Call Importance Scoring via Social Network Context
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Solution Overview
Problem
Current contact centers rely on caller identity and call topic to determine call importance, which is insufficient for providing tailored customer service, as it does not utilize the vast amount of data available in social network contexts.
Innovation Solution
A system and method that determine call importance by generating an importance score using social network context, including social network profiles, graphs, posts, and interactions, in conjunction with CRM data and call center resource availability, to route calls to appropriate agents and provide customized service levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If call importance is determined using only caller identity and call topic, then the system complexity is low, but the accuracy of call importance assessment is insufficient
Solution Approach 1:
The patent combines multiple data sources including social network context (profiles, graphs, posts, interactions), CRM data, and call center resource availability into a unified importance score calculation system. This merging of diverse data types enables more accurate call importance assessment by considering both traditional factors (caller identity, call topic) and new social network-based factors simultaneously.
Solution Approach 2:
The system creates a multi-functional importance scoring mechanism that serves multiple purposes: assessing call importance, routing calls to appropriate agents, determining service levels, and optimizing resource allocation. The social network context data is utilized across multiple functions including caller identification, importance scoring, and service level determination, making the system more versatile and accurate.
2Measurement precision
If social network context data is collected and processed, then call importance assessment accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the social network context data into distinct components including social network profiles, social graphs, posts, and interactions. Each segment is processed and evaluated separately before being integrated into the overall importance score. This segmentation reduces processing complexity by allowing modular analysis of different data types while maintaining comprehensive assessment accuracy.
Solution Approach 2:
The system introduces an intermediary processing layer that translates complex social network data into actionable importance scores. This intermediary mechanism processes and interprets social network context, CRM data, and resource availability information, converting them into a unified scoring system that can be easily applied for call routing and service level determination without requiring direct complex analysis of all underlying data.
3Productivity
If calls are routed based on importance scores, then resource allocation efficiency improves, but the system requires more integration with social networks
Solution Approach 1:
The system performs preliminary action by pre-processing and storing social network context data, CRM information, and resource availability data before calls arrive. Importance scores are calculated in advance based on pre-fetched data, enabling rapid call routing decisions when calls actually occur. This preliminary preparation improves resource allocation efficiency while reducing the complexity of real-time processing requirements.
Solution Approach 2:
The system enables self-service by automatically calculating importance scores and routing calls based on pre-established criteria and social network data. The automated scoring and routing mechanism eliminates manual intervention, allowing the system to independently assess call importance and allocate resources optimally without requiring complex manual coordination or additional human processing.
Data Source
AI summary
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for determining call importance using social network context. A system can receive a call from a caller and establish the identity of the caller. The system can then retrieve a social network context associated with the caller identity from a social network and determine an importance score for the call using the social network context. Social network contexts can be derived from a social network profile, caller utterances, and a social graph. Based on the importance score, the contact center provides an appropriate level of customer service. The level of customer service a contact center provides can be based on resource availability, call type, call time, agent queue selection, offered communication modalities and customer follow-up.