How Amazon Uses Big Data for E-Commerce Websites

how amazon uses big data

How Amazon Uses Big Data for E-Commerce Websites

If you are new to online business, perhaps “you don’t know how Amazon uses big data” would ring true to you. The common knowledge in the business world is that Amazon uses big data for various factors. However, here are some ways Amazon applies big data to improve efficiency, cut costs, make better decisions and reach more consumers and earn more profit.

When we talk about Amazon’s success, one factor that jumps out is their reliance on data to understand their customers’ needs. In fact, Amazon Web Services actually implements its own proprietary predictive analytics technology based on over two hundred data points from all sorts of external sources. Each source has a different focus – supply chain for instance, customer behavior, and market segment, to name a few. In each vertical of the supply chain, there is a distinct set of metrics that are critical to the very core of the organization. When used together, they form a tightly tuned, synchronized system that helps make business decisions, cut operating costs, increase profits, and expand into new areas. As such, the bottom line is, Amazon uses predictive analytics to understand both the demand and supply situation of their customers, which greatly reduces the time spent analyzing customer’s responses and improves customer service.

Amazon uses big data to make use of time, price, quality and location in formulating its policies and its solutions. For example, they have developed an e-commerce platform that allows users to shop for the items they want from their homes without having to go through the hassles of visiting a store, finding a parking space, standing in lines, queuing for payment and so on. Online shopping is made easy by using special software called “awsperprise” which uses natural language processing, web optimization, visual recognition technology, and other advanced signal processing technologies to process real-time customer data and provide the users with what they are looking for, when they are looking for it. Amazon also makes use of” Mechanical Turk” to perform “auction style” online shopping. This method of retail shopping requires consumers to browse through thousands of products, making use of all the necessary search tools, making use of the “Mechanical Turk” to rate and review the products, and lastly, paying for their product using their credit cards.

The second most important use of Amazon Web Services is its e-commerce capability. It uses dynamic pricing that changes according to supply and demand. Amazon uses two types of pricing models: General discounted prices and Special variable pricing. Its General discounted price model makes use of supply and demand algorithms to determine the price of a product based on the demand and supply factors. Special variable pricing is based on certain variables that change according to changing priorities of the customer.

Amazon Web Services also makes use of its e-commerce tools, which allow the customer to complete transactions such as changing their shipping address, checking out, rating and reviewing product details, and is requesting a catalog of items. These tools make the process of online shopping easy for customers. The e-commerce solutions provided by Amazon Web Services also include tools for web analytics, where the customer data is analyzed for improvements and for improvement in future business. Amazon Web Services’ recommendation engine also plays an important role in helping online shopping.

In addition to these activities, Amazon Web Services also makes use of its own in-house predictive analytics tool, called Kinesiology. Its predictive analytics tool uses the data that is collected every day by the users of Amazon’s web services. This data is used to analyze the purchasing habits of the users of the web services. The data is collected for two purposes: One is to predict the demand that users might have in the near future and the other is to forecast the trends that might be occurring in the future so that the web services can make good use of the data it has and provide better services to the users. The Kinesiology tool can be used to predict the demand in the near future as well as the demand that users may have in the coming months and years. These predictions are made based on factors such as product prices, demographics, patterns in the past purchase, and so on.

Recommendation Engine Marketing: The data that is collected and analyzed by Amazon Web Services for providing its web services also makes use of its own in-house Recommendation Engine. The Recommendation Engine allows the users to create personalized lists of e-mail addresses, websites, or blogs that they want to visit and get more information from. This is one of the factors that make using Amazon Web Services for e-commerce such a success. The Recommendation Engine makes it easy for consumers to find products, services, and other pieces of information that they may need through a 360-degree view.

Shipping Address Geocoding: This is another factor that makes Amazon’s Choice Solutions an efficient service. It provides its clients with accurate mapping of their shipping address using public and proprietary databases such as Zagat, Mercator, Yelp, and others. The maps are provided to the clients through a user-friendly interface. This allows clients to gain a complete picture of where their packages are currently located at all times. This also enables the client to find their packages and track the status of their orders using real-time updates.