Amazon, a digital commerce company founded in 1994, has grown exponentially since its initial public offering in 1997, and, as it continues to dominate competition and penetrate numerous industries, Amazon gives the impression that its unprecedented growth will not cease. As its stock price, sales, and e-commerce market share consistently show drastic increases, Amazon continues to invest, innovate, and change the world (nasdaq.com). However, in order to continue to proliferate sales, Amazon has deployed various strategies to engage and satisfy customers and persuade them to purchase specifically-targeted products. The one vital aspect that distinguishes Amazon from its competition is its consistent application of big data and predictive analytics. Amazon collects and stores its users’ data and information in order to determine how its consumers spend their money, and they analyze this data and information for target marketing. Yet, Amazon is only one example of the multitude of companies that have recently applied big data to reach a greater insight by learning more about their customers, to enhance decision-making, make marketing more effective, and, ultimately, to increase revenue. And as the application of big data becomes more and more prevalent, it continues to bolster and stimulate U.S. economic growth. According to a report by the National Institute of Standards and Technologies (NIST), (https://bigdatawg.nist.gov) big data is defined as the presence of extremely voluminous data sets that are analyzed to reveal trends and patterns relating to behavior and interactions. The NIST report also stated that companies and organizations derive their masses of data from an abundance of different sources including smartphones, social media, sensors, computer servers, and the internet. As big data analytics is advanced and increasings amounts of data are gathered, the opportunities and possibilities continue to grow, especially for the U.S. economy. Similar to Amazon, many large and small businesses have applied big data because it provides numerous competitive advantages. First, companies can optimize big data as a strategic asset. Big data analysis reveals information that allows companies to enhance and accelerate decision-making because, by using big data analytics to quickly identify trends and patterns, companies can adjust quickly to target specific goods or services in particular locations for specific customers at particular times. The key to marketing is understanding the consumer. With big data analysis, companies can analyze consumer behavior, which allows them to alter their marketing strategies and operations to tailor to certain demands and customer interests. Big data is a valuable strategic asset because the intelligence and insight it reveals leads to better real-time decisions, so companies can instantly increase profits. Understanding consumer behavior with big data also allows companies to build loyalty and optimize marketing performance. In addition, according to a recent survey conducted by BARC, (http://barc-research.com) big data analytics enables cost and time reduction and an increase in productivity, which allows for new product development and innovation because companies have more money and time to invest. For example, IBM Watson Analytics, an advanced data analysis service, converts data to information efficiently, which allows for greater and quicker insights and better real-time business decisions (https://www.ibm.com/watson-analytics). With quick data analysis, such as that enabled by Watson, companies can increase productivity, cut costs, and raise savings, which will increase their overall value and earnings. Another economic impact of big data is demonstrated in the sales and marketing industry. Sales and marketing entails putting products or services into the hands of a customer in the most profitable way. Big data analysis is a crucial tool for sales and marketing because by understanding their customers’ behavior and interactions, companies can improve customer engagement and loyalty and optimize marketing performance. As previously mentioned, big data analysis facilitates customer analytics and target marketing, which allows companies to increase revenue by tailoring goods and services to certain demographics. In addition to more advanced customer analytics, some other impacts of big data in sales and marketing include: more efficient product development, pricing optimization, increased operational efficiency, greater customer loyalty, and search engine optimization (SEO). First, leveraging big data shows companies what products to create and what products to modify or advance, which leads to an upsurge in production efficiency (http://ide.mit.edu). The purpose of big data analysis in sales and marketing is to determine what products and services engage and satisfy specific consumers and market niches. When developing new products, companies can use this information to create products that connect with consumers or develop existing products based on consumer activity and behavior. Products developed with the application of big data are more “well informed” because they are deliberately designed to display a greater appeal to consumers. Also, companies can allocate resources more efficiently and purchase materials more economically if they have more information about planning, producing, and launching new products. Second, big data analysis can lead to pricing optimization. Pricing optimization refers to setting the best prices – the maximum price that customers would be willing to pay, – and it is critically important because prices are a major determinant of a company’s profits. Big data analysis facilitates finding the best prices. Uber, a highly successful global taxi technology company, uses its pricing algorithm to maximize the amount it charges its customers without charging an absurd price. Uber’s pricing algorithm depends on big data analysis. It uses demographics, time of day or year, and other factors to predict demand and supply and optimize pricing, which increases the company’s net profits. Next, companies use big data to increase operational efficiency. Operational efficiency is essentially a measure of the “efficiency of profit earned as a function of operational costs” (investopedia.com). In other words, it is an input to output measure of how much a company makes in regards to how much it spends. According to an article by IBM, the implementation of big data can improve operational efficiency because it enables companies to gain insights from analyzing various data sources in order to enhance asset and infrastructure efficiency. Big data allows analysis of production, so companies can improve manufacturing and reduce production costs, which results in greater operational efficiency. Also, with predictive analytics in big data, companies can enhance production efficiency by pinpointing or predicting the most demanded products and services and ramping up production for those items. By ensuring asset and infrastructure efficiency with big data, companies can reduce costs, upgrade service, and avoid failures. Operational efficiency is crucial for companies to grow and increase profits, and it would not be nearly as effective without big data. Fourth, companies use big data to build customer loyalty by discovering what influences consumers’ decisions and what factors motivate them to return. By working to satisfy, better understand, and predict the behavior and needs of individual customers, companies can build a relationship with individual customers. This greater loyalty encourages customers to return, which increases long-term revenue. The final major impact of big data on the sales and marketing industry is marketing through search engine optimization (SEO). SEO in relation to digital marketing refers to the process of increasing website traffic, views, and conversion rates in order to find new customers and grow. Big data monitors a searcher’s complete history and activity on the internet. Companies analyze this data and deliver results based on a searcher’s activity in an effort to prompt them to a specific page, which can help to increase the traffic of a website and make it more visible and attractive to searchers. Then, companies can adjust their sale tactics to tailor to the consumers’ needs. Another method companies have implemented for digital marketing and sales is effective email campaigns. With this method, companies deliver personalized and relevant emails to consumers marketing certain products based on their preferences and behavior, which have shown to drastically increase sales. Furthermore, since big data permits less costly and more efficient operations and production, companies have a greater amount of capital and time leftover. This additional capital and time can be reinvested into the company, which fosters more innovation. In conclusion, according to a McKinsey & Company report titled Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, companies that incorporate data into their operations show productivity rates much higher than those of their competition. The application of big data analysis in the sales and marketing is crucial for companies to grow and maximize their earnings, which can lead to substantial economic growth.One prominent company that has applied big data as a strategic asset is Netflix. Netflix pursued big data analytics with the hope of engaging its customers, which would, in turn, increase monthly subscriptions and revenue. Netflix fulfilled this hope. They designed an algorithm to gain insight into their customers’ behavior such as their viewing habits, so they could accurately predict the preferences of a specific customer based on their watch history and interests. Netflix is the ideal example of a data-driven company. They collect data based on the activity of their abundance of customers, which they use to satisfy their customers and to make better decisions to tailor certain shows and movies to specific customers. In Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Are, Seth Stephens Davidowitz, a former Google data scientist with a PhD in economics from Harvard, stated: “Netflix learned a similar lesson early on in its life cycle: don’t trust what people tell you; trust what they do… Netflix stopped asking people to tell them what they wanted to see in the future and started building a model based on millions of clicks and views from customers” (Stephens-Davidowitz, 156-157). Netflix used this predictive model to suggest films to consumers based on what the data revealed about the consumer’s behavior. As a result, customers visited Netflix more frequently and watched more movies.” Additionally, Netflix uses big data, especially customers’ viewing habits, to design new shows aligning with the interests and tendencies of their customers. According to an article by Medium titled How Netflix Uses Big Data, (medium.com) Netflix’s predictive algorithm for their original shows demonstrates a success rate of 80% as opposed to a success rate of 30% to 40% for traditional TV shows. This disparity in success rate can be attributed to Netflix’s extensive implementation of big data. Although big data provides companies with a competitive and strategic advantage, some privacy and trust issues have also emerged regarding companies’ usage of big data. A major issue is differential pricing, which is commonly referred to as price discrimination. According to a report titled Big Data and Differential Pricing released in 2015 by the Obama Administration, (https://obamawhitehouse.archives.gov) differential pricing is “the practice of charging customers different prices for the same product.” Traditionally, a retailer might use visual clues or speech to price discriminate, but big data takes this practice to the next level. Big data analysis reveals information about specific consumers, and companies use this information to understand consumer behavior and determine the likelihood of a certain consumer paying a certain price for a product. Specifically, the data and information companies receive about customer behavior is derived from their location, gender, income, race, family size, search history, social network activity, purchase history, and song and video history. This information is exploited to predict consumer behavior and characteristics. For example, senior citizens pay less than middle-aged consumers at movie theaters and for air travel because they are not at peak purchase power. Consumers in certain demographic groups especially benefit from differential pricing because the data shows they are more price sensitive, so they will be presented with lower costs. Seth Stephens-Davidowitz, who has a PhD in economics from Harvard, stated: “Businesses are often trying to figure out what price they should charge for goods and services. Ideally they want to charge customers the maximum they are willing to pay. This way, they will extract the maximum possible profit.” Based on the demographics of customers – which is known through big data analysis – companies charge their customers maximal sums in order to extract maximal profits, and they set prices depending on the demand of consumers for a product or services. Also, with differential pricing, companies can expand the size of the market by providing lower costs to more cost-sensitive customers and reach customers who might not otherwise purchase a product. One may argue that price discrimination is inherently an inequitable tactic companies use to take advantage of their customers; however, as economic theory suggests, differential pricing is actually an effective tactic because it benefits both retailers and consumers. In essence, differential pricing, which is facilitated by big data analysis, uses varying demands and takes advantage of information asymmetry to charge customers different prices for the same product or service, and this allows companies to increase their revenue. Another industry in which big data has had a significant impact is healthcare. Similar to business, big data allows healthcare organizations to become more efficient, productive, and make better decisions in real time. According to an article by Forbes, (forbes.com) the usage of big data in healthcare can be used to improve care efficiency and cost, predict epidemics, cure and prevent disease, and improve safety and avoid preventable deaths. The importance of big data in the healthcare industry is expanding rapidly because as population and longevity increases, organizations must be quicker to provide treatment and predict chronic diseases and outbreaks before they occur. In order to improve care efficiency and cost in the industry, health organizations must analyze big data and information to reveal meaningful insights about individual patients. Optimally, healthcare organizations would strive to present the highest quality patient care at the lowest costs. Big data analysis facilitates this ambition. First, big data allows faster time to treatment, which can potentially save lives and costs, and it delivers imperative diagnoses quickly and accurately by accounting for numerous unique circumstances to formulate the best treatment in real-time. Another way the usage of big data limits costs is by reducing the number of hospitalizations and readmissions, which is done by monitoring patient data in real-time so that patients can self-manage conditions or seek preventive care early on. According to a recent report by McKinsey and Company, in the future big data could save Americans $450 billion annually.