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Mng4200 Amazon’s Big Data Stategy Case Study

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Business Policy and Strategy

MNG 4200

Amazon’s Big Data Stategy Case Study

Lecturer:                Dr. Kiven Pierre

Group Members:                Anuradha Singh – Porter                 1020885

                Renuka Singh                         1021518

                Gina Jaikarran                         10211720

                Shivane Gokul                         1021647

                Anoushka Ramnarine                 1020699

                Lee – Ann Parris                         1014964

                Chelsea Venture                         1020864

                David Fitzpatrick                         1013936

Due Date:                April 11, 2019


EXECUTIVE SUMMARY

Amazon is the leading provider of Internet service since early 2000s. Its success is due to the huge database of customer purchasing data and history that is useful in ascertaining customer preference and keeping tabs on inventory level resulting in better management of operational costs. The data also is also sold to many companies earning income that has boost profits. However, Amazon is faced with large operational cost, the need for constant upgrades to keep up with technological changes and finding innovative ways to earn money form their data. It is therefore recommended that Amazon adapt a product differentiation strategy which is deemed to be profitable in the long run.

INTRODUCTION

Amazon.Com Inc. was incorporated in 1994 by its owner Jeff Bezos Actual operations commenced in 1995 and ran initially from his garage as an online e- commerce of books that were sold by a third party for a commission of fifteen percent (15%). In 1997, the company went Public and offered three (3) million of its shares for sale. The company later diversified to sell software, electronics, food, clothing, toys and jewelry. They are being the largest e- commerce marketplace by revenue and evolved over time to being the largest internet company by revenue in the world. The company was ranked as the top ten retailers in the National Retail Federation Foundation /American Express Customers Choice Award for two years. This was felt by many as a result of the company's use of 'Big Data Strategy” which refers to the “growth and availability of large volumes of data, both structured and unstructured.'

Big data spans three key dimensions - Volume, Velocity and Variety (Laney, Doug).

The company used this strategy to upgrade the customer services which used the customer’s recommendations to give them customized recommendations for future search. Big data helped Amazon developed a 360-degree customer profile thus improving the quality of customer care. In 2009, Amazon acquired Zappos which pushed the company's utilization of the Big Data Strategy even further. This strategy not only aided to the customers care it assisted with the detection of fraud at the organizational level. Product catalogue data analyzed to which items were most likely to be stolen and then communicated to the warehouse to limit the theft.

Additionally, the company used thus strategy to help other smaller e-commerce companies by allowing them to use the same recourse to develop their business. This led to the launching of Amazon Webstore in 2010 which they charged a fix rate. This evolved into Amazon Web Services (AWS) which has solutions for small businesses to implement big data easily. In 2013, Kenesis changed that game and forced Amazon to rethink at the various aspects of big data capacity and effective leveraging.

CENTRAL ISSUES

This section of the assignment entails the problems and challenges undertaken in Amazon’s Big Data Strategy and the opportunities obtainable to exploit on in order to surpass their competitors.

1. Amazon knows what you want before you buy it since it has patented the process of shipping a product to a customer with an expectancy that the customer will order that product based on the power of predictive analytics. The patent signifies that Amazon considers, that the predictive analytics systems will become so precise that they will be able to forecast what a customer will buy and when. If the big data algorithms go wrong with the predictions, then Amazon might have to undertake a tough time in holding the logistics cost for shipping the product and returning it back to the production centers.

Amazon’s success attracted many new competitors like Book Stacks and Book Zone to the market and this led to higher competition for the company. To contradict the competition successfully, Amazon initiated new features like online product reviews where customers could write their own book review as well as read reviews written by others. Retailers like Walmart, Nordstrom and eBay are making big data investments to maintain customer relationships and enhance their business. It is astounding to see how retailers are making precise predictions about customer behavior to drive business value in the long run.  

As an e-commerce giant, Amazon’s success had always depended on making the right products available to its customers. Making the right products available in turn depended on understanding the precise products that customers wanted.

2. A significant factor in the retail market as retailers struggle to offer its best price on each product is price optimization. Price management is monitored closely at Amazon to attract buyers, compete against other competitors and develop the business on profit margins. Dynamic pricing helped Amazon boost profits by 25% on an average and they stay competitive by monitoring the price 365 days. Amazon increased its sales by 27.2% from 2012 to 2013 and made an entry into the list of top 10 retailers in US for the first time. Product pricing strategy at Amazon supports real-time pricing by considering data from various sources like customer activity on the website, available inventory of a product, competitor pricing for a product, order history, preferences set for a product, expected margin on the product, and more.   An average shopper always stumbles on that Amazon’s product prices are the lowest among all their competitors on the web because of their dynamic pricing strategy. Amazon’s dynamic pricing algorithms adjust each product’s price several times in an hour to make the best use of human psychology about price perception. Amazon provides huge discounts on best-selling products at the same time making greater profits on somewhat less popular ones. Amazon is not actually the lowest-priced seller but its consistently low prices on the highest viewed and best-selling items drive a perception among consumers that Amazon has the best prices overall even better than its competitor; Walmart.

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