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ReviewSense: Transforming Customer Review Dynamics into Actionable Business Insights

Siddhartha Krothapalli, Tridib Kumar Das, Praveen Kumar, Naveen Suravarpu, Pratik Narang

TL;DR

ReviewSense addresses the need to convert unstructured customer reviews into prescriptive, business-facing guidance rather than simple preference predictions. It combines SBERT-based clustering with domain-adapted large language models (LLMs) fine-tuned via LoRA and 4-bit quantization, plus expert evaluation to generate targeted recommendations. The framework integrates data collection, privacy-preserving preprocessing, iterative clustering, and a dedicated prompting scheme to produce actionable insights across industries. This approach has practical potential to guide strategic decisions, improve customer-centric operations, and enhance loyalty by turning feedback into concrete actions, though standardized evaluation metrics and scalability remain areas for future work.

Abstract

As customer feedback becomes increasingly central to strategic growth, the ability to derive actionable insights from unstructured reviews is essential. While traditional AI-driven systems excel at predicting user preferences, far less work has focused on transforming customer reviews into prescriptive, business-facing recommendations. This paper introduces ReviewSense, a novel prescriptive decision support framework that leverages advanced large language models (LLMs) to transform customer reviews into targeted, actionable business recommendations. By identifying key trends, recurring issues, and specific concerns within customer sentiments, ReviewSense extends beyond preference-based systems to provide businesses with deeper insights for sustaining growth and enhancing customer loyalty. The novelty of this work lies in integrating clustering, LLM adaptation, and expert-driven evaluation into a unified, business-facing pipeline. Preliminary manual evaluations indicate strong alignment between the model's recommendations and business objectives, highlighting its potential for driving data-informed decision-making. This framework offers a new perspective on AI-driven sentiment analysis, demonstrating its value in refining business strategies and maximizing the impact of customer feedback.

ReviewSense: Transforming Customer Review Dynamics into Actionable Business Insights

TL;DR

ReviewSense addresses the need to convert unstructured customer reviews into prescriptive, business-facing guidance rather than simple preference predictions. It combines SBERT-based clustering with domain-adapted large language models (LLMs) fine-tuned via LoRA and 4-bit quantization, plus expert evaluation to generate targeted recommendations. The framework integrates data collection, privacy-preserving preprocessing, iterative clustering, and a dedicated prompting scheme to produce actionable insights across industries. This approach has practical potential to guide strategic decisions, improve customer-centric operations, and enhance loyalty by turning feedback into concrete actions, though standardized evaluation metrics and scalability remain areas for future work.

Abstract

As customer feedback becomes increasingly central to strategic growth, the ability to derive actionable insights from unstructured reviews is essential. While traditional AI-driven systems excel at predicting user preferences, far less work has focused on transforming customer reviews into prescriptive, business-facing recommendations. This paper introduces ReviewSense, a novel prescriptive decision support framework that leverages advanced large language models (LLMs) to transform customer reviews into targeted, actionable business recommendations. By identifying key trends, recurring issues, and specific concerns within customer sentiments, ReviewSense extends beyond preference-based systems to provide businesses with deeper insights for sustaining growth and enhancing customer loyalty. The novelty of this work lies in integrating clustering, LLM adaptation, and expert-driven evaluation into a unified, business-facing pipeline. Preliminary manual evaluations indicate strong alignment between the model's recommendations and business objectives, highlighting its potential for driving data-informed decision-making. This framework offers a new perspective on AI-driven sentiment analysis, demonstrating its value in refining business strategies and maximizing the impact of customer feedback.
Paper Structure (30 sections, 1 figure, 4 tables)

This paper contains 30 sections, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Literature Survey
  3. Methodology
  4. Clustering
  5. Generating embeddings with BERT models
  6. Pre-Processing and Clustering Algorithm
  7. Language Model - LLAMA-3 8B Instruct
  8. Tokenizer and Model Initialization
  9. Prompt Tokenization
  10. Training Process
  11. Response Generation
  12. Dataset and Lack of Evaluation Metrics
  13. Data Collection
  14. Data Pre-processing
  15. Data Selection
  16. ...and 15 more sections

Figures (1)

  • Figure 1: Customer Feedback Analysis Pipeline