Ereader Case Study Essay

It has recently come to my attention that Target Co. utilizes data mining to extract a wide spectrum of information about its customers by accumulating, analyzing and storing data about customer purchases. While I understand that this practice enables Target Co. to simultaneously deliver individually targeted advertisements across its diverse customer groupings, thereby increasing the potential for sales and improving customer retention, I also understand that large amounts of data unearthed by data mining can be manipulated to uncover hidden purchasing patterns to predict and shape future purchase decisions. Therefore, although there are significant benefits to using data mining, there are also serious costs associated with data mining that

It is needed to analyze customer behavior and preference data. To generate the necessary data and understand customers’ preferences, Harrah’s had to mine the data.

The retail industry has seen an increase in revenue as a result of their use of data analytics. The department store, Macy’s, implemented the use and

In this article How Does Target Know so Much about Its Customers? Utilizing Customer Analytics to Make Marketing Decisions, the authors describe the kinds of data needed to recognize changes in a consumer’s habits, privacy concerns that occur when data mining, and marketing decisions made from the information. Whenever shoppers make a purchase or browse their Web site, Target gathers customer information, analyzes it, and might sell the data to other businesses. In fact, all three authors explained, “Target Corporation is a leader in analyzing vast amounts of data to identify buying patterns, improve customer satisfaction, predict future trends, select promotional strategies, and increase revenue.”(Corrigan, Craciun, Powell 159). Along with other stores, Target has created a

It became an issue for consumer, and they felt uncomfortable due to companies invaded consumers privacy without their consent. Another issue regarding data mining is there no clear policies around how the company uses data once they have collected the information. After Target received negative publicity regarding its original data mining approach, they change their strategy by mixing all their ads, so it not that obvious that they were spying on them. It makes customers’ to assume that everyone else gets same coupons and ads. Data mining has given Target awareness into the relationship between product sales and online reviews. Target retailers identify guests how write reviews, examining their history of purchasing and invited to write review on their products. This program was highly successful, leading Target to obtain incremental reviews and increase sales of those key products. More importantly, data mining has empowered companies to find new opportunities for growth, make better decisions to succeed business goals and safe money. The retailer industry utilizes data mining and customer analytics to support customer decisions. Exploring how collected data can be manipulated to identify customers buying patterns and increase profitability is a growing business

Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information about all of its customers: their age, ethnicity, demographics, and shopping habits. The starting point will be a data warehouse containing a combination of internal data tracking all customers contact coupled with external market data

The government collects all kinds of useful information about our population. How many people live where, incomes, family sizes, ages, do they rent or own a home, and lots more demographic data that is free for the asking. Modern computer programs make possible for any company to take the masses of demographics and analysis segment populations. This has propelled data mining to the forefront of making customers relationships profitable (Ogwueleka, 2009). This will help Swan understand his customers better and find association between each segment. Customer have life cycle due in part to the time of year, so Swan can now structure his advertising and see results based on a better segment model rather than just counting customers. Data mining can also be used in customer retention applications identifying

Big data technology is used to help produce useful information out of the massive amount of data collected from retail stores. According to Dylan Love,

The book is all about the things that make one to be a smart consumer of cutting-edge analytics, facilitating to frame the judgement, questioning about the information and the procedure, operating to comprehend the consequences, and using them to progress results for his or her business. Even though it sounds direct, I directly acknowledged it as a deceptively single-minded set of purposes of the book. The writers planned the first half of the book about an analytics outline that entails of six stages: problem acknowledgment, evaluation of previous results, displaying, data assortment and data examination, and outcomes demonstration and action. This planned method to discerning about analytics is one of the most important notions that Davenport

I love reading, I always have and I always will. I love going to the books store and picking up a new book in a series or a standalone. Lately there have been other ways to read books. You no longer have a 20 minute car ride to go to the nearest book store; instead books are at the touch of your fingertips in seconds by a nook or kindle.

It is needed to analyze customer behavior and preference data. To generate the necessary data and understand customers’ preferences, Harrah’s had to mine the data.

I chose to analyze Amazon's webpage, specifically, their Kindle page. The Kindle is an electronic reader device that functions as both a tablet and a computerized version of a book. The website for the Amazon Kindle appeals to the nostalgia one has of reading books, as well as the tech-savvy advantages of the world we live in that seem to appeal to the majority of us. The design of the product is sleek, simple, and easy to use, while the website for the product is informational without being overwhelming. The Kindle and its website use devices like color, alignment, simplicity, similarity, and other convenient and helpful design aspects to give consumers all the joys of reading without the traditional limitations of a normal book. (Figure 1)

Dana and Gandy (2002) studied the technical and social concerns associated with using data mining techniques to create customer profiles that may exclude classes of consumers from the marketplace. Dana and Gandy’s (2002) study differ from Scott (2014) and Bond and Foss (2005) because it takes into consideration the specific use of data mining techniques employed by companies such neural networks, decision trees, market basket analysis, and clustering. The solution Dana and Gandy (2002) propose is to use more sensitive alternatives to the use of data mining. Dana and Gandy (2002) employ the use of hypothetical scenarios to illustrate how different data usages negatively affect people to simply a complex topic, which is similar to the persuasive devices used by both Scott (2014) and Bond and Foss

Data collected by a business includes internal data, such as financial or operational information, as well as external data, such as customer or website usage information. Properly analyzing and acting on this vast amount of data can transform the way companies do business and can become their biggest competitive advantage. Leaders of the organization no longer have to rely on their “gut instincts” to make key decisions, instead they will make decisions off historic data and will be able to more easily measure and track the effectiveness of those decisions.

Fragmented marketing touch points produce a dizzying array of data, from traditional structured data sources, such as retail checkout data and credit card transactions, to new structured data such as DVR data and the endless clicks and actions taken online, and access, to new unstructured data in call center logs and social media platforms (Nichols, 2013). All disciplines, not just marketing analytics, will need to contend with the data deluge and the various Vs of big data: volume, variety, velocity, and veracity (Vorhies, 2014). New models, skills, and machines are needed that can first, process petabytes and exabytes of data, and then attribute the data to the correct channel. Marketing analytics must marry offline and online touch points (Van Auken, 2015). This is an entirely new way to understand customers, and therefore creates entirely new ways to formulate and measure marketing strategy (Erevelles, Fukawa, & Swayne,