Fee Benchmarking Tool for Managed Accounts
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Solution Overview
Problem
Financial professionals face difficulty in setting optimal fees for managed accounts due to lack of reliable market data and guidance, making it challenging to balance revenue maximization with competitiveness and client-specific variables.
Innovation Solution
A method is provided to evaluate proposed fees using a computer-based tool that compares the proposed fee to a range of historical fees from similar accounts, displayed in a visual format, allowing professionals to assess their pricing strategy relative to competitors and adjust accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If professionals charge higher fees to maximize revenue, then revenue increases, but competitiveness decreases and clients may leave
Solution Approach 1:
The system provides feedback to professionals about their fee levels relative to peers by displaying benchmarking data and competitiveness assessments. This feedback loop enables professionals to adjust their fees based on market conditions and peer comparisons, optimizing revenue while maintaining competitiveness.
Solution Approach 2:
The system allows professionals to change the fee parameter based on multiple variables including account size, asset mix, service level, and peer comparisons. By enabling dynamic adjustment of the fee parameter according to specific account characteristics and market conditions, professionals can maximize revenue without losing clients to competitors.
2Object-affected harmful factors
If professionals charge lower fees to remain competitive, then client retention improves, but revenue potential decreases
Solution Approach 1:
The system enables differentiated fee structures where different fees can be charged to different clients based on their specific characteristics, account size, asset mix, and service requirements. This local quality approach allows professionals to charge higher fees to clients who can bear them while maintaining lower fees for price-sensitive clients, thereby maximizing overall revenue while maintaining competitiveness.
3Ease of operation
If professionals lack accurate market pricing information, then pricing discretion freedom is maintained, but pricing optimization becomes difficult
Solution Approach 1:
The system acts as an intermediary that provides professionals with market pricing information and benchmarking data without removing their pricing discretion. The system delivers objective feedback about peer fees and competitiveness, enabling professionals to make informed pricing decisions while maintaining the freedom to set final fees based on their judgment and client relationships.
4Productivity
If professionals use complex pricing strategies to optimize revenue, then revenue potential increases, but decision-making complexity increases
Solution Approach 1:
The system enables professionals to independently evaluate and optimize their pricing decisions using automated benchmarking tools and peer comparison data. By providing self-service capabilities including fee calculators, competitiveness assessments, and scenario analysis, the system empowers professionals to make revenue-optimizing decisions without requiring complex external analysis or support.
Data Source
AI summary
A computer for displaying an evaluation tool for evaluating a proposed fee on behalf of a professional, including a memory having at least one region for storing computer executable program code and a processor for executing the program code stored in the memory. The program code includes code for receiving a product type for a proposed client account, a proposed fee for the proposed client account, and at least one of the following numerical parameters: (i) an account asset amount for the proposed client account, (ii) a relationship asset amount for the proposed client account, and (iii) a breakdown of components in an asset mix for the proposed client account. A range of historical fees of the nearest neighbor historical accounts is shown on a graph from lowest to highest, and the proposed fee is shown on the graph relative to the nearest neighbor historical fees.


