DefinePK

Abstract

Today’s generation enjoys making purchases online. The online market is expanding quickly. The competition to sell items gets more intense as more stores enter this market. On the other side, these marketplaces are giving their clients convenient alternatives and earning their confidence. Customers are far smarter now; they research and weigh their alternatives before making a purchase. While some customers are frequent shoppers, others are still apprehensive about making purchases online. Because internet shopping has so many drawbacks, people are becoming increasingly aware of the need for a system that can properly analyze existing customers in order to attract new clients and business. One creative method used in this study to gauge consumer loyalty to a product is to enable new customers and news consumers make decisions more quickly. Our method uses a novel idea to assess a buyer's loyalty to a specific brand or product, and it might help a new consumer decide on a particular item based on its numerous features and previous customer reviews. In order to measure customer loyalty in our suggested model, we employed an artificial neural network (ANN) technique. To do this, we took a big data set from Kaggle that was based on customer evaluations of online products. The reviews' textual and non-textual information is extracted via POS tagging, which then pre-processes and turns the textual data into tokens. The suggested ANN method uses pre-processed and mapped reviews to create vectors. The proposed ANN model receives the highlighted dataset for training. The proposed method is tested using additional sample data after training. The trained dataset is utilized to predict loyalty. In along with adding to the body of knowledge on predicting consumer loyalty, this study is expected to offer e-commerce managers insights for pursuing customer loyalty.