Universal Data Modeling for Holistic Marketing View
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Solution Overview
Problem
Customer Relationship Management (CRM) tools lack a holistic view of marketing campaign effectiveness, making it difficult to track the impact of marketing efforts on sales and customer interactions across various channels, and fail to provide insights on which channels are most effective for specific customers or customer clusters.
Innovation Solution
A system and method for universal data modeling that acquires and processes diverse data types from internal and external sources, applies quality control, enriches data through natural language processing, and uses machine learning to associate data with customers, providing an organizational-wide view of customer relationships and enabling targeted marketing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If CRM tools track separate distribution channels, then data collection from each channel is maintained, but holistic view of marketing campaign effectiveness is lost
Solution Approach 1:
The patent merges data from multiple separate distribution channels (digital, print, direct mail, telemarketing, social media) into a unified CRM system. This integration allows holistic tracking of customer interactions across all channels while maintaining the individual channel data structures, resolving the contradiction between information completeness and system complexity.
Solution Approach 2:
The CRM system is designed with universal data structures and analytics capabilities that can handle multiple data types and channels through a single interface. The system provides multi-functional analytics that work across all distribution channels simultaneously, eliminating the need for separate tracking systems while preserving channel-specific information.
2Loss of information
If marketing personnel use traditional CRM tools, then basic customer data is tracked, but insights on channel effectiveness for specific customers are unavailable
Solution Approach 1:
The system implements feedback loops that continuously analyze customer interaction data across channels and provide actionable insights back to marketers. Analytics engine processes data and generates recommendations on which channels are most effective for specific customer segments, enabling data-driven marketing decisions and improving productivity.
Solution Approach 2:
An analytics engine acts as an intermediary between raw channel data and marketing decisions. This intermediary layer processes, analyzes, and translates multi-channel data into meaningful insights about channel effectiveness for different customer segments, making the information accessible and actionable for marketers.
3Loss of information
If data from multiple sources is integrated, then comprehensive customer view is achieved, but data quality and consistency become problematic
Solution Approach 1:
The system dynamically adjusts data processing parameters based on the source and type of data being integrated. Different data quality thresholds, validation rules, and cleaning procedures are applied to different channels and data types, allowing comprehensive data integration while maintaining high quality standards through parameter optimization.
Data Source
AI summary
Systems and methods for universal data modeling are disclosed. Exemplary embodiments include an information processing apparatus that can acquire a plurality of different data types form different sources that contains both anonymous and nonanonymous customer data. This data is ingested into a data repository and a quality control check is applied to the data The data is enriched and data analytics are applied to the data in order to associate at least some of the data with a customer.