Microservices for Client Experience Assessment in Incident Management
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Solution Overview
Problem
Assessing client experience in incident management is often reliant on time-consuming and inaccurate surveys, lacking objective and quantitative measures, which hinders organizations in improving customer service and decision-making.
Innovation Solution
A computing system utilizing microservices, including a database polling microservice, client experience analysis microservice, and client experience reporting microservice, that retrieves incident tickets, performs sentiment analysis, calculates client experience indices, and generates reports to provide an objective and quantitative assessment of client experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surveys are used to assess client experience, then some assessment data can be collected, but the process is time-consuming and inaccurate
Solution Approach 1:
The patent replaces manual survey collection and analysis with automated microservices that extract communication messages from incident tickets, perform sentiment analysis using NLP algorithms, and calculate client experience indices automatically. This substitution of mechanical survey processes with automated digital processing eliminates time consumption while improving measurement precision through objective quantitative analysis.
Solution Approach 2:
The system enables self-service by automatically monitoring incident tickets and computing client experience metrics without requiring manual intervention. The microservices autonomously extract data, analyze sentiment, and generate reports, allowing the system to assess itself rather than requiring external survey participation from clients.
2Reliability
If traditional incident management systems are used, then incident tracking is maintained, but client experience assessment is lacking and subjective
Solution Approach 1:
The patent introduces intermediary microservices that act as mediators between the incident management system and client experience assessment. These microservices extract communication messages from incident tickets, perform sentiment analysis, and calculate client experience indices, thereby bridging the gap between raw incident data and meaningful experience metrics with reliable quantitative output.
Solution Approach 2:
The system achieves multi-functionality by enabling the incident management system to not only track incidents but also automatically assess client experience through integrated microservices. The same infrastructure that manages incident tickets now simultaneously performs sentiment analysis, calculates experience indices, and generates reports, eliminating the need for separate assessment systems.
3Measurement precision
If quantitative assessment methods are implemented, then objective client experience data can be obtained, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the client experience assessment function into separate independent microservices: one for extracting communication messages, another for sentiment analysis, and a third for calculating client experience indices. This modular segmentation allows each service to be optimized independently while working together to achieve quantitative assessment accuracy without overwhelming system complexity.
Data Source
AI summary
A technology for assessing client experience in incident management can be implemented. The technology can fetch an event log entry from a first database comprising a plurality of event log entries generated by a client, wherein the event log entry is associated with a timestamp, an event descriptor, and a prescribed target time to close the event log entry. The technology can extract a communication message sent by the client from the event descriptor, determine a polarity score based on sentiment analysis of the communication message, determine a client experience index (CEI) based on the polarity score, save the CEI in an event record in a second database, determine an aggregated CEI based on an average of a plurality of CEIs determined for the corresponding plurality of event log entries, and output the aggregated CEI.


