Product Pricing Optimization Using MNL and Hartwick Models
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Solution Overview
Problem
Current product pricing models, such as the multinomial logit (MNL) model, fail to capture market halo effects associated with complementary items, limiting their ability to optimize category pricing strategies effectively.
Innovation Solution
A product pricing system that encodes MNL calibration parameters and constraints into a mixed-integer program (MIP), using a bi-level predictive modeling framework to manage price elasticity and competition, and applies the Reformulation-Linearization Technique (RLT) to transform the model into a solvable MIP formulation, thereby optimizing product prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the MNL model is used for product pricing optimization, then the model can capture inter-item interactions among substitutable items, but it cannot capture market halo effects associated with complementary items
Solution Approach 1:
The patent combines the MNL model with the Hartwick model to create a hybrid pricing optimization system. The MNL model captures substitution effects among competing items, while the Hartwick model captures complementarity effects and market halo effects. By merging these two models, the system achieves comprehensive coverage of both substitutable and complementary item interactions, resolving the limitation of the standalone MNL model.
2Reliability
If a comprehensive pricing model capturing all market effects is developed, then pricing optimization accuracy improves, but computational complexity and model solution difficulty increase
Solution Approach 1:
The patent segments the complex pricing optimization problem into two distinct components: the MNL model for substitution effects and the Hartwick model for complementarity effects. This segmentation allows each model to specialize in capturing specific market dynamics, making the overall system more manageable and solvable while maintaining comprehensive coverage of market interactions.
Solution Approach 2:
The patent introduces an intermediary optimization framework that coordinates the outputs of the MNL and Hartwick models. This intermediary layer integrates the substitution and complementarity effects into a unified pricing optimization solution, managing the complexity of solving both models simultaneously while achieving accurate pricing recommendations.
Data Source
AI summary
A product pricing system determines a product price. The system receives product pricing constraints and multinomial logit (“MNL”) calibration parameters. The system then generates a calibrated MNL model using the calibration parameters and encodes the MNL model and the product pricing constraints into a mixed-integer program (“MIP”). The system then solves the MIP to generate the product price.