Automatic Mining of Customer Pain Points from Open Reviews: The “Review to Pain Matrix” Workflow

Abstract

This paper examines the automatic extraction of customer pain points from open reviews using the “Review to Pain Matrix” pipeline. The objective of this study is to develop a systematic approach for extracting customer pains from unstructured reviews, ensuring the reproducibility, transparency, and scalability of the analysis, while preserving the contextual usage and emotional valence of the statements. The relevance of this work is driven by the growing volume of user reviews and the need for product teams to rapidly focus on real customer issues without manual processing, which is constrained by subjectivity and the labor-intensive handling of large datasets. The novelty of the proposed “Review to Pain Matrix” workflow lies in the integration of product discovery and delivery stages within a single automated pipeline: from text cleaning and normalization, deduplication, thematization and aspect-level analysis, through to the generation of a canonical table of pain points with measurable attributes of frequency, intensity, impact on key metrics and effort required for resolution. Of particular importance is the ability to cyclically reprocess successive batches of reviews for prompt evaluation of product release effects and for updating marketing and service scripts. The outcome is a Pain Matrix: a machine- and human-readable matrix of verifiable pain-point formulations linked to audience segments and customer-journey stages, with priorities computed as a weighted combination of frequency, intensity, impact, and effort. This format enables the transformation of fragmented user feedback into a governed decision-making system, accelerates the conversion of insights into backlog items and experiments, and optimizes communication among product managers, designers, engineers, and front-office teams. This paper will be of interest to researchers and practitioners in product management, UX research, and data analytics.