Automated Customer Discrepancy Resolution System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current customer service systems lack the ability to autonomously determine and resolve customer-perceived discrepancies during automated interactions, leading to friction and inefficiency in addressing customer concerns.
Innovation Solution
A computer-implemented method and system that utilizes Natural Language Processing (NLP) and a discrepancy determination device to extract perceived and expected states from customer communications, verify discrepancies, and generate personalized responses to confirm, correct, or explain the current state of customer information, thereby resolving the discrepancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated customer service systems are used to handle customer inquiries, then productivity and efficiency are improved, but the ability to autonomously determine and resolve customer-perceived discrepancies is lacking
Solution Approach 1:
The system segments the customer service process into distinct functional modules: NLP processing module for extracting states, discrepancy determination module for comparing states, verification module for validating discrepancies, and response generation module for formulating answers. This segmentation enables each module to specialize in specific tasks, achieving autonomous resolution of customer-perceived discrepancies while maintaining high productivity in automated customer service interactions.
2Reliability
If human customer service agents manually investigate and explain discrepancies, then accuracy and customer satisfaction are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system enables self-service by automatically performing the entire discrepancy resolution process without human intervention. The NLP module extracts perceived and expected states from customer communications, the discrepancy determination module identifies mismatches, the verification module validates findings against customer information, and the response generation module provides personalized explanations. This self-service approach maintains high accuracy while dramatically reducing time consumption compared to manual human investigation.
3Ease of operation
If personalized explanations are provided to customers, then customer satisfaction is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system incorporates feedback mechanisms where the NLP module continuously monitors customer communications to extract perceived and expected states, the discrepancy determination module uses this feedback to identify mismatches, and the response generation module adjusts explanations based on the specific discrepancy found. This feedback-driven approach enables personalized explanations tailored to each customer's situation while managing system complexity through structured processing of feedback loops.
Data Source
AI summary
Systems and methods are provided herein for autonomously determining and resolving a customer's perceived discrepancy during a customer service interaction. The method can include receiving an incoming communication from a customer; extracting a perceived state and an expected state (possibly of a product or service) based on the incoming communication; determining a perceived discrepancy between the perceived and expected states of the customer; retrieving customer information; extracting a current state of the customer from the retrieved customer information, verifying, by a rule-based platform, the discrepancy; generating a response based on the discrepancy after comparing the perceived stated with the current state, where the response may include a confirmation or a correction related to the discrepancy and a personalized explanation describing the current state of the customer; and outputting, for presentation to the customer, the response.