Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Abstract

Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.