Social CRM Message Priority Assignment via Historical Pattern Matching
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Solution Overview
Problem
In social CRM environments, identifying high-priority messages from customers across noisy and informal social media channels is challenging for organizations, making it difficult to maintain timely and effective customer communication.
Innovation Solution
A customer relationship management system that retrieves historical conversations, extracts features, and assigns priorities to incoming messages based on categorization and cumulative priorities from past interactions, using both predefined and dynamically generated rules to display the priority for customer agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customers utilize social medium channels to communicate with customer care representatives, then communication channels are expanded and customer reach is increased, but the noise and informality of these channels make it difficult to identify high priority messages
Solution Approach 1:
The patent introduces an intermediary system comprising a processor that acts as a mediator between social media channels and customer care representatives. This intermediary automatically analyzes incoming messages, extracts features, compares them against historical conversation patterns, and generates priority scores, thereby resolving the difficulty of identifying priority messages in noisy social media environments without requiring manual assessment of each message
Solution Approach 2:
The patent replaces the manual mechanical process of customer care representatives reading and prioritizing messages with an automated electronic system. The processor-based system electronically analyzes message features, retrieves historical data, performs pattern matching, and automatically assigns priorities, substituting human cognitive effort with computational processes that can efficiently handle the noise and volume of social media channels
2Measurement precision
If manual review of all incoming messages is performed to identify priority messages, then accurate priority identification may be achieved, but significant time and resources are consumed
Solution Approach 1:
The system enables self-service by allowing incoming messages to automatically assess and prioritize themselves through feature extraction and comparison with historical data. The processor analyzes each message's inherent features (such as keywords, sender history, message content) and autonomously determines its priority level without requiring manual review, thereby achieving accurate priority identification while eliminating time loss associated with human reading and assessment
Solution Approach 2:
The patent implements preliminary action by pre-processing messages through automatic feature extraction and immediate comparison against historical conversation databases. The system performs priority assessment in advance before messages reach customer care representatives, so that when agents receive the message queue, priorities are already determined, eliminating the need for time-consuming manual review at the point of service
3Productivity
If automated priority assignment systems are implemented, then processing speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex priority assignment task into distinct modular components: a feature extraction module that identifies relevant message characteristics, a historical data retrieval module that accesses conversation history, a pattern comparison module that matches features against historical patterns, and a priority scoring module that generates final priorities. This segmentation allows each component to perform its function independently and efficiently, improving overall processing speed while managing system complexity through modular design
Data Source
AI summary
What is disclosed is a customer relationship management system to help customer care agents prioritize a response to an incoming message. Historical conversations between customers and agents are retrieved. Each conversation has associated features and an assigned priority. Features are categorized into a plurality of categories. A priority is assigned to each category based on the features and the cumulative priority of all conversations in each category. Thereafter, an incoming message is received from a customer's computing device. Features are extracted from the incoming message. A determination is then made as to which of the categories this incoming message belongs based on this message's features. A priority is then assigned to the incoming message based on a priority assigned to the category which the incoming message belongs. The priority is then displayed for a customer agent so the agent can prioritize his/her response to that customer's incoming message.