests into distinct clusters based on multiple attributes including guest demographics, travel behavior, and external factors. This segmentation enables differentiated pricing strategies for different guest segments, moving away from uniform pricing while maintaining manageable complexity through automated clustering algorithms.
Solution Approach 2:
The system dynamically adjusts pricing parameters based on cluster-specific demand models that incorporate multiple variables such as guest attributes, travel attributes, external factors, and room category features. This allows flexible parameter optimization for each segment without requiring manual intervention.
2Productivity
If guest profiling and personalized pricing are implemented, then profit maximization improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent employs a unified demand model framework that handles multiple guest segments, room categories, and external factors through a single computational system. This multi-functional approach consolidates what could be numerous separate models into one cohesive system, reducing overall complexity while maintaining personalized pricing capabilities.
Solution Approach 2:
The system uses historical data to create representative demand models for each guest cluster, effectively copying patterns from past behavior to predict future demand. This allows the system to handle complex personalized pricing by leveraging replicated historical patterns rather than requiring entirely new analysis for each pricing decision.
3Measurement precision
If multiple attributes and external factors are incorporated into the demand model, then prediction accuracy improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts and isolates the most influential attributes and external factors through the clustering and demand modeling process. By identifying and focusing on key variables that drive guest behavior within each cluster, the system achieves high prediction accuracy without being overwhelmed by all possible data points.
Solution Approach 2:
The system performs preliminary clustering of guests based on multiple attributes before building demand models. This preliminary segmentation organizes the data structure in advance, making subsequent analysis more efficient and reducing computational complexity by grouping similar guests together who will share the same demand characteristics.
Data Source
AI summary
Embodiments model demand and pricing for hotel rooms. Embodiments receive historical data regarding a plurality of previous guests, the historical data including a plurality of attributes including guest attributes, travel attributes and external factors attributes. Embodiments generate a plurality of distinct clusters based the plurality of attributes using machine learning soft clustering and segment each of the previous guests into one or more of the distinct clusters. Embodiments build a model for each of the distinct clusters, the model predicting a probability of a guest selecting a certain room category and including a plurality of variables corresponding to the attributes. Embodiments eliminate insignificant variables of the models and estimate model parameters of the models, the model parameters including coefficients corresponding to the variables. Embodiments determine optimal pricing of the hotel rooms using the model parameters and a personalized pricing algorithm.