Amazon's Big Data Strategy Adapa Srinivasa Rao Debapratim Purkayastha IBS Hyderabad IBS Hyderabad "As Amazon's recommendation team, we asked ourselves, how do we take this data and make a little bit more money, how do we apply it in this channel differently and it was really neat. Now it is being called 'big data' in the marketing and media world, but at the time we were doing this stuff, it was just kind of putting one foot in front of the other." 1 —David SeBinger, Former Software Manager (Customer Behavior Research), Amazon.com , Inc. (Amazon), in October 2013. • • eading e-commerce company Amazon corn, Inc. (Amazon) and its subsidiary Zappos were ranked among the top ten retailers in the National Retail Federation Foundation/American Express Customers' Choice Awards a for two years (2010 and 2011) in a row. Industry observers felt that the coveted recognition was the result of Amazon's use of its big datab resources to provide superior service quality. Right from the time it had emerged as a dominant provider of Internet services in the early 2000s, Amazon had started to focus on big data to improve its performance. Along with many other major Internet companies, it realized the importance of big data in the early 2000s, and had since then, focused on properly utilizing the huge databases of people who were shopping on its e-commerce portals. Amazon leveraged its big data sources to give its customers good product recommendations and L thereby improve the relationship with them. It utilized its big data resources to meticulously upgrade its famed customer recommendation system. Data on past purchases made by customers was used to give them highly customized product suggestions. Analysis of past customer data also helped Amazon in giving suggestions to new customers who were buying from its portal for the first time. Big data helped Amazon in developing 360 degree customer profiles and to create hyper-personalized marketing messages regarding the products based on the needs and preferences of individual customers. On the customer side, Amazon also utilized its big data resources to improve the quality of its customer care. Easy access to the profiles of customers and their past purchasing/browsing habits made it easy for the company's customer service executives to provide quick solutions to the complaints of customers. The a The National Retail Federation Foundation/American Express Customers' Choice Awards were designed to know the consumer attitudes toward retailers' customer service. The survey collects data by polling consumers and is conducted by the marketing intelligence firm BlGinsight. b Big data refers to the growth and availability of large volumes of data, both structured and unstructured. Such an exponential volume of data could not be analyzed by the traditional software used to handle databases. The latest trends in technology allowed decision making to be done largely based on data and analysis instead of past experience and intuition. According to a definition given by industry analyst, Doug Laney, big data spans three key dimensions, viz. Volume (amount of data generated), Velocity (speed at which data is streamed), and Variety (formats in which data comes in). This case was written by Adapa Srinivasa Rao, under the direction of Debapratim Purkayastha, IBS Hyderabad. It was NYDER ABAD compiled from published sources, and is intended to be used as a basis for class discussion rather than to illustrate either effective or ineffective handling of a management situation. © 2014, IBS Center for Management Research All rights reserved. To order copies, call +91 9640901313 or write to IBS Center for Management Research (ICMR), IFHE Campus, Donthanapally, Sankarapally Road, Hyderabad 501 203, Andhra Pradesh, India or email: I S info@icmrindia.org , www.icmrindia.org C-404 PART 2 Cases in Crafting and Executing Strategy acquisition of Zapposc by Amazon in the year 2009 further facilitated the use of big data in improving customer service quality. Big data resources were also put to some innovative uses like checking fraud at the organizational level. Product catalogue data was analyzed thoroughly to identify which of the items were more likely to be stolen. The results of this analysis were fed back to the warehouses of the company to limit the theft of items. Other than improving its own performance, Amazon also helped other smaller e-commerce companies by allowing them to use its big data resources and improve their performance. An innovative service called Amazon Webstore, launched in 2010, allowed smaller companies to build their portals around Amazon's e-commerce platform. Users of Amazon Webstore could advertise their products on Amazon's portals by paying a small part of the sales proceeds as a commission to Amazon For a fixed monthly fee for utilizing the service, partnering businesses could use Amazon's big data resources. Amazon Webstore was quite successful and was adopted by both small as well as big retailers such as Timex and Samsonite who did not want to have their own e-commerce system. Amazon's suite of cloud based Internet services known as Amazon Web Services (AWS) had also come out with solutions for small companies so that they could implement big data easily. A new service known as Kinesis, announced in November 2013, could process high volumes of data flowing into AWS on a real time basis. According to some industry observers, this was Amazon's bid to close the loop on its integrated cloud stack and deliver an end-to-end solution for collecting and processing data. They felt that just by taking a relook at the various aspects of its big data capabilities and effectively leveraging on these, the company could emerge as a threat to the entire analytics eco-system. 2 BACKGROUND NOTE Amazon was founded in the year 1994 by Jeffrey Preston Bezos (Bezos). It started its operations at a time when the reach of the Internet was increasing and the Internet was being considered as a potential business medium. Understanding the trend, Bezos came up with the idea of selling books through the Internet. He felt that books were the best products to sell online as millions of titles were in print and an e-commerce site could house and sell many more books than the conventional brick-and-mortar bookstores. Bezos calculated that the common brick-andmortar stores could not house more than 200,000 books at a time3 and aimed to build a large online bookstore which would be bigger than any physical bookstore in the world. Amazon was initially funded with the money that Bezos borrowed from friends and relatives. Bezos and his wife, along with some employees, built the website and tested it for over a year before launching it (Refer to Exhibit 1 for Timeline of Amazon). Amazon was finally opened to customers in the year 1995. Like many other technology giants, it was initially run from a garage—the one in Bezos' Washington home. At the time when Amazon started its operations, the book retailing market was highly fragmented and there was no major player except Barnes & Noble, Inc. d Barnes & Noble had onetenth of the total market share but no online presence. Amazon thus got the first mover advantage and faced very little competition in its initial days of operation. Right from when it began its business operation, Bezos focused on customers and believed that customer loyalty was the key to penetrating the market and increasing sales Amazon started to ship goods to all the 50 states in the US and 45 other countries within a month of its launch—and all this while still working from Bezos' garage. Amazon's popularity grew through word-of-mouth as customers recommended it to others. Within four months of its launch, Amazon was selling more than 100 books a day. The company's impressive performance attracted investors and Amazon got its first big investment of US$ 100,000 from Madrona Venture Group, Inc.e in 1995. The company reported net sales of US$ 511,000 during the first six months of its operations and Bezos' confidence that he could make a success of the company increased. " Zappos, headquartered in Las Vegas, Nevada, USA was a leading online retailer of shoes and clothing products. This online shopping portal was founded by Nick Swinmum in the year 1999. • Barnes & Noble, Inc., headquartered in Manhattan, New York City, USA, is the largest book retailers in the United States. Madrona Venture Group, Inc., headquartered in Seattle, Washington, USA, is a venture capital which primarily focuses on investing in earlystage technology companies. • • • CASE 28 EXHIBIT 1 C-405 Timeline of Amazon.com Year Month Event 1994 July Amazon incorporated in Delaware 1995 1996 1997 July July May 1998 June March Amazon.com launched. Sells its first book, "Fluid Concepts & Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought" Amazon.com Associates Program launched Announces IPO and begins trading on NASDAQ Launches music store 1999 Launches Amazon.com Auctions, the company's Web auctions service December Jeff Bezos named Time Magazine "Person of the Year" Launches Amazon.fr (France) 2001 August November April 2002 July Launches Amazon Web Services November Opens Apparel & Accessories Store 2000 • Amazon's Big Data Strategy Launches Amazon.co.jp (Japan) Amazon partners with Borders Group to run the company's online bookselling business 2003 2004 June Launches Amazon Services, Inc. subsidiary April 2005 2006 2007 February September November Opens Jewelery Store Introduces Amazon Prime Launches digital video download service, Amazon UnboxTM Launches Amazon Kindle Announces Frustration-Free Packaging initiative 2008 November 2009 May Introduces Kindle DX 2010 2011 2012 2013 April July February August Amazon moves to new HQ in South Lake Union, Seattle Market capitalization of Amazon tops US$ 100 billion Amazon launches Sports Collectibles Store Jeff Bezos buys Washington Post Source: Compiled from various sources. • At the beginning of 1996, Amazon moved to new headquarters—a small warehouse in Seattle. The company employed 11 people and offered 2.5 million book titles. Following the Japanese model, Amazon had very limited inventory and thereby kept its costs under control. It started an innovative affiliate marketing program called Amazon Associates Program in July 1996. The program allowed third party websites to sell books through links to Amazon posted on their sites for a commission of 15 percent on the total sales made. The program was a huge success and helped in expanding Amazon's reach without the company having to spend much on advertising. Experts opined that the program not only generated traffic to Amazon but enhanced the brand's presence online as these third party sites carried Amazon's logo on their pages. The Amazon Associates Program was later extended to all the products sold on Amazon's portal. Amazon went public in the year 1997 and offered 3 million of its shares for sale. The shares opened at US$ 18 a share and the IPO raised US$ 54 million for the company. In the year 1998, Amazon started selling DVDs with the opening of its video store which was followed by the launch of Amazon.com auctions in March 1999. 4 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 counter the competition effectively, Amazon introduced new features like online product reviews where customers could write their own book review as well as read reviews written by others. By the year 2000, Amazon had made a big change in its business model and started selling other products. In 2000, it also expanded its presence and launched sites in France and Japan. In the year 2001, Amazon C-406 EXHIBIT 2 PART 2 • Cases in Crafting and Executing Strategy Financials of Amazon from 2008-2012 (in US$ Millions) Total net sales $61,093 $48,077 $34,204 60,417 47,215 676 40 862 61 32,798 1,406 51 (92) (65) (39) (80) (132) 76 79 29 72 91 32 59 1,161 901 (247) Total operating expenses Income from operations Interest income Interest expense Other income (expense), net Total non-operating income (expense) 544 Income before income taxes Provision for income taxes Equity-method investment activity, net of tax Net income (loss) 934 (291) (12) (428) (155) $ (39) $ 631 1,497 (352) 7 $ 1,152 $24,509 $19,166 18,324 842 23,380 1,129 37 (34) 83 (71) 47 (253) (6) $ 902 $ (9) 645 Source: allowed other retailers to sell their products through its site and took a part of the sales proceeds as its commission. In the last quarter of the year 2001, Amazon reported its first profit. 5 Amazon's operations were further expanded in the year 2003 as it opened new websites in AsiaPacific and European countries. In the year 2006, it launched a key subsidiary called Amazon Web Services.6 Amazon Web Services provided an array of cloud based remote computing services to its customers. The advent of the digital era was changing the content consumption patterns of people. Many people started reading books and magazines on their desktops and laptops instead of buying physical copies. Responding to this change, Amazon introduced an e-book reader called Kindle in the year 2007. Kindle was a big hit in the market and heralded a new era of digital reading. Kindle was later released as an app for other devices working on operating systems like Android and i0S f. By the year 2011, the market capitalization of Amazon had reached the US$ 100 billion mark, making it one of the leading technology companies in the world. For the fiscal year 2012, Amazon had revenue of US$ 61.09 billion and a net loss of US$ 39 billion (Refer to Exhibit 2 for the financials of Amazon). ( Android and iOS are the two leading mobile OS promoted by Google and Apple respectively. They are used in mobile devices like smartphones and tablets. BIG DATA AT AMAZON Over the years, Amazon had evolved from being a pure e-commerce player into a giant Internet services firm which offered a large range of services for individuals and corporations. It started to focus heavily on big data and embarked on its transition from a pure online retailer into a giant big data company.7 Amazon along with other major Internet giants like Yahoo! Inc.g (Yahoo) and Twitter, Inc. h • (Twiter)alzdnhy20staed huge amounts of data about their users which they could put to valuable use. 8 While the other companies did not concentrate on the importance of big data, Amazon was quick to cash in on the invaluable database of people who shopped on its e-commerce portals around the world. The product recommendation team at Amazon thought of innovative ways in which it could use the data accumulated by the company.9 The result was the big data revolution which transformed the way Amazon did business. 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. Understanding the g Yahoo, Inc., headquartered in Sunnyvale, California, USA, is a leading multinational Internet company. h Twitter, Inc., headquartered in San Francisco, California, USA, is a leading social networking and microblogging service. It allows its users to send and receive text messages which are limited to 140 characters. • • CASE 28 needs and tastes of customers involved doing proper market research as well as analyzing its own customer base. Since its inception, Amazon had been renowned for its product recommender system which provided product suggestions to customers depending upon their past purchasing behavior. Data collected from its customers was the primary driving force behind Amazon's recommender system. Being the leading e-commerce player, Amazon had a large bank of data regarding the likes and the past purchasing behavior of its customer base. It had used this data bank to build its recommender system. Its earlier recommender system had been based on showing more items similar to the ones which were being looked for by its customers. This item-by-item similarity method was built on the basis of collaborative filtering' and was hugely successful in deepening the relationship with its customers. Its recommender engine had since been improved and perfected to give better results. Amazon later started utilizing the historical purchase data of consumers as well and the clickstream data of all its customers to show webpages with uniquely customized information. 10 Using such data helped Amazon in many ways other than showing the related and alternative products that the consumers had been looking for. Mining the vast amount of data helped in understanding the inner feelings and likings of customers which they could not express themselves. Commenting on the importance of data in understanding the behavior of customers, Michael Driscoll of Datasporai said, "You can ask people what influences their desire to renew their cell phone contract, and what people say and what they do are often very different. Data is the key to differentiating between what people say in terms of sentiment and what they do in terms of actions." 11 UTILIZING BIG DATA Amazon leveraged on big data to improve its relationship with its customers and provide superior customer service. The online retailer built a vast database of its customers and their buying preferences over a long period of time. It was one of • The Collaborative filtering system is a technique used in many recommender systems of e-commerce portals. Collaborative filtering involves filtering information or usage patterns through techniques involving multiple data bases, viewpoints, and agents. Dataspora, headquartered in Cambridge, Massachusetts, US is a leading big data and analytics consultancy. Amazon's Big Data Strategy C-407 the first e-commerce companies to start using the cross-selling/up-selling method. This customer recommendation system was later augmented by utilizing its big data resources. Using big data, Amazon started analyzing the past product purchases made from its online store by its customers and the other items that were purchased along with them. Data collected from its customers was used to give silent but highly customized suggestions to make them buy more. This analysis of data helped Amazon give product suggestions to existing as well as new customers who might not have otherwise bought a complementary product. This was the reason why Amazon's sites displayed 'Other customers who bought this item also purchased that item' kind of cross-selling recommendations. 12 Product related recommendations were also customized based on many factors such as the customer's location and demography. "It can even cross-correlate buying behavior between home and garden sales," 13 said Jeff Kelly, lead big data analyst at Wikibon k. The bewildering range of products that were showcased on e-commerce portals made them seem unwieldy and incomprehensible to many customers. According to analysts, mining the treasure trove of information and providing relevant product recommendations could make e-commerce sites feel smaller and more intimate to the consumers. 14 Big data also helped Amazon in the development of its personalized marketing strategy—a tactic in which it excelled. Many e-commerce firms resorted to generic mass emailing of the products and offers that were available with them. This strategy led to the wastage of marketing efforts of many firms and the labeling of e-commerce mails as spam Amazon created 360 degree customer profiles which tracked and stored everything related to customers like their browsing history, social data, tastes and preferences, past purchase history, etc. These 360 degree customer profiles facilitated the identification of discerning groups of customers who could be well targeted. 15 Amazon could create hyper-personalized marketing messages regarding the products based on the individual customer's needs and interests. 16 Amazon also relied on big data to improve the quality of its after sales service to its customers. k Wikibon, headquartered in Marlborough, Massachusetts, USA, is a community of practitioners and consultants on technology and business systems that use open source sharing of free advisory knowledge. C - 408 PART 2 Cases in Crafting and Executing Strategy Most American customers were known to have a largely negative experience in their service interactions. 17 Amazon tried to solve this problem by leveraging on the large data it had regarding its customers. Having the right data helped it have a favorable exchange with its customers and to solve their problems quickly. Amazon's customer service executives had speedy access to data regarding the past purchases and browsing history of its customers. This enabled the company to provide quicker solutions to the problems and complaints of its customers. Complainants did not have to spell out their details like last names, contact numbers, and addresses repeatedly before their queries/problems were solved. This unique approach of utilizing big data to improve service quality gave spectacular results. After having a positive experience with Amazon's support team in one such encounter, Sean Madden, a top business blogger, said, "After nearly a decade of ordering stuff from Amazon, I never loved the company as much as I did at that moment." I8 One of the factors which facilitated the use of big data for customer service was Amazon's acquisition of Zappos, the largest online retailer of shoes, in the year 2009. Amazon acquired Zappos for US$ 1.2 billion to expand its reach in product categories in which it was not strong. 19 Zappos was famed for using its customer database to provide a personal touch to its customers and turn them into its fans and cheerleaders. 2° Amazon adopted the customer service strategies of Zappos after it took over the company. The application of big data for improving customer EXHIBIT 3 service made Amazon and its subsidiary Zappos to rank among the top 10 retailers in National Retail Federation Foundation/American Express Customers' Choice Awards for the years 2010 and 2011 • (Refer to Exhibit 3 for top 10 retailers in National Retail Federation Foundation/American Express Customers' Choice Awards for 2010 and 2011). 21 Rather than using big data to just provide better product suggestions and improve the quality of service, Amazon used it to check fraud in the organization. An interesting area where Amazon benefited through using big data was in preventing warehouse theft. At any given point of time, Amazon had 1.5 billion items in its catalogues across its 200 fulfillment centers across the world. Theft of these items was a big threat to Amazon. The problem with identifying which of these items were more sought after by thieves was that both expensive and low-priced items were stolen. Inexpensive items too were often stolen due to reasons like their scarcity. To solve this problem, Amazon used big data and updated its product catalogue data nearly 50 million times a week. 22 Product catalogue data was collected, stored, and analyzed to identify which of the items were more likely to be stolen and the information was fed back to the warehouses (Refer to Exhibit 4 for the five components of big data process). This helped Amazon in preventing the theft of items in its catalogues. Werner Vogels (Vogels), Chief Technology Officer and Vice President of Amazon.com , felt that data and storage should be unconstrained. "In the old world of data analysis you knew exactly which questions you wanted to ask, which drove a Top 10 Retailers in National Retail Federation Foundation/American Express Customers' Choice Awards for 2010 and 2011 1 Amazon.com Zappos 2 L.L. Bean Amazon.com 3 Zappos L.L. Bean 4 Overstock.com Overstock.com 5 QVC Lands' End 6 Kohl's Department Stores JCPenney 7 Lands' End Kohl's 8 JCPenney QVC 9 Newegg Nordstorm Nordstorm Newegg 10 Source: "Customers' Choice Awards." • • CASE 28 Collect Collecting and getting the data to the place where the process can be started. Store Storing the collected data before it is put to proper use. Organize Controlling the quality of data by knowing which data to include in the stream. Organizing also involves validating data in order to make sure that correct data is used. Analytics Analysis of well-organized data to create usable information. Share Information that is created through analytics is shared with those who need it. very predictable collection and storage model. In the new world of data analysis your questions are going to evolve and change over time and as such you need to be able to collect, store, and analyze data without being constrained by resources." 23 • C-409 EXHIBIT 4 Five Components of the Big Data Process Source: Eric Savitz, "CeBIT: Amazon CTO Werner Vogels Talks Big Data," • Amazon's Big Data Strategy AIDING OTHER COMPANIES WITH BIG DATA Other than utilizing big data for improving its own performance, Amazon also helped other e-commerce portals to leverage its big data resources. This it achieved through an innovative service for smaller e-commerce businesses called Amazon Webstore. Amazon Webstore, launched in 2010, allowed retailers to build their portals around Amazon's e-commerce platform.24 Amazon Webstore was an independent store outside of Amazon's official e-commerce store built on the third party domain name and brand. Users of Amazon Webstore could place Amazon Product Ads' on Amazon's portals. People who clicked on these product ads would be redirected to the site of the partnering site where the sale would be completed. As part of this partnership, Amazon allowed its partnering sites to use its big data while maintaining their independent identity as a small online e-commerce store.25 Amazon charged its merchant partners a fixed monthly fee as well as a fixed commission for using its resources and big data resources. 26 An interesting case where Amazon Webstore improved the performance of a small retailer was Anaconda Sports. Anaconda Sports, a successful Amazon Product Ads was an advertising program that allowed sellers to promote their products on the official e-commerce portals of Amazon in different countries. , August 3, 2012. sports retailer from New York, USA, found itself stuck with an inefficient and expensive e-commerce system with issues like inability to store all the customer information, lack of unique experience based on customer preferences, and poor customer service quality.27 Modifying its e-commerce portal through Amazon Webstore made it possible for it to develop an efficient store which solved all the problems it had been facing as well as increase its sales substantially. Commenting on the benefits of Amazon's Webstore for small and medium businesses, Scott Pulsipher, director of Amazon Webstore, said, "By leveraging Amazon's technology and infrastructure, Amazon Webstore levels the playing field for smalland medium-sized businesses, helping them quickly and easily build their businesses and improve the customer experience."28 Amazon Webstore was implemented even by big brands like Timex, MTV, Boeing, and Samsonite which allowed them to improve their engagement with their customers (Refer to Exhibit 5 for the screenshot of Samsonite's website built using Amazon Webstore). Commenting on how Amazon Webstore helped to increase sales and cut costs at Timexm, its e-commerce director Cal Crouch said, "When we launched our new Amazon Webstore, we saw an immediate lift of 40 percent in revenue and average order size. And on the support side, we have gained the flexibility to make most changes to content as well as brand ourselves—saving us thousands (of dollars) in development costs." 29 Amazon's Amazon Web Services (AWS) helped a lot of companies to develop better applications, deploy new products and services, and cut their costs m Timex, headquartered in Hoofdorp, Netherlands, is a maker of timepieces and luxury goods. PART 2 Cases in Crafting and Executing Strategy C-410 EXHIBIT 5 Screenshot of Samsonite's Website Built Using Amazon Webstore • Sams nite SELECT PRODUCTS BUY 1 GET 10% OFF, BUT 2 GET 15% OFF, RUT 3 GET 20% OFF • FREE STANOARO SHIPPING ON ALL LIGHTWEIGHT SPINNERS Online only. Ends 1/2. use Promo Code: NEWYEARS Offer Derails., LUGGAGE BUSINESS & LAPTOP BACKPACK & MESSENGER DUFFLE & SPORT ACCESSORIES COLLECTIONS Lightweight Spinners, Four Wheel Freedom. SHOP LIGHTWEIGHT SPINNERS HIGHLY RATED TOP SELLERS Featured LATEST RELEASES Prod r•••It III Samsonite Samsonite EZ Cart Samsonite Lift HypErSpaCe Spinner 2S" Wheeled Boarding BPS Boarding Bag SamsoniteVizAlr Laptop Backpack • Samsonite Carbonl DUX 20' Spinner Pink Source: http://webstore.amazon.com/client-showcase/b/6254207011. (Refer to Exhibit 6 for AWS architecture). Amazon offered its solution using familiar tools such as Oracle Database and Microsoft SQL Server, while also pioneering and promoting new platforms such as DynamoDBt1, Hadoop°, and RedshiftP. 3° "One of the core concepts of Big Data is being able to evolve analytics over time. For that, a company cannot be constrained by any resource. As such, Cloud Computing and Big Data are closely linked because for a company to be able to collect, store, organize, " DynamoDB is a managed NoSQL database service which makes it simple and cheap to store and retrieve large amounts of data. ° Hadoop is an open-source software framework for storing and processing large data-sets. and powerful data warehouse servicc which is a Pare P Redshift is a fast of AWS. analyze, and share data, they need access to infinite resources,"31 said Vogels. Small companies faced a lot of difficulties in adopting and deploying big data due to the limited resources at their disposal Amazon came out with solutions for such companies so that they could implement big data easily. In November 2013, Amazon Web Service announced a new service for real time processing of big data. The service known as Kinesis, processed the high volumes of data flowing into Amazon's web-based storehouses on a real time basis. The tool had the capability to accept any number of data sources and could process terabytes of data per hour. It was intended to allow developera to create acycateatierts that -a/el -Ste...I ea a bumir. as basis for tasks like website traffic arnalysis, 110 • CASE 28 Amazon's Big Data Strategy C-411 EXHIBIT 6 AWS Infrastructure AWS Simple Storage Service Logs Users Elastic Compute Cloud Instance Elastic Compute Cloud Instance RESTful API Web Server Storage (Images, Video, etc.) Job Worke A Backup Source: tp://d36cz9buwruitt.cloudfront.net/pixnet_diagram_2.jpg. transactions related to marketing and finance, social media data, and logs (Refer to Exhibit 7 for Kinesis' architecture). Commenting on the flexibility Kinesis would bring to businesses, an analyst at Neoviseq Paul Burns said, "Sometimes people spend hours or days just collecting the data, then coming back and processing it, so it's out of date. . . . So Amazon said we'll take care of all that for you, just write your own program and connect to us." 32 The ability to create big data apps through Kinesis was expected to remove one of the biggest bottlenecks for smaller companies in adopting big data for their businesses. But one limitation of Kinesis was that all the data processing would be done at the data centers of Amazon itself instead of at the clients' location. LOOKING AHEAD Analysts came up with suggestions on more ways in which Amazon could benefit from big data. Having • q Neovise, headquartered in Fort Collins, Colorado, USA, is an IT industry analyst firm. its roots in selling books, Amazon had built a review system for the books sold through its website. Amazon's review system was mainly based on text reviews written by customers and the number of stars (from one to five) given to a book or author. This review system allowed Amazon to build a community and a loyal customer base. Over the years, there were allegations that many authors had found a way to manipulate Amazon's review system and to get paid reviews for their books. Such paid reviews tended to be biased and in turn, they impacted the reliability of the review system. 33 To solve this problem, some industry experts suggested that Amazon create a big data solution which would allow readers to give a vast range of additional feedback and comments which could be used to check the veracity of the reviews. Analysts opined that apart from improving the reliability of its review system, a big data based review system would also make it possible for Amazon to show more relevant reviews to the customers just as it suggested relevant products. Another suggestion regarding the use of big data to further Amazon's prospects was in giving C 412 - PART 2 • Cases in Crafting and Executing Strategy EXHIBIT 7 Kinesis' Architecture Appl (Aggregate & De-Duplicate) App2 AWS Endpoint Availability —"– Zone (Metric Extraction) App3 (Sliding Window Analysis) App4 (Machine Learning) Source: http://arstechnica.com/information-technology/2013/11/amazon-wades-into-big-data-streams-with-kinesis/. better competition to other big Internet companies like Google Inc.` and Facebook, Inc.' The business models of many of these Internet companies were based on online advertising. And according to an estimate by Google, 30 to 40 percent of its revenue from search advertising came from e-commerce sources. 34 The growth of Amazon as the Internet's one-stop shop and its increasing product base made it the primary destination for product searches, clearly bypassing Google. This unique position left Amazon in possession of more shopping data of people than any other Internet company. Some analysts Google Inc., headquartered in Mountain View, California, USA, is a leading Internet-related products and services firm. Facebook, Inc., headquartered in Menlo Park, California, USA, is a leading social networking service. The Washington Post, headquartered in Washington, D.C., USA is a leading American newspaper. • were of the view that using big data, Amazon could beat Google and Facebook in the long term. Bezos acquired The Washington Post` for US$ 250 million in August 2013.35 This acquisition sparked speculation among industry observers that Bezos would use big data to revitalize the news business and find new revenue sources for the ageing business. The use of big data analytics could give better insights into the readers of The Washington Post. Amazon could thereby integrate the likings of readers in developing new products in the news business. According to analysts, Amazon's expertise in big data could transform the online news business in the same way as Bezos had transformed the 500-year-old book publishing business. 36 According to Wikibon's big data analyst Jeff Kelly, as of end 2013, Amazon had all the pieces of the big data puzzle but the firm would have put these together effectively to emerge as a dominant player in this space. 37 • • END NOTES • Teresa Novellino, 'At War with Amazon? Rich Relevance Offers Big Data Weaponry," , October 9, 2013. 2 Maria Deutscher, 'Amazon Closes the Loop on Big Data," , November 22, 2013. 3 "History of AMAZON.COM ," 1 4 "Timeline History Amazon.com ," 5 "History of AMAZON.COM ," 6 "History and Timeline," 7 "How Amazon is Leveraging Big Data," Bill Vorhies, 'A Brief History of Big Data Technologies — From SQL to NoSQLtpHadoop and Beyond," , October 31, 2013. 9 Teresa Novellino, At War with Amazon? Rich Relevance Offers Big Data Weaponry," , October 9, 2013. 10 "How Amazon is Leveraging Big Data," CASE 28 Amazon's Big Data Strategy Adria Saracino, "Interesting Ways Businesses Use Big Data to Improve Personaliza, April 23, 2013. tion," 15 Lisa Desjardins, "How Amazon Uses Marketing Personalization," , October 7, 2013. 16 Lisa Desjardins, "How Amazon Uses Marketing Personalization," , October 7, 2013. Sean Madden, "How Companies like Amazon Use Big Data to Make You Love Them," , May 2, 2012. 18 Sean Madden, "How Companies like Amazon Use Big Data to Make You Love Them," , May 2, 2012. 19 "History and Timeline," 14 8 " "Q&A: What Can Non-IT Companies Learn from Amazon and Facebook about How to Leverage Big Data?" 12 Jodi Beuder, "How Can Big Data Improve the Customer Experience this Holiday Season?" , November 5, 2013. 13 Constance Gustke, "Retail Goes Shopping Through Big Data," , April 15, 2013. • Justin Amendola, "What Zappos.com can Teach you About Turning Customers into Mega Fans," May 19, 2012. 21 Frank Reed, 'Amazon Retains Top Spot in Customer Service Poll, Zappos Third," , January 19, 2012. 22 Ryan Lawler, "How Amazon Uses Big Data to Prevent Warehouse Theft," , October 18, 2011. 23 Roberto V. Zicari, "On Big Data: Interview with Dr. Werner Vogels, CTO and VP of Amazon. com ," , November 2, 2011. 24 Chris Crum, 'Amazon Launches New Webstore E-Commerce Product," , May 24, 2010. 25 "Power of Amazon," , .„-,ostore.amaaon 20 -your-brand/ 26 Amazon Webstore Pricing," C-413 ap://webstore. amazon.com i arnazar.-webstore-pricing/b/ 6960 7501•. Adria Saracino, "Interesting Ways Businesses Use Big Data to Improve Personaliza, April 23, 2013. tion," 28 Chris Crum, 'Amazon Launches New Webstore E-Commerce Product," , May 24, 2010. 29 Chris Crum, 'Amazon Launches New Webstore E-Commerce Product," , May 24, 2010. 30 Doug Henschen, 'Amazon's Vogels: Big Data Belongs In The Cloud," , April 19, 2013 31 Roberto V. 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