Anti-trending Message Analysis for Influential Customer Issues
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Solution Overview
Problem
Existing systems for monitoring social media messages do not automatically identify and address significant, actionable content beyond the primary reason for categorization, potentially missing critical issues from influential customers.
Innovation Solution
A three-step process involving dimensionality reduction, subtraction of common variables, and intent determination to identify and act on secondary meanings within message clusters, including special routing and escalation procedures for verified, influential users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If keyword and phrase searches are used to track messages, then large amounts of data about a company can be monitored, but many similar comments and posts are clustered together without identifying significant individual messages
Solution Approach 1:
The system extracts and removes common variables and expected content from message clusters to isolate and identify significant individual messages. By subtracting the common denominator from each message in a cluster, the system highlights the unique, actionable information that would otherwise be lost in the volume of similar messages.
Solution Approach 2:
The system applies different processing quality to different messages within clusters. Instead of uniform treatment, it identifies messages with local significance (those containing unique, actionable information beyond the cluster's common theme) and routes them differently for specialized attention.
2Productivity
If all messages within a cluster are processed using default rules, then processing is efficient and automated, but significant messages from influential customers are not identified for special attention
Solution Approach 1:
The system applies different processing quality to different messages within clusters. Instead of uniform treatment, it identifies messages with local significance (those containing unique, actionable information beyond the cluster's common theme) and routes them differently for specialized attention.
Solution Approach 2:
The system uses feedback loops to continuously learn from message patterns and improve identification of significant messages. By analyzing which messages require special attention and which can be handled by default rules, the system refines its clustering and significance detection algorithms over time.
3Adaptability or versatility
If messages are clustered by common themes, then related messages are grouped together for analysis, but individual messages with unique significant content are not distinguished
Solution Approach 1:
The system extracts and removes common variables and expected content from message clusters to isolate and identify significant individual messages. By subtracting the common denominator from each message in a cluster, the system highlights the unique, actionable information that would otherwise be lost in the volume of similar messages.
Solution Approach 2:
The system segments message analysis into two levels: cluster-level analysis for common themes and individual message analysis for unique significant content. This segmentation allows the system to simultaneously leverage the efficiency of clustering while preserving the ability to identify and act on individual messages with special significance.
Data Source
AI summary
An automated system for message analysis whereby messages within a given category may be identified and processed as a category connote. While a domain of messages may be monitored and processed in the due course of business, connote message are different. For example, a number of messages may fall into a domain of “poor airline food.” Such messages may be processed in the due course of business. However, a message with a different aspect, such as, “I found glass in my food,” may be initially identified as begin within the domain of “poor airline food,” and processed further to distinguish the message as being a connote with regard to the “poor airline food” category and warranting special handling.