Pricing Recommendation System Using Efficient Frontier Analysis
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Solution Overview
Problem
Existing systems for optimizing business metrics, such as revenue and profit, face challenges in efficiently managing trade-offs between multiple metrics, leading to computationally intensive processes that limit real-time analysis and user flexibility due to resource constraints.
Innovation Solution
Implementing a two-part method where candidate solutions are generated offline using a Monte Carlo approach and an approximate efficient frontier is generated in real-time, allowing users to select pricing recommendations that satisfy rationality constraints and adapt to changing business parameters, reducing the computational burden and enabling rapid analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex optimization models are used to evaluate pricing strategies and generate efficient frontiers, then the quality and accuracy of pricing recommendations improve, but the computational resources and processing time required increase significantly
Solution Approach 1:
The system performs preliminary computation by pre-calculating and storing multiple candidate pricing solutions in a database before real-time queries. When a pricing recommendation is needed, the system retrieves pre-computed candidates rather than performing full optimization calculations, significantly reducing real-time computational resource consumption while maintaining recommendation quality
Solution Approach 2:
The optimization problem is segmented into multiple independent candidate solutions that are pre-computed and stored. Instead of solving one complex optimization problem in real-time, the system divides the solution space into discrete candidate points that can be generated offline and retrieved on-demand, reducing the computational burden during real-time operations
2Adaptability or versatility
If comprehensive optimization models with multiple metrics and constraints are implemented, then the ability to analyze trade-offs between business metrics improves, but the processing speed and real-time responsiveness deteriorate
Solution Approach 1:
The system pre-computes efficient frontiers for multiple metric combinations and stores them in advance. When users need to analyze trade-offs between different business metrics, the system retrieves pre-calculated efficient frontiers from the database rather than performing new optimization calculations, enabling fast real-time analysis of multiple metrics without the computational overhead
Solution Approach 2:
The complex multi-metric optimization problem is segmented into multiple separate efficient frontier calculations, each for a specific pair of metrics. These segmented calculations are performed offline and stored, allowing the system to provide comprehensive multi-metric analysis capability while maintaining fast response times by retrieving pre-computed results
3Productivity
If real-time generation of efficient frontiers is implemented to provide timely pricing recommendations, then the user flexibility and responsiveness improve, but the computational complexity and resource requirements increase
Solution Approach 1:
The system performs the computationally intensive efficient frontier generation in advance and stores results in a database. Real-time pricing recommendations are generated by retrieving and interpolating from these pre-computed efficient frontiers, providing fast responsive service without requiring complex computational infrastructure during real-time operations
Solution Approach 2:
The system introduces a database as an intermediary between the optimization models and real-time queries. Pre-computed efficient frontiers are stored in the database, serving as a mediator that allows fast real-time retrieval without direct computation, reducing the complexity of the computational infrastructure needed for real-time operations
Data Source
AI summary
Systems and methods for optimizing marketing strategies. Various embodiments implement methods which can include generating a plurality of candidate solutions which satisfy pricing rationality constraints. The candidate solutions can be generated when processing resources are available for performing a Monte Carlo algorithm. The candidate solutions can be stored and a master and trade off metric can be selected. Values for these selected metrics can be evaluated (at the candidate solutions) and then input into the approximate efficient frontier algorithm. The algorithm can output an approximate efficient frontier. Users can select any of the efficient solutions on the frontier to obtain associated pricing recommendations by mousing over the efficient frontier. Various metrics associated with the efficient frontier may be updated at about the same time as the master and trade off metrics are selected.