Trust Object System for Automated Marketing Compliance
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Solution Overview
Problem
Businesses face challenges in maintaining compliance with varying regional regulations and customer engagement preferences in electronic marketing, requiring cumbersome manual modifications to contact lists.
Innovation Solution
A system that tracks person-related data sources using trust objects with metadata, allowing for automatic filtering and replacement of non-compliant data, and adaptation to changing regulations and preferences by appending trust metadata to social media content and business-to-business entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual modifications are used to maintain compliance with regulations and customer preferences, then compliance can be achieved, but the process becomes cumbersome and inefficient
Solution Approach 1:
The system automatically monitors, filters, and updates contact lists based on regulatory changes and customer preferences without requiring manual intervention. The trust layer self-manages compliance by automatically appending trust metadata, identifying non-compliant data, and executing removal or replacement actions, thereby achieving reliable compliance while eliminating cumbersome manual operations
Solution Approach 2:
The system proactively appends trust metadata to contact list data in advance, establishing a trust layer before compliance issues arise. This preliminary action enables the system to pre-identify potential non-compliant data and prepare automated responses to regulatory changes or preference updates, ensuring compliance is maintained continuously without reactive manual modifications
2Reliability
If existing methodologies are used to modify contact lists based on customer norms, then compliance can be maintained, but the process requires designated personnel and becomes extremely cumbersome
Solution Approach 1:
The patent replaces the mechanical system of manual contact list modification with an automated information processing system. The trust layer uses algorithms and automated processes to monitor regulatory changes, evaluate contact list compliance, and execute modifications, substituting human personnel operations with automated computational mechanisms that reduce complexity while maintaining reliable compliance
Solution Approach 2:
The trust layer acts as an intermediary between contact list data and compliance requirements. It automatically appends trust metadata containing regulatory and preference information to contact data, serving as a mediator that translates complex compliance rules into automated filtering and modification actions, thereby simplifying the overall system while ensuring reliable compliance maintenance
3Adaptability or versatility
If suppliers manually conform to changing customer engagement preferences, then customer satisfaction can be improved, but the process becomes extremely cumbersome and inefficient
Solution Approach 1:
The system automatically adapts contact lists to changing customer preferences through self-service mechanisms. The trust layer continuously monitors preference changes, automatically filters non-compliant contacts, and executes updates without requiring supplier intervention, thereby achieving high adaptability to customer preferences while maintaining high productivity through automation
Solution Approach 2:
The system implements continuous feedback loops where customer preference changes are automatically detected, evaluated against existing contact lists, and translated into immediate modifications. The trust layer receives feedback from regulatory updates and customer preference changes, processes this information automatically, and adjusts the contact lists in real-time, ensuring high adaptability while maintaining efficient productivity through automated feedback-driven adjustments
Data Source
AI summary
The technology disclosed relates to enhancing trust for person-related data sources by tracking person-related sources using trust objects that hold trust metadata. In particular, it relates to generating trust-enhanced data by appending trust metadata to social media content and other business-to-business entities, and further using the trust-enhanced data to develop social engagement models based on customer preferences. The trust metadata described includes names, interface categories and origins of the person-related data sources along with customer engagement preferences and connection types.