Big Data: The Extra Service Amazon Provides

Group 10: Deema Al-Masri Sosebee, Ha Eun Bumm, Frederic Lübbert, Jang Seob Yoon

What is Amazon.com?

Amazon.com is an American e-commerce company with headquarters in Seattle, Washington. The company is the world’s largest internet-based retailer by total sales, and was founded in 1994 by Jeff Bezos. Amazon begin in retail as an online bookstore, but quickly diversified into other branches, such as electronics, CDs, and toys. In recent years they’ve innovated their own products, such as a tablet – the Kindle Fire –  and an Android-based smartphone – the Amazon Fire. Amazon is also one of the largest providers of cloud infrastructure services[1], offering the Amazon Simple Storage Service (S3), a “highly-scalable cloud storage”.

As Amazon grew bigger, so did its international fame. Amazon now has separate retail websites for some of the most populous nations, such as the United States, [2]Australia, Great Britain, China, Mexico and Germany. The company even sends certain products to the countries that lack their own web service. With this, Amazon’s annual revenue is US$ 107 billion (2015)[3] with US$ 65.444 billion in capital (2015) [4].  In 2014, Amazon had a total active user base of 244 million, where an active customer is defined as a customer account that has bought something through the service in the past 12 months.[5]

Big Data and Amazon

So what does that have to do with big data? Much of Amazon’s appeal comes from its individualized service – a result of the incredible amount of data it has collected. Amazon uses cookies to track the websites a consumer visits[6]. It also uses specialized algorithms to advertise items it believes would be relevant to that particular consumer –  a method of advertising that only a retailer with such a large amount of information on any unique customer could execute. This large amount of information can only be classified as big data.

The Types of Big Data

Because Amazon collects a variety of big data from its customers, there are many different types of big data. An example of one would be the information they record about what and when a consumer has viewed on their site. This is very relevant in terms of a business’s sales plan, especially around particular times of the year (e.g.: holidays). Other types would be the information from customer shipping methods, reviews left for retailers – including the period of time between the purchase and the written review – and which platform consumers accessed their website (e.g.: mobile browser, app, et cetera)[7].

So what does Amazon do with our data?

While Facebook, Google and other general sites that use big data have the advantage of more general data on potential consumers, Amazon collects data on what consumers of a different demographics actually purchase[8]. Because of this, Amazon can, and does, sell this information about its customers to retailers so that they can more effectively place advertisements for a specific market segment. It can also make recommendations according to our preferences using this data. Other collectors of big data, such as Google, cannot claim to know what consumers like, only what they are interested in. This does not allow for such effective market segmentation approaches by retailers.

Amazon’s Gain from Big Data

In 2015, Amazon’s revenue was $48 billion USD, while advertising only brought in $500 million[9]. Amazon uses its collected data on both demographics of people, as well as individual customers, to build a brand and relationship with its customers. Its constant innovation, such as its storage systems, as well as its top-ranking customer service and incredible product selection, are what come together with this data to produce a service that has such a loyal and profitable consumer base[10].

 

Project Timeline (to date)

September 28th 2016:

  • Initialized group communication for project
  • Agreed to individually research various topics and possible case studies pertaining to Big Data

October 3rd 2016:

  • Decided on main topic – “Amazon & Big Data”
  • Distributed workload to each member
  • One person to investigate Amazon
  • Two people to investigate how Amazon collect the data
  • One person to explain the data collected from Amazon
  • Set up date for future meeting to discuss (5th October)

October 5th 2016:

  • Proofread & shared each member’s work (via Google Doc)
  • Combined and formatted project proposal to be uploaded on blog & sent by email.
  • Future meeting date to be decided

 

Bibliography

NV, Synergy Research Group Reno. “Microsoft Cloud Revenues Leap; Amazon Is Still Way Out in Front | Synergy Research Group.” Microsoft Cloud Revenues Leap; Amazon Is Still Way Out in Front | Synergy Research Group. N.p., n.d. Web. 06 Oct. 2016. [1]

Amazon.com. Amazon.com. N.p.: Amazon.com, n.d. 31 Dec. 2012. Web. 4 Oct. 2016. [2]

Rogers, Ty. “Exhibit 99.1.” Exhibit. Amazon.com, n.d. Web. 4 Oct. 2016. [3]

Rogers, Ty. “Exhibit 99.1.” Exhibit. Amazon.com, n.d. Web. 4 Oct. 2016. [4]

Kline, Daniel B. “How Many Customers Does Amazon Have?” The Motley Fool. N.p., 01 Jan. 1970. Web. 4 Oct. 2016. [5]

By Visiting Amazon.com, You Are Accepting the Practices Described in This Privacy Notice. “Amazon Privacy Notice.” Amazon.com Help: Amazon.com Privacy Notice. N.p., n.d. Web. 4 Oct. 2016. [6]

By Visiting Amazon.com, You Are Accepting the Practices Described in This Privacy Notice. “Amazon Privacy Notice.” Amazon.com Help: Amazon.com Privacy Notice. N.p., n.d. Web. 4 Oct. 2016. [7]

Leber, Jessica. “Amazon Woos Advertisers with What It Knows about Consumers.” MIT Technology Review. N.p., 21 Jan. 2013. Web. 4 Oct. 2016. [8]

Leber, Jessica. “Amazon Woos Advertisers with What It Knows about Consumers.” MIT Technology Review. N.p., 21 Jan. 2013. Web. 4 Oct. 2016. [9]

Howen, By Allison. “5 Reasons Amazon Wins at E-Commerce.” – ‘Net Features. N.p., 03 Mar. 2014. Web. 4 Oct. 2016. [10]

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