Segment Analysis System for Survey Influence Scoring
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Solution Overview
Problem
Current systems for analyzing customer survey data lack the ability to accurately identify which questions have the greatest influence on aggregate scored metrics, as they do not account for the number of responses and do not allow for cross-question/response combination comparisons, leading to incomplete insights into customer satisfaction and product feedback.
Innovation Solution
A segment analysis system that processes survey score data to generate influence scores for each question/response combination, allowing for the identification of questions with the most impact on aggregate scored metrics by standardizing statistical analysis and enabling user-defined filtering to customize the analysis based on specific criteria such as demographic information and purpose.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional systems only provide data metric information and scores without normalization, then the system complexity is reduced, but the ability to accurately determine question influence on scored metrics deteriorates
Solution Approach 1:
The patent transforms raw survey data into normalized influence scores by changing the parameter representation. Each question/response combination is converted from simple score counts to standardized influence scores that account for response distribution across all questions, enabling accurate comparison while maintaining manageable system complexity through automated normalization calculations
Solution Approach 2:
The patent segments the survey analysis into distinct components: individual question/response combination scores, response counts, and normalized influence scores. This segmentation allows each element to be calculated and analyzed separately, improving measurement precision while keeping the overall system complexity manageable through modular processing
2Loss of information
If traditional systems analyze each question/response combination without normalization, then the processing time is reduced, but the ability to compare across different questions deteriorates
Solution Approach 1:
The patent applies parameter transformation by converting raw score counts into normalized influence scores that enable cross-question comparison. The normalization process standardizes different question scales to a common metric, preserving cross-question comparison capability while using efficient algorithms to minimize processing time overhead
3Measurement precision
If traditional systems do not account for the number of responses, then the analysis simplicity is maintained, but the accuracy of impact indication deteriorates
Solution Approach 1:
The patent incorporates response count as a critical parameter in the influence score calculation. By changing the analysis parameter from simple mean score comparison to a formula that includes both score values and response counts, the system achieves accurate impact indication while maintaining analysis method simplicity through a unified influence score metric
Data Source
AI summary
The segment analysis system analyzes survey data to determine the influence each custom question/response combination (segment) has on a given aggregate scored survey metric for a given date/date range. The system removes from consideration all surveys that do not include a scored survey metric and date that matche the aggregate scored survey metric and given date/date range. The system further removes from consideration all surveys not pertaining received user-defined filtering. Once the system has eliminated all extraneous surveys from consideration, the system segments each question/response combinations across the pool of surveys to generate an influence score for each question/response combination. The system identifies which segment has the greatest positive and negative influence on the aggregate scored survey metric for the given date/date range. The system generates reports for the segment analysis and stores all segment analyses for further comparative analysis.