How does a retailer like Amazon use Big Data to gain competitive advantage?
There are many ways that big data can be used to create value across sectors of the global economy. Indeed, that we are on the cusp of a tremendous wave of innovation, productivity, and growth, as well as new modes of competition and value capture. Big Data is only useful when it is used to extract meaningful information. As in the retail business, the more information you can retrieve about customers, the more predictable and accurate their shopping habits become. For an example, a person who has been thinking about purchasing a new Panasonic LED HDTV and has been comparing prices with Samsung HDTV prices on Amazon.com. Then the next day, he gets an Email from Amazon.com with an exclusive offer on all HD Samsung and Panasonic TV’s. In addition, when he revisits the Amazon’s website and he notices several other branded HDTVS under “items that you might be interested in.” Confident that he has found the best price and satisfied with the convenience, he makes his purchase from Amazon. This scenario is an example of Big Data being used effectively by the major online retailer, Amazon.com. It’s not just a coincidence when Amazon recommends a product to you that you’ve been interested in purchasing. Amazon has generated 29% of sales through their recommendations engine, which suggests popular products to specific customers (sqreamtech.com). By analyzing customer data coming from 152 million+ accounts, Amazon has figured out the real secret behind sales success – Big Data. Amazon uses Big Data analytics to determine what a customer has placed inside their virtual shopping cart and which items they’ve recently viewed and purchased in the past. Amazon calls this technique, “item-to-item collaborative filtering,” a method which uses structured and unstructured data sources to customize a returning customers’ shopping experience (sqreamtech.com). This provides Amazon shoppers with an easier shopping experience due to a type of, “virtual customer service.” Within seconds of entering Amazon.com, consumers are presented with merchandise options that they are considering purchasing. Here are just a few business solutions which retailers have gained from utilizing Big Data: Ensuring a personal shopping experience: By examining a customers’ purchase history, clicks generated from online sites, likes via social networks, geospatial services and other behavior patterns, retailers can now create real-time targeted promotions which can be broadcasted directly to customers’ smart phones while they shop allowing consumers to find exactly what they are looking for upon entering a store. More effective merchandising: A thirty-year old pregnant woman walks into a San Francisco Target store and finds baby diapers displayed at the store entrance. How can this be? Thanks to data coming from online sources, retailers can now pinpoint which merchandise should be stocked at specific locations and where items should be placed throughout the store. Customer loyalty: Retailers, which cater to their customers’ needs, are seeing an increase in returning cliental. Customers are looking for the easiest and most convenient way to shop and big data allows retailers to understand their customers’ needs before they even enter a store or a online storefront. Inventory Management: big data, which is being, used in near real-time enables retailers with the capability to predict when merchandise should be restocked and which shelves should be equipped with certain products. This optimizes inventory and prevents stock-out incidents (Saracino).

So how does Amazon’s the largest online retailer use big data analytics in order to improve their sales? Let’s assume Amazon needs to predict a newest Samsung LED HDTV this season. Big Data analytics combines the enterprise data with other relevant information such as, web browsing patterns, social media sentiment, movie releases, TV industry