True NPS Score Calculation via Sentiment Analysis
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Solution Overview
Problem
The standard Net Promoter Score (NPS) methodology does not analyze free-text responses, resulting in overlooked valuable customer feedback and a lack of standardized methods to estimate the impact of customer sentiment on NPS, limiting organizations' ability to understand customer loyalty and satisfaction effectively.
Innovation Solution
A system and method that includes a data processing module to receive NPS survey responses, classify respondents as promoters, passives, or detractors, analyze sentiment in free-text responses, and calculate weighted averages to estimate influencing metrics and impact of customer sentiment on NPS, providing a True NPS score with varying weightages to sentiment and rating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard NPS methodology is used to calculate NPS score, then the calculation is simple and fast, but valuable customer feedback from free-text responses is overlooked
Solution Approach 1:
The patent segments the NPS analysis into multiple dimensions: quantitative rating analysis and qualitative sentiment analysis. By dividing the feedback processing into separate analytical streams, the system preserves calculation efficiency while extracting valuable information from free-text responses through sentiment analysis.
Solution Approach 2:
The patent merges quantitative NPS ratings with qualitative sentiment analysis results to create a comprehensive customer feedback assessment. This combination allows organizations to maintain the simplicity of standard NPS calculation while enriching it with insights from text analysis, thereby reducing information loss.
2Measurement precision
If sentiment analysis is incorporated into NPS methodology, then understanding of customer loyalty is enhanced, but the complexity of the system increases
Solution Approach 1:
The patent introduces sentiment analysis as an intermediary layer between raw NPS responses and final loyalty metrics. This mediator processes free-text responses to extract sentiment signals, which then inform the overall NPS interpretation, enhancing measurement precision without requiring complete system redesign.
Solution Approach 2:
The patent applies sentiment analysis selectively to enhance specific aspects of NPS measurement rather than completely transforming the methodology. By applying sentiment analysis to free-text responses while maintaining the original rating-scale framework, the system improves loyalty measurement precision without proportionally increasing overall complexity.
3Loss of information
If free-text responses are analyzed manually, then detailed insights can be obtained, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual text analysis (mechanical human processing) with automated sentiment analysis algorithms. This substitution maintains the ability to extract detailed customer insights from free-text responses while dramatically reducing the time required for analysis, as computational processing is far more efficient than human reading and interpretation.
4Ease of operation
If individual rating scales are normalized, then comparison between customers becomes easier, but the individual understanding and context are lost
Solution Approach 1:
The patent applies different analytical approaches to different parts of the feedback data. Quantitative ratings are processed with normalization for comparison purposes, while qualitative free-text responses are analyzed with sentiment analysis that preserves individual context and nuance. This localized quality approach allows both comparison ease and context retention.
Data Source
AI summary
The present invention relates to a system (100) for determining actionable brand equity, comprise of a data acquisition module (127) and a data processing module (128) integrated with a brand presence assessment module (1281), a brand experience assessment module (1282), a brand edge assessment module (1283), and a brand preference assessment module (1284). The brand presence assessment module (1281) captures top-of-mind awareness responses, the brand experience assessment module (1282) utilizes net promoter scores to rank brands based on respondent experiences, the brand edge assessment module assesses brand differentiators, while the brand preference assessment module (1283), gauges brand likability. Each of the modules generate a module-level score and grade to be used by a concerned authority to produce a set of actions for improving brand presence, brand experience, brand edge and brand presence. A composite grading module combines presence, experience, edge, and preference scores to offer an actionable brand equity grade.