Directed Customer Support Analytics for Routing
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Solution Overview
Problem
Current customer support systems in retail institutions lack efficiency in routing and responding to customer communications, as they often rely on general representatives and mediums, failing to provide tailored support based on customer data and history.
Innovation Solution
A directed customer support system that analyzes customer data, including support history, browsing history, and transaction data, to determine the subject of a communication and select the appropriate response medium, such as a phone call, chat, or SMS, and routes the communication to a suitable customer support representative.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general customer support representatives and mediums are used, then device complexity is reduced, but customer support effectiveness and personalization deteriorate
Solution Approach 1:
The system performs preliminary analysis of customer data, support history, browsing history, and transaction data before customer inquiries are routed. This pre-processing of information enables personalized support assignments without adding complexity during the actual support interaction, as the analytics determine subject matter expertise requirements and optimal communication channels in advance.
Solution Approach 2:
An analytics system acts as an intermediary between customers and support representatives. This intermediary component analyzes multiple data sources and automatically determines the most appropriate representative and communication medium, thereby improving support effectiveness without requiring complex direct interactions between customers and representatives.
2Measurement precision
If customer data analysis is performed to determine communication medium, then response accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial analysis by focusing on key data elements most relevant to determining the optimal communication medium and representative assignment. Rather than analyzing every possible data point, the system identifies and processes the most critical information from customer data, support history, and transaction records to make efficient routing decisions.
Solution Approach 2:
The system dynamically adjusts analysis parameters and data weighting based on the type of inquiry and customer profile. By changing which data elements are prioritized in the analysis, the system can quickly determine response accuracy requirements and select appropriate communication channels without consistently requiring full-depth analysis of all available data.
3Adaptability or versatility
If analytics system analyzes multiple data sources, then customer support personalization is improved, but computational resources required increase
Solution Approach 1:
The analytics system segments the analysis process into distinct modules that handle different data sources separately (customer data analysis, support history analysis, browsing history analysis, transaction data analysis). This segmentation allows the system to process only the necessary data segments for each routing decision, reducing overall computational resource requirements while maintaining personalization capabilities.
Solution Approach 2:
The analytics system is designed as a multi-functional platform that can analyze various types of data sources using unified analytical frameworks. By creating a universal analysis engine that handles multiple data types through common processing logic, the system reduces redundant computational overhead and optimizes resource utilization across different analysis tasks.
Data Source
AI summary
Various examples are directed to systems and methods for directed customer support. An analytics system may receive support communication data describing a support communication regarding a user account received from a user computing device and determine a subject of the support communication. The analytics system may select a response medium based at least in part on the subject and generate a response message based at least in part on the subject.