Decision Model for Customer Satisfaction Prediction
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Solution Overview
Problem
Existing customer satisfaction analysis and prediction systems are inefficient in providing effective strategies to improve customer experience, as they are primarily software-driven and digitalized, failing to realize significant improvements in decision-making information and are unable to account for the stochasticity and long-term mechanisms of customer responses.
Innovation Solution
A decision-making solution utilizing a trained decision model that determines target decisions based on attribute information and current perception categories, incorporating a causal model to predict perception category transitions and reward functions, enabling more accurate and long-term customer experience management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional questionnaire-based satisfaction surveys are used, then customer satisfaction can be evaluated, but the process is time-consuming and laborious
Solution Approach 1:
The patent replaces the mechanical questionnaire-based survey system with an automated machine learning system that processes customer interaction data (calls, messages, transactions) to automatically evaluate satisfaction levels, eliminating manual survey administration while maintaining measurement capability
Solution Approach 2:
The system enables self-service evaluation by automatically collecting and analyzing customer interaction data without requiring active customer participation in surveys, allowing the system to autonomously generate satisfaction metrics from existing operational data
2Measurement precision
If traditional satisfaction surveys are used, then immediate satisfaction can be measured, but long-term satisfaction maintenance strategies cannot be provided
Solution Approach 1:
The patent implements dynamic decision models that adapt to changing customer states and contexts, enabling the system to provide evolving strategic recommendations for both immediate and long-term satisfaction management rather than static survey results
Solution Approach 2:
The system incorporates feedback loops where machine learning models continuously learn from customer interaction outcomes and satisfaction measurements, enabling iterative improvement of strategic recommendations for long-term satisfaction maintenance
3Productivity
If software-driven digitalized systems are used for customer experience management, then data processing efficiency is improved, but the systems fail to account for stochasticity and long-term mechanisms of customer responses
Solution Approach 1:
The patent changes the fundamental parameters of the system by transitioning from deterministic software algorithms to probabilistic machine learning models that incorporate stochasticity, allowing the system to capture the inherent randomness in customer responses while maintaining computational efficiency
Data Source
AI summary
Embodiments of the present disclosure relate to an information processing method and an electronic device and relate to a computer field. The method comprises: obtaining input information from a user, the input information at least indicating at least one of: attribute information of at least one target object, or information of a current perception category of the at least one target object; determining a target decision for the at least one target object based on the input information using a trained decision model; and outputting the target decision. In this way, the embodiments of the present disclosure can output a target decision corresponding to the input information of the user based on the trained decision model, so as to provide a reference to the user for decision making and facilitate the user to maintain the perception category of the target object.