AI Hotel Demand Model Using Dynamic Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The hotel industry faces challenges in accurately predicting demand for hotel rooms due to heterogeneous customer populations with varying willingness-to-pay, rate plan selections, travel attributes, booking channels, and other factors, leading to ineffective traditional one-size-fits-all revenue management policies.
Innovation Solution
A dynamic clustering approach using a semi-parametric mixture of discrete choice models to form clusters based on customer attributes, update weights and probabilities iteratively, and maximize a weighted likelihood function to predict choice probabilities for room categories and service type combinations, allowing for personalized pricing and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional one-size-fits-all revenue management policies are used, then device complexity is reduced, but prediction accuracy deteriorates due to heterogeneous customer populations
Solution Approach 1:
The patent segments the homogeneous customer population into multiple heterogeneous clusters based on travel attributes, booking behaviors, and willingness-to-pay. Each cluster is modeled with its own discrete choice model parameters, allowing for personalized demand prediction that captures customer heterogeneity while maintaining manageable complexity through targeted segmentation.
Solution Approach 2:
The patent applies local quality by allowing different model parameters and cluster assignments for different customer segments. Each cluster receives customized modeling treatment with cluster-specific parameters in the multinomial logit models, rather than applying a single uniform model to all customers, thereby improving prediction accuracy for heterogeneous populations.
2Measurement precision
If static clustering methods are used, then device complexity is reduced, but prediction accuracy deteriorates by 4% compared to dynamic clustering
Solution Approach 1:
The patent implements dynamic clustering that adapts to customer characteristics and booking contexts. The clustering algorithm dynamically assigns customers to clusters based on their specific attributes and the current offering, rather than using fixed static segments. This dynamic approach captures changing customer preferences and behaviors, improving prediction accuracy by 4% over static methods.
Solution Approach 2:
The patent incorporates feedback mechanisms where cluster assignments and model parameters are continuously refined based on observed customer choices and booking outcomes. The system learns from actual customer behavior patterns and adjusts cluster definitions and parameters accordingly, creating a feedback loop that improves prediction accuracy while managing complexity through iterative refinement.
Data Source
AI summary
Embodiments generate a demand model for a potential hotel customer of a hotel room. Embodiments, based on features of the potential hotel customer, form a plurality of clusters, each cluster including a corresponding weight and cluster probabilities. Embodiments generate an initial estimated mixture of multinomial logit (“MNL”) models corresponding to each of the plurality of clusters, the mixture of MNL models including a weighted likelihood function based on the features and the weights. Embodiments determine revised cluster probabilities and update the weights. Embodiments estimate an updated estimated mixture of MNL models and maximize the weighted likelihood function based on the revised cluster probabilities and updated weights. Based on the update weights and updated estimated mixture of MNL models, embodiments generate the demand model that is adapted to predict a choice probability of room categories and rate code combinations for the potential hotel customer.