Ancillary Bundle Recommendations with Segmented Dynamic Pricing
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Solution Overview
Problem
Existing systems fail to optimize ancillary bundle recommendations and pricing for segmented customers, leading to missed revenue opportunities due to lack of customer segmentation, pairwise bundle interaction modeling, and non-optimal pricing strategies, particularly for perishable products.
Innovation Solution
A method and system that utilize Segmented Customer Bayesian Personalized Ranking Learning to Rank (SCBPR-LTR) and Segmented Customer Optimal Bundle Pricing (SCOBP) techniques to analyze historical ancillary purchase data, segment customers, rank bundle preferences, and optimize prices for maximizing revenue, incorporating features like customer travel attributes and bundle interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing solutions consider only limited or very few bundles without diversity, then the system complexity is reduced, but the revenue maximization capability and customer preference accuracy deteriorate
Solution Approach 1:
The patent segments customers into different groups based on their ancillary purchase behavior and preferences. By dividing the customer base into segments, the system can recommend diverse bundles tailored to each segment without overwhelming system complexity. This segmentation enables the handling of multiple bundle types while maintaining manageable computational requirements.
Solution Approach 2:
The patent introduces a new dimension of bundle interactions by modeling pairwise interactions between ancillary items. This goes beyond traditional single-item recommendations by considering how bundles of items interact together, creating a multi-dimensional recommendation space that captures complex customer preferences while structured approaches keep the system manageable.
2Device complexity
If existing solutions lack modeling of pairwise bundle interactions, then the model simplicity is maintained, but the bundle recommendation accuracy and customer preference learning deteriorate
Solution Approach 1:
The patent pre-computes and stores pairwise interaction matrices between ancillary items before the recommendation process. By performing this computation in advance, the system captures complex bundle interactions without adding computational burden during real-time recommendations, thus maintaining model simplicity while improving recommendation accuracy.
Solution Approach 2:
The patent replaces complex real-time interaction calculations with pre-computed interaction matrices and lookup tables. This substitution transforms the computational problem from one requiring complex real-time processing to one using simpler matrix operations and data retrieval, maintaining model simplicity while achieving high recommendation accuracy.
3Device complexity
If pricing is based only on discounts rather than optimized for maximum revenue, then the pricing simplicity is maintained, but the revenue maximization capability deteriorates
Solution Approach 1:
The patent implements dynamic pricing that adapts to customer segments, bundle compositions, and demand conditions. Rather than static discount-based pricing, the system adjusts prices dynamically based on learned customer preferences and bundle interactions, enabling revenue optimization while structured dynamic pricing keeps the complexity manageable.
Solution Approach 2:
The patent changes pricing parameters based on customer segment characteristics and bundle composition. By adjusting price parameters according to segment-specific preferences and bundle interactions, the system moves from fixed discount pricing to flexible parameter-based pricing that optimizes revenue while maintaining manageable complexity through parameterization.
4Adaptability or versatility
If granular segmentation distinguishing various customer segments based on ancillary bundle purchase behavior is implemented, then the recommendation personalization is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments customers into distinct groups based on their ancillary purchase behavior patterns. By clustering customers with similar preferences together, the system achieves personalized recommendations for each segment while processing data in manageable groups rather than individually for each customer, thus reducing overall data processing complexity.
Solution Approach 2:
The patent creates segment profiles that can be applied universally across multiple customers within the same segment. By learning segment-level preferences and applying them to all members, the system achieves personalized recommendations at scale without processing each customer's data independently, reducing data processing complexity while maintaining personalization.
Data Source
AI summary
The embodiments of present disclosure herein address unresolved problem of how to make optimized decisions jointly on ancillary bundles recommendation and optimal ancillary bundle pricing using data in order to increase the traffic and maximize revenues remains challenging. Embodiments herein provide a method and system for generating ancillary bundled offers by jointly optimizing the customer bundle preferences of ancillary product bundles and revenue maximizing bundle prices for segmented customers using customers' historical ancillary purchase data. The historical ancillary purchase data can include historical bundles purchase data, or historical items purchase data depending upon the service provider, whether they offer bundles or items only. The overall flow of the disclosure for recommending top-k ancillary bundle offers for segmented customers by jointly optimizing the ancillary bundle products/services and bundle pricing for existing and new customers is provided.