An Effective Customer Segmentation Approach to Improve E-Commerce Platform Quality Based on Intensive Learning Methodology

To accomplish company goals and reap advantages while satisfying and meeting consumer expectations, segmentation is an essential facilitator. Identifying subgroups of the target market that share common traits, requirements, and priorities is customer segmentation's primary goal. Various dataset dimensionalities are also considered, as are changes across time. In this study, we use time-based segmentation and clustering approaches to the e-commerce industry to conduct predictive neural network analysis using factors such as product reviews, goods, buying trend, watching pattern, and more. In this research, Intensive Neural Classification Learning (INCL) is presented as a novel deep learning technique. To assess its efficacy, it is cross-validated with the traditional Neural Network (NN) learning algorithm. We have evaluated unigrams, bigrams, and trigrams as part of the feature extraction process and identified the unique brands per consumer. In addition, the customer's chosen brands may be determined using a neural network, and the projected data can then be utilized for statistical analysis. We all know that sentiment analysis and categorization are huge help when trying to guess how customers feel about certain companies and items. Lastly, using the provided set of input data, we will choose the top brands by assessing the accuracy of the forecasts.