Information System Based On Data Mining Techniques

Customer profitability was a determinant used for segmenting and targeting, studies were done on customers’ likes, dislikes and types of products they would benefit from and models were developed to determine their propensity to buy.

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

In this article How Does Target Know so Much about Its Customers? Utilizing Customer Analytics to Make Marketing Decisions, authors describe data mining program, types of data needed to identify changes in consumer behavior, privacy issues that arise with data mining, and customer analytics support marketing decisions. Target customer behavior is tracked, analyzed, and possibly shared with other business, whenever shoppers make a purchase or browse on its Web site. In fact, all three authors enlightened, “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). Target

Many marketers agree that by reducing customer’s to competitors defection by only 5 per cent, companies can improve profits by anywhere from 25 per cent to 95 per cent. There is no question this will be a great advantage and could benefit any retailer. It is for this very reason why consumer’s relationship marketing and using tools such as loyalty scheme is

Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.

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

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.

As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.

Dana and Gandy recognize that data mining technologies have allowed firms to predict profitable customers and distinguish them from others that are less profitable which centers around the principles of customer relationship management similar to what Bond and Foss (2005) argues. However, Dana and Gandy (2002) also consider various technical problems related to data mining such as missing data, erroneous data, and errors in the data collection method to consider their relation to discrimination by asking, “if particular classes or populations of people more likely to have missing data associated with their records?” The key argument that Dana and Gandy (2002) make is that data will never truly represent a complex, and autonomous individual, whereas Bond and Foss (2005) suggest companies can use data to understand the intricacies of human thinking even if the consumer is unaware of it themselves. One of the downfall of Danna and Gandy’s argument is that it tends to ignore the benefits of data mining altogether (2002). Dana and Gandy (2002) discusses discrimination of customers through data mining tools, which is extended by both Bond and Foss (2005) and Scott

Customer Relationship Management (CRM) software can help VTBC manage customer data to determine buying habits, coordinate its marketing strategy, forecast product sales, and interact quickly with customers. Vermont Teddy Bear Company’s marketing strategy is aimed at gift sales for Valentine’s Day, Mother’s Day, and Christmas. Their marketing strategy is ineffective during off peak times. Vermont Teddy Bear Company needs conduct market research and revise their marketing campaign to capture a broader customer base than just holiday gift shoppers. There is a need for a data warehouse to store the collected data and for data mining. Data mining can provide information on customer buying habits, trends and target potential

In this assignment, we will explore RFM segmentation, a technique used to group customers according to their aggregate purchase history with a company. We specifically look at how recently customers have purchased (R – recency), how often they have purchased

An example of how data mining is conducted and used to benefit business can be explained in the following scenario:

Wal-Mart’s advanced data-mining tools allow them to fine tune and improve customer responsiveness, giving customers what and when they want in offer. It can be compared to

Easily customisable, CRISP-DM process model was used to implement this data mining project. With respects to this methodology, this study suggests to identify high value customers and to profile the customers using customer segmentation techniques and by effectively utilising data mining methods. There are mainly four phases for this analysis. Figure 3.1 illustrates the research methodology of this study.