Automated Service Issue Detection via NLP and Web Analytics
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Solution Overview
Problem
Customer service interactions often fail to detect and report underlying technical issues in computing applications, as service professionals may resolve customer complaints without identifying or communicating the root cause of the problem, leading to undetected issues that persist over time.
Innovation Solution
A system that analyzes communication data between customer service professionals and users using natural language processing algorithms, combined with website analytics, to identify and prioritize unreported service issues by extracting relevant concepts and generating service issue records for troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If customer service professionals manually resolve customer complaints, then customer service efficiency is improved, but underlying technical issues remain undetected and unreported
Solution Approach 1:
The system implements automated feedback loops by monitoring customer service interactions and website analytics to detect underlying technical issues. The NLP algorithm analyzes communication data to identify potential problems, and the system continuously monitors website analytics to confirm issues, creating a closed-loop feedback mechanism that ensures technical issues are detected and reported automatically without burdening customer service professionals
Solution Approach 2:
The system enables self-service by automatically detecting and reporting technical issues without requiring customer service professionals to manually identify or communicate them. The automated monitoring and analysis systems perform the detection function independently, allowing the organization to benefit from issue detection without consuming customer service personnel time
2Ease of operation
If customer service professionals focus on resolving immediate customer complaints, then customer satisfaction is improved, but root cause analysis is neglected
Solution Approach 1:
The system introduces an intermediary automated analysis layer between customer service interactions and root cause identification. The NLP algorithm acts as a mediator that extracts and analyzes information from customer service communications, while website analytics serve as another intermediary that provides objective technical data. This intermediary layer handles the complex task of root cause detection, allowing customer service professionals to focus on customer satisfaction while the system independently performs deep technical analysis
3Measurement precision
If automated systems monitor all customer interactions, then issue detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the monitoring task into distinct functional modules: communication data collection, analytics data collection, NLP processing, pattern matching, and issue identification. Each module performs a specific function and can be independently configured and maintained. This segmentation allows the system to achieve comprehensive monitoring capability while keeping individual components manageable and reducing overall system complexity
Data Source
AI summary
Identifying unreported issues in computing applications based on customer service interactions and website analytics. A computing system may receive communication data between a CSP and a user. The system may analyze the communication data using an NLP algorithm to identify a plurality of concepts in the communication data. The system may identify, based on the concepts, a target application associated with the communications between the CSP and the user. The system may receive analytics data from a web server hosting the target application and identify a feature of the target application that is not functioning. The system may then assign a priority to the feature of the target application that is not functioning based on a type of the feature and the received analytics data and generate a service issue record for the feature of the target application that is not functioning and the assigned priority.