Next-Gen Retail: NLP-Enhanced Personalized Shopping & Dynamic Customer Feedback Tracking

Abstract

This research examine the integration of Natural Language Processing (NLP) techniques in esthetic personalized shopping experiences and tracking customer feedback. The hawking terrain undergoes with a consequential alteration due to the advent of online shopping, necessitating the need for an advanced appliance to personalize customer experiences and comprehend their feedback effectively. NLP offers a promising avenue to achieve these objectives by enabling retailers to understand and respond to customers' preferences, sentiments, and feedback in real time. The main objective of this paper is based on two techniques i.e. to perform sentiment analysis on customer reviews for an e-commerce platform (it focuses on the unearth client requisite and proclivity concerning online products & services) Secondly, The goal was to develop machine learning models that could accurately classify reviews as positive, negative, or neutral i.e. through Machine Learning Classifiers, it monitors or checks the trends & behavior of customers choices, preferences, purchasing pattern i.e. it generates the review (frequencies) of purchase & moderate amount spent over a categorical era. It then further, investigates by applying various NLP methodologies, including sentiment analysis, on those reviews to illustrate how they contribute to refining personalized shopping experiences as (Positive, Negative or Neutral) & improving feedback management. Through comprehensive analysis, Empirical Data and different Case Studies, this paper aims to elucidate the significance & potential of NLP Applications in improving customer satisfaction, loyalty, and operational efficiency.