customer shopping, assertions and otherwise is nothing new. However, the manifestations and degree to which this data is used in the modern marketing sphere is much more substantial and significant than it used to be. Just one example of a firm that does this is Amazon. This report shall explain what data that company uses, the additional data that they might attain from other vendors, the specific insights or information that is extracted from the raw data, how the analysis of the data translates into decisions and so forth. While Amazon has to be careful when it comes to the depth and breadth of how they use the data they get a hold of, there is no question that the use of that data is the core of their creation and sustaining of their business.
Analysis
In the grand scheme of things, Amazon has not been around all that long. Similarly, the use of “big data” and information systems as a significant tool (if not the main one) to market products and analyze data is also fairly new. It is fitting, thus, that Amazon has jumped to the stratospheric heights that it has reached in large part because of how they collect, harness and use the data that they collect from their customer. The data that they acquire and track would include what products sell well, which ones do not, on-time performance in terms of shipping, what product purchases correlated with each other, what consumers look at, what they actually buy, whether they “bite) (and to what degree) when it comes to cross-promotion of similar or related products, and so forth. Basically, Amazon is collecting and amassing a huge amount of data on what happens with individual products and what is happening with individual buyers and they make an effort, usually successfully, to mesh and meld the two when it comes to selling products and keeping their sales figures high (Madden, 2012).
As for what data might come from outside vendors, there are many sources and places that Amazon could look. However, they need to be efficient and careful as spending too much time looking at the wrong things can lead to wasted motion and loss of opportunity. While what is happening in their own midst is important, Amazon needs to be mindful of what other firms are selling, how they are selling it, for what price they are selling it and so forth. For example, Amazon would be very mindful and attentive to what their competitors do. Just a few of these would include Walmart, Target, Wayfair and Fingerhut, just to name some of them. As one might notice, Amazon is competing with brick and mortar firms like Best Buy and Walmart but they are also competing with online vendors like Zappos, Wayfair and others. Beyond that, the ground-based stores like Walmart also have online portals and shopping venues, if not extensive ones, and thus Amazon is competing with businesses that operate on two major fronts rather than the online-only nature that typifies most of Amazon’s business. However, Amazon is starting to delve into the ground-based sphere in several forms and they would be wise to keep an eye on what Walmart and other firms are doing in response. As one might expect, Amazon certainly would be paying attention. There is a limit to what can be observed and tracked as Walmart and other firms are not going to report data and outcomes that are not required by law. To be fair, Amazon does pretty much the same thing. The key for Amazon is to collect sufficient and proper amounts of industry data so that they remain competitive with, or ahead of, with their competitors and what they are doing (Taylor, 2015).
When it comes to the insights and conclusions that can and should be drawn from the voluminous amounts of data that they take in, this has already been touched upon to some degree. For example, Amazon makes a mint off of cross-marketing products that are related and/or that can be offered as a bulk purchase. For example, if someone is looking at buying Hotel Transylvania 3, then Amazon will surely market the first two movies in that franchise as well as other animated movies that have come out recently such as Minions, the Secret Lives of Pets and so forth. Indeed, there is usually a strong correlation when it comes to sets of products and this can serve as a guide for what can be cross-promoted and what might not be as effective. Indeed, it makes perfect sense to cross-market ping-pong balls and red Dixie cups to a college student because beer pong is a sport for many people in that age group (Moylan, 2016).
As for how all of that data translates into decisions, the general idea is to maximize what the person buys on the site and this is facilitated and made possible by Amazon in a way that is methodical but not over the top. Indeed, if the aforementioned person that wants to buy Hotel Transylvania wants to buy all three, they will. However, they might not be interested or they might already have the first two installments. Either way, Amazon makes it easy to buy them as a set but it also makes it easy to just buy one of the three and/or other items. So long as Amazon makes it a point to continually monitor what each customer looks at when it comes to the single person and macro levels, the system that is their website can be attuned to market the products that will help maximize sales (Chaffey, 2014).
Conclusion
In the end, it is clear that Amazon is one of the experts when it comes to suggested complementary products and so forth. Further, there are many other websites out there such as Wayfair and the like that use the same or at least similar methodology when it comes to selling wares online. This can be done, and is done, in a way that is so much more efficient and real-time than can be done in a physical store. Between that and the fact that many people use the physical stores as a way to touch and feel the product and then they use Amazon or Wayfair to buy the product, Amazon comes out smelling like roses.
References
Chaffey, D. (2014). Amazon.com case study – Smart Insights Digital Marketing Advice. Smart Insights. Retrieved 5 August 2016, from http://www.smartinsights.com/digital-marketing-strategy/online-business-revenue-models/amazon-case-study/
Madden, S. (2012). How Companies Like Amazon Use Big Data To Make You Love Them. Co.Design. Retrieved 5 August 2016, from http://www.fastcodesign.com/1669551/how-companies-like-amazon-use-big-data-to-make-you-love-them
Moylan, B. (2016). Gawker.com. Retrieved 5 August 2016, from http://gawker.com/5842230/drug-stores-finally-marketing-to-the-beer-pong-and-drunken-hook-up-demographic
Taylor, H. (2015). Amazon tests status quo with aggressive big data play. CNBC. Retrieved 5 August 2016, from http://www.cnbc.com/2015/10/07/amazon-super-powers-web-services-with-data-analytics.html
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