Call Routing via Social Influence Scores
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Solution Overview
Problem
Current call handling systems struggle to effectively route user communications to the most appropriate human agents based on social media influence and customer experience, often relying on automated response systems that may not adequately address complex customer issues or provide satisfactory interactions.
Innovation Solution
A call handling platform that analyzes social media data to compute a social network influence score and experience score for callers, using these metrics to route calls to human agents promptly and prioritize interactions based on impact and satisfaction levels, while also offering feedback mechanisms and recording capabilities for quality assurance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated response systems are used to handle customer communications, then operational efficiency is improved, but customer satisfaction deteriorates for complex issues
Solution Approach 1:
The system changes the routing parameter from simple automated rules to dynamic social network influence scores. Calls are routed based on the caller's social media impact level, with high-influence callers automatically directed to human agents while lower-influence callers can be handled by automated systems, thus optimizing both efficiency and satisfaction based on customer value parameters
2Reliability
If all calls are routed to human agents, then customer satisfaction is improved, but operational cost increases
Solution Approach 1:
The system applies different service qualities to different customer segments. High social network influence callers receive premium human agent service, while other callers receive automated service. This localized quality approach ensures that resources are concentrated on customers whose positive feedback has the greatest potential impact, reducing overall operational costs while maintaining satisfaction for key influencers
3Measurement precision
If social network data analysis is implemented, then routing precision is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of social network data beforehand to establish influence scores and customer segments. This pre-computation allows the routing system to make precise decisions using simple lookup tables and predefined thresholds during actual call handling, rather than performing complex real-time analysis, thus achieving high routing precision without excessive system complexity during operation
Data Source
AI summary
A call handling platform receives a call placed by a caller to a calling number. The platform examines parameters of the call, determines identifying information of the caller and matches the identifying information with a social network username corresponding to a social media network. The platform obtains the caller's social network data from the social media network. Using the social network data, the platform computes a social network influence score for the caller. The platform compares the social network influence score to a predetermined influence score threshold value and determines that the social network influence score for the caller indicates that the activity of the caller in the social media network has a high level of impact. The platform accordingly selects a first human agent at a call center and routes the call to the first human agent at the call center.