NPS Analysis System Segmenting Promoters Passives Detractors
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Solution Overview
Problem
Current methods for analyzing Net Promoter Score (NPS) ratings are complex due to the continuous scale and categorization into promoters, passives, and detractors, which overlooks the passives category and struggles to identify key drivers from unstructured textual data.
Innovation Solution
A system and method that utilize hardware processors and memory modules to receive and process NPS survey responses, determining influencing factors by counting respondents, calculating key drivers, performing segment-wise response counts, calculating probabilities and impacts, and conducting sentiment analysis to prioritize key drivers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If NPS ratings are divided into three discrete categories (promoters, passives, detractors), then the continuous scale is simplified for analysis, but the passives category is completely disregarded in NPS score computation
Solution Approach 1:
The patent segments the NPS analysis into three distinct categories (promoters, passives, detractors) while maintaining separate tracking and analysis for each segment. This allows the system to preserve information from all categories including passives, while still providing simplified categorical analysis for ease of operation.
Solution Approach 2:
The patent adds a new dimension to NPS analysis by introducing segment-wise analysis that operates parallel to the traditional overall NPS calculation. This enables the system to disregard passives in the traditional score computation while simultaneously providing dedicated analysis that captures and utilizes passive respondent information through separate metrics and visualizations.
2Loss of information
If open-ended driver questions are used to collect reasons for ratings, then qualitative feedback is obtained, but the unstructured textual data makes it challenging to identify key drivers
Solution Approach 1:
The patent replaces manual analysis of unstructured textual data with automated text processing and natural language processing algorithms. This substitution transforms the complex mechanical task of manually coding and categorizing open-ended responses into an automated computational process that can efficiently identify key drivers, themes, and patterns in the qualitative feedback.
Solution Approach 2:
The patent transforms unstructured textual data into structured parameters by extracting key drivers, sentiment scores, and thematic categories from open-ended responses. This parameter transformation converts qualitative text into quantifiable metrics that can be systematically analyzed, tracked, and integrated with the NPS scoring system.
3Measurement precision
If segment-wise analysis is performed for each NPS category, then accurate identification of key drivers is achieved, but the analysis complexity increases
Solution Approach 1:
The patent implements a universal analysis framework that processes all three NPS segments (promoters, passives, detractors) through the same text analysis pipeline and key driver identification algorithms. This multi-functional approach maintains measurement precision by applying consistent analytical methods across segments while reducing overall complexity through standardization and reusability of analytical components.
Solution Approach 2:
The patent merges the segment-wise analysis results into a unified key driver identification process. By combining the analytical workflows for promoters, passives, and detractors into a single integrated system that shares common processing logic and data structures, the patent achieves accurate segment-specific insights while managing analysis complexity through consolidation.
Data Source
AI summary
A system for determining influencing factors of net promoter score (100). The system (100) comprise of plurality of modules (126) that include a data acquisition module (127) for receiving a response to a net promoter score survey from a survey respondent, a data processing module (128) for determining one or more influencing factors of net promoter score and the data processing module (128) is configured to determine a count of respondents, promoters, passives and detractors, determine a count of the responses, determine one or more key drivers in each of the responses, perform segment-wise response counts for each key driver, calculate segment-wise probabilities and an impact for each key driver, evaluate a segment-wise prioritization of key drivers, determine an overall impact and importance of each of the key drivers through a plurality of weighted averages, perform a sentiment analysis to determine a sentiment score and impact of each of the key drivers, determine the sentiment-based prioritization of key drivers, identify a set of business drivers from the key drivers and determine the business prioritization for the key drivers.