Automated MLM Structure Merging with Segmented Commission Trees
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Solution Overview
Problem
Merging multi-level marketing (MLM) companies with different commission structures poses challenges, as existing methods often require maintaining separate structures or disrupting existing income streams, leading to member disorientation and income loss.
Innovation Solution
A system and method for merging two or more MLM data structures into a merged multiline MLM data structure, allowing each member to maintain their existing downlines without changes, and automatically suggesting positions for new members based on criteria like geographic location, income level, and occupation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MLM companies with different commission structures are merged into a single structure, then integration efficiency improves, but existing members experience disorientation and income loss
Solution Approach 1:
The patent segments the commission structure into multiple independent parallel structures (binary, unilevel, matrix) that coexist within a single MLM system. Each member retains their original commission structure type, allowing seamless integration of companies with different structures while preserving individual income streams and avoiding member disorientation.
Solution Approach 2:
The patent creates a universal MLM platform that supports multiple commission structure types simultaneously. The system is designed to handle diverse commission structures (binary, unilevel, matrix) within a single unified framework, enabling integration of companies with different structures without requiring conversion or disruption to existing members.
2Reliability
If MLM companies with different commission structures are kept separate, then existing income streams are maintained, but integration and resource sharing are limited
Solution Approach 1:
The patent merges multiple MLM companies with different commission structures into a single integrated system where each company maintains its original commission structure. This allows resource sharing, cross-promotion, and unified management while preserving the unique characteristics and income streams of each individual company.
3Device complexity
If commission trees are merged to unify structure, then organizational simplicity improves, but member positions change causing disorientation and income loss
Solution Approach 1:
The patent segments the organizational structure into multiple parallel commission trees, each maintaining its original structure and member positions. This segmentation allows unified management at the platform level while preserving the integrity and stability of individual commission trees, preventing member disorientation and income loss.
4Measurement precision
If new members are manually placed in commission structures, then placement accuracy improves, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements an automated system that performs new member placement without manual intervention. The system automatically analyzes new member profiles, evaluates compatibility with existing commission structures, and places members in optimal positions based on predefined criteria, eliminating time-consuming manual operations while maintaining high placement accuracy.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors member performance and commission structure effectiveness. This feedback is used to refine placement algorithms and improve future placement decisions, ensuring high accuracy while maintaining automation efficiency.
Data Source
AI summary
Disclosed herein is a system and method to any two or more MLMs to be merged into a multiline MLM system despite having different commission structures. Each member of the original MLMs is able to maintain their existing downlines without any changes. The system may further automatically suggest positions in the commission tree for new members based on specified criteria including such factors as geographic location, income level, occupation, gender, social or political disposition, etc.