Peruvian e-commerce product-matching

The rise of e-commerce in Latin America has been driven by the digital presence of the younger generations and the adaptation of retail businesses to online sales channels. The COVID-19 pandemic has further accelerated this shift, forcing businesses to enhance their online commerce strategies. Peru has witnessed a notable 131\% increase in online shoppers from 2019 to 2021. However, the absence of a unique global code for product identification negatively affects the Zero Moment of Truth (ZMOT) in customer decision-making. To address this, a novel approach is proposed that uses machine learning based classification models along with natural language processing and image analysis techniques is proposed. This implementation aims to facilitate product matching across online retail stores in Peru, allowing companies to gain insight into their competitors, regulate prices, and improve the shopping experience for both consumers and retailers. Reaching a 92.7\% of accuracy and 93.6\% of F1 score with hyperparameter optimization, laying the foundation for further investigation of product matching in the Peruvian online retail sector.