Amazon has long been at the forefront of e-commerce, offering a wide variety of products and services to customers via their Web site and their Kindle device. In recent years, Amazon has developed an interesting approach to leveraging big data. Today, they are using predictive analytics to help them understand their customer’s interests, desires and needs. Here is an overview of their approach and how Amazon is leveraging big data to provide customers with a better shopping experience.
Amazon uses big data to understand customer preferences, their purchasing habits and their past purchases. By developing an online questionnaire that collects information on every purchase and where it was purchased by customers, Amazon can understand not only the frequency but also the location of the purchase. The resulting data reveals an individual consumer’s patterns of behavior, or predictability of behavior. For instance, if a customer buys a particular item from a department store twice per month, they are more likely to make purchases in the same department store every time. If that same customer purchases an item from the same department store just three times per month, they are less likely to make that purchase from another department store.
Amazon’s online auction platform, which is called the Marketplace Research Tool (MRT), also uses big data to better serve its buyers. Using MRT’s “real-time” auction-style software, sellers are able to present buyers with a range of multiple comparable items at different prices. With this information, the seller can determine the lowest price point for an item so that buyers will be highly motivated to bid. Similarly, Amazon’s voice search feature provides instant access to product information, reviews and recommendations from other shoppers. These features provide significant boosts in the efficiency with which buyers make their decisions.
This ability to respond to customer interests also makes use of big data. Amazon’s extensive e-commerce infrastructure allows the company to track and analyze various factors that make customers tick. For example, an order typically starts out as an individual transaction but is subsequently completed by a group of people who have individually entered their order into the form. The resulting collective response and feedback provided by all these parties give insight into what the specific needs of each customer are and are using this information, the personalized recommendation system can be configured to provide more relevant and useful suggestions.
Another example of how Amazon uses big data to enhance e-commerce is its use of its collaborative filtering and feedback system. In this system, one member of an Amazon wishlist can suggest alternative products to the buyer based on various criteria that the buyer has chosen. In doing so, the suggested products will be one of a kind and unique to the suggested user. Similarly, when a second user clicks on a suggested product, the second user is required to sign in to offer additional feedback about the product. Through this feature, the suggested product and other similar products in the wishlist are subjected to a more detailed analysis.
Using big data to help streamline online e-commerce and deliver personalized service has obvious advantages. For instance, it enables faster decision making on both small and large scale purchasing decisions, which is critical for improving customer satisfaction. It also offers a more holistic approach to e-commerce, which takes into account all the necessary factors in determining the efficiency of an online store from the point of view of the end users. The real-time insights provided by these recommendations enable faster implementation of plans and improved profitability, by improving supply chain management and increasing customer retention.
Amazon uses big data applications in a number of different ways, some of which have been detailed above. But how does the company go beyond offering recommendations? In many ways, one of the most striking examples of how Amazon uses big data to enhance e-commerce is its real-time replenishment services. In this setting, the actual order placement is done using data provided by customers. The reason why Amazon uses this method is that it provides better value to the customers by taking into consideration their preferences in terms of what they want to buy, when they want to buy it, and where they want to buy it from. Not only does this provide a more accurate anticipation of what they need, but it also reduces the costs associated with manual supply chain management.
While the above example highlights how Amazon uses big data to make its business more efficient, it is not the only way that the company benefits from predictive analytics. For one, it is well-known that the fulfillment center industry is highly competitive and fraught with many obstacles. This is why many companies are looking towards automation as a way to improve profitability and cut costs. RCTPA is an open source project that is designed to automate the warehousing and order fulfillment process. As such, it provides a comprehensive, real-time visibility into the supply chain and enables companies to make strategic decisions about inventory, packaging, and shipping in a cost-effective manner.