Partner Fee Recommendation Service Using Deep Neural Network
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Solution Overview
Problem
Online marketplaces face challenges in determining optimal fee structures for merchants, as existing methods lack data-driven approaches to compete effectively and provide customized fee recommendations based on various transaction data sources.
Innovation Solution
A system utilizing a deep neural network (DNN) to analyze and classify payment transactions, separating purchase and partner fee components, and recommending partner fees based on data from multiple marketplaces, including onboarding, sales, and transaction data, to provide competitive and customized fee structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a percentage-based fee model is adopted to provide attractive incentives for sellers, then seller adoption increases, but marketplace revenue potential decreases
Solution Approach 1:
The system dynamically adjusts fee structures based on real-time analysis of multiple factors including transaction volume, seller performance metrics, marketplace category, and competitive positioning. Instead of static percentage-based or flat fees, the system generates optimized fee recommendations that adapt to changing conditions, allowing marketplaces to maximize revenue while maintaining seller attractiveness.
Solution Approach 2:
The system changes multiple parameters simultaneously to optimize fee structures - adjusting base fees, percentage rates, tier thresholds, and promotional discounts based on analyzed data. This multi-parameter optimization allows the system to resolve the contradiction by finding fee configurations that balance seller appeal with revenue maximization.
2Ease of manufacture
If manual fee structure determination is used, then implementation is simple, but accuracy and competitiveness of fee recommendations deteriorate
Solution Approach 1:
The system replaces manual, mechanical fee determination processes with an automated machine learning system. The ML model analyzes complex datasets including transaction histories, seller profiles, market conditions, and competitive benchmarks to generate precise fee recommendations, eliminating the need for time-consuming manual analysis while significantly improving accuracy.
Solution Approach 2:
The system enables marketplaces to self-generate optimized fee structures without external consultants or manual intervention. The automated ML system continuously processes data and provides actionable fee recommendations that improve upon manual determination methods while remaining easy to implement through automated workflows.
3Measurement precision
If data from multiple marketplaces is analyzed to provide customized recommendations, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system uses a universal ML framework that handles multiple data sources and analysis functions through a single integrated platform. The same core model processes transaction data, seller profiles, market conditions, and competitive information, providing customized recommendations across different marketplaces without requiring separate systems for each function.
Solution Approach 2:
The ML model acts as an intermediary that processes complex multi-source data and transforms it into actionable fee recommendations. This intermediary layer simplifies the system architecture by centralizing data processing and analysis, making the complexity manageable while maintaining high recommendation accuracy through sophisticated data synthesis.
Data Source
AI summary
Methods and systems are provided to determine a partner fee recommendation. Such a partner fee may be a flat fee, percentage-based fee, or both, that a marketplace provider charges for sellers to use their platform. The partner fee recommendation may be calculated based on a plurality of factors. The partner fee recommendation may be based on data from a plurality of marketplaces. At least a portion of such data may be collected by a payment service provider or other service provider that provides services at multiple levels of transactions and through a plurality of marketplace platforms.