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Integrated Marketing Communications: Amazon.com

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on April 29, 2007

in College, Marketing, Technology and UMass Amherst

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The goal of this essay, for the Fundamentals of Marketing class at Umass-Amherst, was to “Explain what it means to have “integrated marketing communications”. Include issues such as how to measure results. Pick a company and describe how you view their use of Integrated Marketing Communications…”. I’ve yet to receive a grade for this paper, but will update this post once I do (update 04.30.2007: I received a grade of 10/10 for the below essay).

Integrated Marketing Communications: Amazon.com

In today’s world, a multitude of communication tools exist to connect people and promote ideas, services and goods. Marketing departments and companies must recognize their message will be transmitted via multiple channels, even if it is not their intent to do so. If a company has a particularly compelling or comedic video ad, it will make it’s way to youtube.com, where it can be viewed repeatedly and stored permanently. If a company makes a typo in a print ad or conveys an unintended connotation, bloggers will find the advertisement and critique it to no end. As a result, companies must practice and optimize a process called integrated marketing communications, through which all contact points an existing or potential customer interacts with are kept consistent and relevant to the targeted customer.

In essence, integrated marketing communications [IMC] focuses on controlling the messages that a company releases to the public to ensure that these messages do not conflict with each other and thus weaken the main message the company wishes to distribute. In other words, rather than having unique advertising departments for Europe and North America, companies now fuse the two together or at least encourage the two departments to communicate with each other. Companies using IMC also focus on providing each different customer base a message that is relevant to them. Thus, using the previous example, if government regulations force a product to be different in Europe and North America (for instance, cell-phone network standards) then the companies must ensure that each group receives the proper message, while also ensuring neither group would feel disadvantaged if seeing both messages (e.g., if an American saw the European version of an ad for a product with better features in Europe).

Not surprisingly, technology companies are very keen on using integrated marketing communications. For instance, Amazon.com will provide users a personalized homepage with products relevant to previously purchased products, send out e-mails for deals or new products that have some similarity to previously bought goods, and offer bloggers an opportunity to list their wish list or reviewed books on their blog. Public relations is a huge aspect of Amazon.com’s success. The site allows every user to post reviews on products they’ve bought, thus building a database of reviews and adding credibility to Amazon.com. If someone sees 500 positive reviews for a book on Amazon.com, they are much more likely to purchase that book than if they go to a site with no reviews and have to figure out on their own if it is worthy of their money. Bloggers are also a huge source of publicity for Amazon.com; for instance a blogger may review a book on their blog and then post a link to the Amazon.com listing for that book, thus giving a positive review to not only the book but also Amazon.com as a place to buy that book. I myself have given Amazon.com publicity via a post on my blog advising people of the various ways to save on college textbooks, such as searching for international versions or used copies on Amazon.com. Of course, the company cannot control what people say about them on their personal sites, but most references to Amazon.com are simply links to products, thus generating traffic without an opinion on the site itself.

A potential issue for Amazon.com is the potential for users to end up on a site discussing or linking to Amazon.com when the user actually was attempting to arrive at Amazon.com. The company must practice Search Engine Optimization (SEO) to ensure that users can easily find Amazon.com, when that is their purpose. Still, users arriving at a blog or shopping portal would still potentially locate the link to Amazon.com, thus alleviating this issue. A greater issue is a user may become confused and assume the site is related to Amazon.com, unfortunately there does not appear to be much the company can do to prevent such mistakes, fortunately, most internet users tend to be educated on such matters.

Another aspect of integrated marketing communications is how the company treats their employees and potential employees. If an employee is dissatisfied with a company it will hurt the company in two ways; for one, consumers may become discouraged from buying that company’s products or services, thinking that if the employee is unhappy then the company must not be producing a good product, and secondly the company will have a poor public image which will increase the difficulty of recruiting new talent to the company. I personally know that Amazon.com has excellent human resources as one of my good, close friends had applied for a position there and had his flight to the interview and accommodations paid for. If he had accepted the position, Amazon.com would have hired someone to help him pack and ship his personal items to Seattle from Boston as well as paying for his flight and accommodations while he found a permanent place to live there. Treating potential employees so well ties into Amazon.com’s overall marketing strategy of providing consumers everything they need to make a purchase decision as well as providing a plethora of options to get their purchased product quickly.

Amazon.com can measure their results in a number of ways. As they are an online marketplace, most of their consumers arrive at Amazon.com either via a search engine, shopping portal, blog, or have previously used Amazon.com. Thus, Amazon.com can utilize a number of webmaster tools to track it’s visitors, such as whether a user has been to Amazon.com before, has made a purchased there before and from where the user is coming from (e.g. Google or Yahoo search or a review site with a link to Amazon.com). When the Harry Potter books were being released, Amazon.com decided they wanted to be the #1 stop for consumers looking to buy a Harry Potter book. To put this plan into action, Amazon.com set up specific sections on their site to lure Harry Potter fans to Amazon.com and teamed up with the book’s publisher as well as many other online sites, such as review sites, in an effort to lead consumers to Amazon.com. The effort was successful as Amazon.com became the #1 site on the internet for Harry Potter books and other related items.

With the increasing ease of publishing content and re-distributing existing content made possible by the growth of digital technologies and the internet, marketing departments are required to ensure their company’s message is interpreted in the same manner by a vast audience. As more and more methods are introduced for distributing content, marketing departments will have to focus on producing a consistent and similar message at its’ source, thus no matter how many times it is re-distributed, the message will hopefully be interpreted the same way by consumers.

One Way Analysis of Variance (ANOVA)

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Adam Pieniazek

on April 28, 2007

in Statistics and UMass Amherst

. 4 Comments

The requirements for the fourth written response for my Statistics II course at UMass Amherst was: “Explain in your own terms the test for analysis of variance. What does an ANOVA test? Why is it useful? On what type of data would you apply it? What is the logic behind the test? Why does it work?” I have yet to receive a grade for this assignment but will update this post once it is issued. Here is my answer:

An ANOVA test, is in essence a method to determine of three or more population means are equal. It is useful because there are many scenarios for which we do not want to know the exact difference between population means, only if they are equivalent. It is also a relatively simple method to calculate if the population means are roughly equal or vastly different. The ANOVA test requires that the populations being tested are normally distributed, have equal variances and that the samples are independent of each other.

The ANOVA test uses the variation between samples within a category and between categories. For instance if we’re testing whether golf ball A, B and C travel the same distance, the ANOVA test utilizes the differences between the means of A, B, and C and also the differences in means of the samples within A, B, and C. The ANOVA test basically compares these two variations (between categories and within categories) and if the variation between categories is relatively high compared to the within categories variation, then the ANOVA test will lead us to reject the null hypothesis (that all population means are equal). This works because if the variation within categories is fairly clustered and the variation between categories is fairly spread out, then the categories cannot, logically, be equivalent, as the within variation shows that each sample is following some pattern.

Furthermore, the ANOVA test is more reliable than using three separate (for instance hypothesis) tests, as three unique tests will compound the confidence level, thus decreasing our confidence in the test.

Upgrades & Cleanup

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Adam Pieniazek

on April 27, 2007

in Misc.

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There’ll be some changes to this blog in the coming days, mostly in the Wordpress theme and general layout of the site. I’ll be re-arranging and consolidating the categories you see in the side-bar to the right, as well as fixing a few things behind the scenes. If anyone has any suggestions, complaints or ideas, feel free to contact me. My e-mail address, AIM screen name, as well as several other points of contact (facebook, myspace, digg, etc.) are listed in the About section or you can just post a comment if you don’t want to bother with those communication methods.

So if you see a feature go missing over the next few days or see a different look to the site, don’t panic!

Central Limit Theorem

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Adam Pieniazek

on April 26, 2007

in Statistics and UMass Amherst

. 0 Comments

The description for this assignment is as follows: “Given a non-normally distributed population such as the bimodal population which is pictured in figure 6-8, discuss and explain how such a population can have a frequency distribution of sample x-bars as shown in figure 6-9. How does Figure 6-8 relate to Figure 6-9 and then how does figure 6-9 relate to 6-10? Explain what concept is being demonstrated. In short write an explanation of how we move from figure 6-8 to 6-9 to 6-10.” This assignment was the second written response assignment for the Statistics II course (Quantitative Tools for Management) during the Spring 2007 semester at the University of Massachusetts at Amherst’s online program; I received a 5/5 for the below answer:

Looking at figure 6-8*,

we see that the x values with the highest frequency are 10 and 18, the lower and upper limits of the x values, respectively. This population is not exactly symmetrical but is close to being so, as it closely resembles a “U” shape. Figure 6-9* shows the distribution of the average mean of 3 x values chosen at random, 3000 times.

Even though 10 and 18 are the most common x values in the population, there are only a few average mean x values in the distribution in figure 6-9. In order to have a sample mean of 10 or 18 all three x values in a random sample would be to be all 10 or all 18. Thus the probability of choosing three 10’s or three 18’s in a random sample is quite low, thus why the distribution for 10 and 18 is so low in figure 6-9. Moving to the middle of figure 6-9, shows a rise in the number of occurrences of the sample means ranging from 13 to 16. Again, this makes sense because there are many more ways a sample mean could be in the 13 to 16 range and thus would be more commonly chosen in a random sample. Since an increasing amount of sample means will lie in the middle of the range, the standard deviation will be lower than the total population, as a higher proportion of the values will be closer to each other; whereas a high proportion of the values for the population in figure 6-8 lie at the upper and lower limits of the range, thus increasing the probability that the deviation between any two randomly chosen values will be higher.

Looking at figure 6-8, an eyeball estimate would lead me to say the median for this population would lie somewhere between 13 and 15. Figure 6-9 is showing that for 3,000 random samples of size 3, it is more likely the average mean will be close to the median than at the upper or lower limits [in this example, the median is equal to the mean of the total population, this is not always the case and when the median and mean are not equal the middle (and highest point for a high sample size) of the distribution in figures 6-9 and 6-10 will approach the mean].

By increasing the sample size to 10, and thus increasing the reliability and accuracy of the results, figure 6-10* is showing that as more and more x values are included in the sample, the sample mean will approach the population mean because the chance of picking ten x values that average out to be similar to the population mean is higher than in a sample of three. Since the likelihood of ten random values equaling the population mean is higher in figure 6-10, the population mean is the value most often represented in the 3,000 random average means. Likewise, since the likelihood of the average mean of ten random values being equal to or close to the upper or lower limits is low, these values are either not represented or much less so than the population mean. The principle behind figure 6-10 is that if 3,000 random samples were taken, with a sample size close to or equal to the population size, the average means would all come out close to or equal to the population mean, the proximity of the sample size to the population size determines the range of the distributions we see in figures 6-9 and 6-10 and increases (if sample size is not close to population size) or decreases (if population size and sample size are close or equal) the deviation between values. If the sample size was close to or equal to the population size, the standard deviation would be close to or equal to zero (as most of the average means would be equal to the population mean).

The idea being shown through these three figures is the Central Limit Theorem, which in essence states that as a sample size increases, so does the resemblance of the sample distribution of the average means to a normal distribution (e.g. the shape shown in figure 6-10).

*All graphs are courtesy of Course in Business Statistics 4th Edition by David F. Groebner, Patrick W. Shannon, Phillip C. Fry, and Kent D. Smith

Adam Pieniazek

Archive for April, 2007