User Persona Data Evaluation via Distribution Matching
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Solution Overview
Problem
Existing methods for evaluating user persona data lack accuracy and representativeness, as they fail to effectively match distribution information across different attribute dimensions, leading to inconsistent and less reliable results.
Innovation Solution
A method and system that acquire distribution information in a first attribute dimension from user persona data, extract evaluation data from a sample data set with consistent distribution, and determine the accuracy of persona data in a second attribute dimension using evaluation data, ensuring representativeness and accuracy through layered sampling and coordinated sampling techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional off-line member management and questionnaire investigation methods are used, then the process is simple and easy to operate, but the accuracy and representativeness of user persona data evaluation are insufficient
Solution Approach 1:
The patent introduces an intermediary evaluation mechanism that bridges persona data and sample data through distribution information matching. The system uses evaluation data collected from sample users as a mediator to verify and evaluate the accuracy of persona data, rather than directly comparing persona data against ground truth. This intermediary approach enables accurate evaluation while maintaining system manageability.
Solution Approach 2:
The patent replaces conventional mechanical data collection and verification methods with automated data mining and statistical analysis techniques. By using mega-data technology and automated algorithms to match distribution information across attribute dimensions, the system achieves high measurement precision without requiring complex manual evaluation processes.
2Reliability
If persona data is collected from all users, then the data volume is large and comprehensive, but the representativeness for evaluation purposes deteriorates due to data bias and complexity
Solution Approach 1:
The patent extracts and isolates evaluation data from the broader persona data collection. By separating evaluation data into a distinct subset that can be independently analyzed and matched against persona data distribution, the system maintains reliability while managing data volume. The extraction process ensures that evaluation data represents the target population accurately without requiring processing of all user data.
Solution Approach 2:
The patent applies local quality by ensuring that evaluation data has specific representativeness properties tailored to the evaluation purpose. Rather than requiring all data to be uniformly representative, the system ensures that the evaluation data subset possesses the necessary representativeness characteristics for accurate persona validation, while other data can be processed differently.
3Measurement precision
If distribution information matching is performed across multiple attribute dimensions, then the accuracy of evaluation improves, but the complexity of data processing increases
Solution Approach 1:
The patent segments the multi-dimensional attribute space into distinct dimensions for independent analysis. By dividing the complex task of matching distribution information across multiple attributes into separate, manageable segments, the system can process each dimension independently and combine results, reducing overall processing complexity while maintaining comprehensive evaluation accuracy.
Data Source
AI summary
A method and an apparatus are provided. Distribution information in a first attribute dimension is acquired from a persona data collection of users who use a network service. The persona data collection includes persona data sets corresponding to the users. An evaluation data collection to match the distribution information in the first attribute dimension is extracted from a sample data collection that is collected from sample users. The sample data collection includes sample data sets corresponding to the sample users. Further, a level of accuracy of the persona data collection in a second attribute dimension is determined based on the evaluation data collection in the second attribute dimension.