AI-Enabled Product Analytics For Customer-Centric Feature Prioritization and Product Development

Abstract

The increasing availability of customer-generated online reviews provides organizations with valuable opportunities to apply artificial intelligence (AI) and natural language processing (NLP) to understand customer requirements and support product-development decisions. However, the unstructured nature of review content requires systematic analytical approaches to identify meaningful product characteristics, customer concerns, and recurring patterns. This study aimed to analyze customer-generated product reviews using AI-oriented and NLP-based analytical approaches to identify patterns relevant to customer-centric product analytics and establish a data-driven basis for prioritizing customer-relevant product features. The study analyzed the Turkish User Reviews dataset obtained from the UCI Machine Learning Repository. The raw dataset.txt file was organized according to eight product categories: Computer, Tea Machine, Headphones, Modem, Perfume, Mobile Phone, Television, and USB. Non-empty customer reviews were retained for analysis. Review distribution, textual characteristics, and record completeness were examined to establish a descriptive foundation for subsequent customer-centric product analytics. The analysis revealed differences in customer-review volume and textual characteristics across the product categories. Customer reviews varied in their length and level of textual detail, indicating differences in how customers communicate their product experiences. The assessment also demonstrated a high level of record completeness, providing a consistent foundation for further AI-enabled analysis. These findings support the use of customer-generated reviews as a structured source of product-related information. Customer reviews provide a valuable foundation for AI-enabled product analytics and customer-centric feature prioritization. Systematic analysis of review content can support the identification of customer concerns and product characteristics and can contribute to evidence-based product-development decisions.