REAL-TIME NET PROMOTER SCORE PREDICTION USING MULTI-MODAL AI AND BEHAVIORAL ANALYTICS
DOI:
https://doi.org/10.34218/IJCET_17_01_002Keywords:
Net Promoter Score (NPS), Dynamic NPS Prediction, Artificial Intelligence, Multi-Modal Data Fusion, Predictive Analytics, Machine Learning, Customer Satisfaction, Sentiment Analysis, Telemetry Data, Survey, Real-Time Customer Experience (CX), Behavioral Analytics, Telemetry Driven Monitoring, Large Language Model (LLM) Embeddings, Hybrid AI Ensemble, Root Cause AnalysisAbstract
The Net Promoter Score (NPS) is a widely adopted metric that many companies use to measure customer satisfaction and loyalty. In NPS, respondents are divided into Promoters, Passives, and Detractors, and it is one of the key performance indicators (KPI) for businesses. This survey is straightforward to understand, providing a benchmark against competitors and industry averages. Companies rely on this survey because it appears to directly connect to business growth, which catches people’s attention. Sending out surveys, such as post-launch surveys and product reviews, every few weeks or months is an inefficient method that presents several issues. By the time companies review the survey results, it’s often too late to implement changes to collect the right data. Companies are shifting towards gathering information from various sources, such as user activity on the platform, system logs, and actual comments or user feedback. Large language models analyze vast amounts of textual data, while machine learning algorithms identify trends in structured data, together enhancing the accuracy of real-time NPS estimates. All this analysis comes together to give an almost real-time NPS estimate, offering teams insights they can quickly act on. This method can sometimes be inaccurate due to predictive models misinterpreting signals, insufficient data, which may lead to confusing positive feedback with negative, due to nuances in language and context.
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Copyright (c) 2026 Abhinay Kumar Reddy Seella (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
