Calculating effective product marketing on e-commerce applications based on customer rating using big data

Abstract

While purchasing online products, our priority is to consider online rating regarding the product. Based on the customer rating of products it can be possible to determine their lifetime, sales and that impacts the ability to be maintained at a certain rate or level of a product in the market. The rating is considered as datasets where they are being extracted from E-Commerce websites. In specific, consider the review content, product ratings and divide product lifetime. While collecting the relevant information from our review data we consider the data into two categories as positive data and negative data. When a user posted a review, we consider the keywords to state the review was good or bad and their rating behaviors, these extracted scores can be correlated with their rating with product popularity. The product popularity can be considered by the total number of purchases of the product and the rating given to the product. It also can be analyzed by product ratings that indicate that raters’ ratings are likely to influence product popularity. Taking different e-commerce datasets to extract review content and obtaining relevant information from the review data can analyze and predict the product's early raters and product marketing.