Segmented Ancillary Bundle Recommendations With Joint Pricing Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to optimize customer ancillary bundle preferences and pricing for segmented customers, leading to missed revenue opportunities due to lack of granular segmentation, limited bundle interactions, and non-scalable solutions, especially for perishable products.
Innovation Solution
A method and system that utilizes Segmented Customer Bayesian Personalized Ranking Learning to Rank (SCBPR-LTR) and Segmented Customer Optimal Bundle Pricing (SCOBP) techniques to optimize ancillary bundle recommendations and pricing for segmented customers based on historical purchase data, converting item interactions into bundles and identifying customer feature vectors for personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If granular customer segmentation is implemented to personalize bundle recommendations, then customer preference accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the customer base into distinct segments based on purchase behavior, demographics, and preferences. Each segment receives personalized bundle recommendations tailored to their specific characteristics. This segmentation approach enables precise measurement of customer preferences while managing system complexity through structured categorization rather than treating each customer individually.
Solution Approach 2:
The system changes key parameters including customer segmentation criteria, bundle composition variables, and pricing parameters. By systematically varying these parameters across different segments, the system achieves high preference accuracy without requiring equally complex processing for every customer, thus resolving the contradiction between precision and complexity.
2Measurement precision
If multiple bundle interactions are modeled to improve recommendation quality, then recommendation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments bundle interactions by customer segment rather than modeling all possible interactions across the entire customer base. This segmentation reduces computational complexity by limiting the interaction space that needs to be modeled for each segment, while still achieving high recommendation accuracy within each segmented group.
Solution Approach 2:
The system models bundle interactions partially by focusing on the most relevant interactions for each customer segment rather than all possible interactions. This selective modeling approach maintains high recommendation accuracy for each segment while significantly reducing overall computational complexity compared to exhaustive interaction modeling.
3Loss of energy
If optimized bundle pricing is implemented to maximize revenue, then revenue is improved, but pricing model complexity increases
Solution Approach 1:
The patent implements optimized bundle pricing by changing pricing parameters based on customer segment, bundle composition, and demand characteristics. The system adjusts prices systematically across different segments and bundles, maximizing revenue through parameter optimization rather than requiring equally complex pricing models for all customers, thus resolving the contradiction between revenue maximization and model complexity.
Data Source
Figure 1
Figure 2
Figure 3
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.