The influence of companies on society has shifted from the old “media giants” or media conglomerates such as Time Warner, which provides a wide media spectrum with TV, radio, newspapers, entertainment and online services, to new “giants” in digital media such as Google or Facebook. One particular and different example for this new media giant is Amazon.com Inc., which brought the old model of a catalogue service into the digital world and uses social media structures as their base for customer adjustments and marketing.
Amazon is the most successful online-commerce order company, started in 1995 as an online-bookshop and had a turnover of 61,093 billion US-dollars in 2012.  Today they sell and ship not just books but millions of products for the interest, hobby and living section. By their wide product range Amazon took over the role of a global online department store. With the arrival of the kindle (e-book reader) in 2007, the company even started selling their own hardware product. Today Amazon’s main business is in e-commerce and the selling/reselling area, but they continue to grow and are with Amazon MP3/Amazon VoD, Amazon game studio, the kindle production unit and especially other web services (IMDb, audible.com, etc.) a big player or “giant” in the online media market.
Amazon’s business model can be classified as e-commerce, where the commerce part relies on the negotiation of transactions. This definition is going back to Wirtz categorized into four types of business models: content, commerce, context and connection, which I find helpful for this step .
One might discuss that Amazon doesn’t count as social media, but it’s services surely rely on social media structures. I would therefore like to present three of those structures, such as the recommendations, the rating system and the buyer/consumer feedback which are based on design patterns, common in social media. The key to Amazon’s success relies in their algorithm to suggest customers similar and “wanted” products. Linden et al. explain that Amazon “personalizes the online store for each customer” through an “item-to-item collaborative filtering” algorithm . This process is created through a matching of the customers previous purchases and interests (click’s made on articles at the amazon website) combined with the data the customer provided (age, place, gender, etc.) into recommendation lists, which then even consider the purchase patterns of other “similar” customers. Recommendations  are a popular tool in social media and can be found at all kind of platforms and services (f.ex. friends or contacts in facebook/google+, people you may know in LinkedIn, recommended blogs in wordpress).
Another interesting social media structure Amazon uses is their rating system, which is based on customer collaboration (see the favorites section Crumlish and Malone) . The rating system consists of a star-rating-model (five starts as maximum rating) and the customer reviews, where customers give feedback, recommend and complain about the product. These reviews are depending on the customers collaboration and provide a deeper understanding of the product. This might be easier to explain with a quick example:
Imagine Anna would like to purchase a book with a vampire story, she then searches in the book section and popular books will pop up. Here Anna can see how many other people rated for this book and how many stars it received (see fig.1). The additional reviews provide personal comments by other readers, which might help Anna to decide if she wants to buy the book or not. It becomes clear that Amazon picks up on the pattern of word-of-mouth and the pattern of rating or valuing content. Here it is in form of a yellow star-model, in other platforms this is done by hearts (instagram) or thumbs up (liking at facebook).
As a last structure we focus on the seller/consumer feedback: Amazon has a complex process of giving consumer feedback on their products and services, especially concerning other sellers. By this step Amazon tries to maintain control over the negotiation of transactions and their customer relations. This structure can of course also be found at companies with a similar market (f.ex. Ebay). Underlying agenda in all three social media structures are the word-of-mouth approach  and user-generated content methods. If you are interested in the ideas behind word-of-mouth, I can recommend the book Buzz Marketing by Mark Hughes.
The named structures are just a glimpse into the complex market Amazon is working in, but it becomes clear that they are different social media structures at work. Those are mainly there for gaining trust, giving consumers assurance and in order to profit from the consumers input. I would say that the recommendations algorithm has the biggest social behavioural impact on customers. Even Amazon themselves describe their customers as global “shoppers, sellers, content creators, and developers” , hereby often obtaining more than just one role, so this makes amazon to a company worth monitoring.
- http://www.mediadb.eu/datenbanken/onlinekonzerne.html (accessed 2013-09-11)
- Wirtz, B. W. (2011). Medien- und Internet Management. 7., überarb. Wiesbaden. Gabler. p.585.
- Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.
- Swearingen, K., & Sinha, R. (2001, September). Beyond algorithms: An HCI perspective on recommender systems. In ACM SIGIR 2001 Workshop on Recommender Systems (p. 11).
- Crumlish, C., & Malone, E. (2009). Designing social interfaces: Principles, patterns, and practices for improving the user experience. Yahoo Press.
- Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business horizons, 52(4), 357-365.
- http://www.amazon.com/b?_ref=career_AA&node=239364011 (accessed 2013-09-11)
CC Attribution: coverpicture taken in 2008 by Robert Scoble (flickr)