Counterfactual Pricing Policy Evaluation for Online Item Categories
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Solution Overview
Problem
Online concierge systems face challenges in optimizing pricing strategies for numerous items due to computational complexity and interdependencies, leading to suboptimal revenue generation and increased operational complexity.
Innovation Solution
The system categorizes items based on a retailer-specific taxonomy and revenue objectives, applies differentiated pricing policies to categories, trains outcome models on user responses, and adjusts pricing policies to optimize revenue while enforcing constraints, using machine learning models to predict outcomes and adjust prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system evaluates different combinations of prices for different items to optimize revenue, then revenue optimization is improved, but computational complexity and time requirements increase significantly
Solution Approach 1:
The patent segments items into categories based on their characteristics and pricing behaviors. Instead of evaluating every individual item independently (which would be computationally intensive), the system groups similar items together and applies pricing strategies at the category level. This segmentation reduces the complexity from O(n) individual item evaluations to O(c) category-level evaluations, where c is the number of categories.
Solution Approach 2:
The patent merges similar items into categories and evaluates pricing strategies at the category level rather than individually. By combining multiple items with similar characteristics into single evaluation units (categories), the system reduces the total number of price combination evaluations needed, thereby reducing computational complexity while maintaining revenue optimization effectiveness.
2Productivity
If the system applies different markups to different items to account for varying revenue generation, then revenue optimization is improved, but the complexity of determining optimal prices increases
Solution Approach 1:
The patent applies different pricing strategies (markups) to different categories of items based on their local characteristics and revenue potential. Instead of using a uniform markup across all items or attempting to optimize each item individually, the system identifies local patterns at the category level and applies appropriate pricing strategies to each category, simplifying the overall pricing determination process while maintaining revenue optimization.
3Productivity
If the system changes prices of items to optimize revenue, then revenue optimization is improved, but the interrelationships between prices and revenues of different items increase complexity
Solution Approach 1:
The patent segments the pricing problem into category-level decisions rather than individual item decisions. By grouping items with similar pricing behaviors and market responses into categories, the system reduces the complexity of understanding interrelationships between prices and revenues. This allows the system to focus on category-level price-revenue relationships, which are more manageable and less complex than tracking every individual item's price-revenue interaction.
Data Source
AI summary
An online concierge system fulfills orders for items offered by retailers and may increase the price of an item offered by a retailer in some instances. The online concierge system applies a markup to an item by applying a pricing policy to a category including the item. To optimize application of pricing policies to categories, the online concierge system categorizes items offered by the retailer and applies an outcome model to combinations of categories and pricing policies. From the output of the outcome model, the online concierge system selects a set of categories and corresponding pricing policies. Using a price adjustment model, the online concierge system determines modifications to one or more of the pricing policies of the set to enforce one or more constraints across multiple pricing policies.