Recurring Revenue Data Integration and Conflict Resolution
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Solution Overview
Problem
Current approaches to managing recurring revenue assets are inefficient due to fragmented, inaccurate, and incomplete data from disparate sources, lack of specialization in service contracts, and limited analytics and KPIs, leading to suboptimal revenue realization and renewal management.
Innovation Solution
A recurring revenue management system that integrates data from multiple sources into a unified repository, generates a recurring revenue asset data model, and performs data cleaning and conflict resolution, enabling analytics and business intelligence to optimize sales and renewals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple disparate sources (CRM, PRM, data warehouses, entitlement systems, billing systems), then the quantity and coverage of recurring revenue data increases, but data accuracy, completeness and consistency deteriorate due to fragmentation and lack of standardization
Solution Approach 1:
The patent merges data from multiple disparate sources (CRM systems, PRM systems, data warehouses, entitlement systems, billing systems) into a single unified recurring revenue data warehouse. This consolidation eliminates data fragmentation, establishes a single source of truth, and enables consistent data standardization across all recurring revenue assets while maintaining comprehensive coverage from all original sources.
Solution Approach 2:
The unified data warehouse serves as an intermediary layer between various source systems and analytical applications. It receives data from multiple disparate sources, performs standardization and quality validation, and delivers cleaned, consistent data to analytics and business intelligence tools, thereby improving data accuracy without losing quantity.
2Ease of operation
If existing sales management tools and processes are used for recurring revenue, then ease of operation is maintained, but effectiveness deteriorates because these tools are optimized for initial sales rather than renewal management
Solution Approach 1:
The system creates a universal recurring revenue management platform that handles multiple functions previously requiring separate tools: tracking recurring assets, managing renewals, analyzing customer behavior patterns, predicting churn, and optimizing revenue. This multi-functional approach maintains operational simplicity while dramatically improving revenue realization effectiveness.
Solution Approach 2:
The system implements continuous feedback loops that monitor recurring revenue performance, customer behavior patterns, and renewal trends. This feedback enables the system to automatically adjust management strategies, optimize renewal timing, and improve revenue realization without requiring complex manual processes, thus maintaining ease of operation while boosting productivity.
3Measurement precision
If specialized recurring revenue management systems are implemented, then data accuracy and analytical capability improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The system segments the complex recurring revenue management function into modular components: data collection modules from various sources, data standardization modules, quality validation modules, analytical processing modules, and application delivery modules. This segmentation reduces overall system complexity by allowing independent development, testing, and maintenance of each component while achieving high data accuracy through coordinated operation.
Data Source
AI summary
In a recurring revenue management system, a first unit of data and a second unit of data is received. Content is extracted from the first unit of data and the second unit of data based on one or more parameters of a predefined data object that is part of an asset data model. The extracted content is added to an instance of the predefined data object, and a reference tag with identification information for its associated content is associated with the content extracted from each of the first unit of data and the second unit of data. A duplicate data condition can be detected and resolved by applying a predefined approach to conflict resolution based on the identification information in the reference tags of the content of the first unit of data and the second unit of data. Related methods, systems, and computer program products are also described.