Customer data mining and predictive analytics at Target and Monster.com

Database Security Case Study

Database Security: Case Study

The objective of this study is to answer specific questions following have read the case study which is the focus of this work in writing including naming the concepts which are illustrated in the case study and why a customer database is useful for companies related in the case study under review. This work will additionally answer as to what would occur if the companies did not keep their customer data in databases. Secondly this study will determine the effectiveness of the SAS statistical modeling software that is described in the case study and will answer as to how better data management and analytics improve each company's business performance and provide two examples of improvement realized through mining customer databases. This study will additionally describe some of the weak points of predictive analytics and answer as to what management, organization, and technology factors are responsible for those weaknesses. Finally, this study will answer as to the purpose of Target's predictive analytics team in terms of the benefits of predictive analytics and answer as to whether there are any ethical issues raised by mining the customer database.

I. Concepts Illustrated

Concepts illustrated in this case include the concept of 'data mining' or the process of reviewing customer demographics, purchase history and other customer-specific historical data and using that data to predict future customer behavior. Business analytics is another concept illustrated in this case such as that utilized by Monster.com which is reported to be used specifically for the purpose of scaling "back its broad-based brand advertising in favor of a more targeted multi-channel approach." (p.243) It is explained that Monster.com realizes the majority of its revenue from "employers who pay to post job listings and to search its resume database." (p. 243) In addition it is reported that employees are able to search Monster.com's job database and post their resumes and do so without paying a charge. In the past Monster.com had a campaign that began with an email however, historically the messages sent were described as generic in nature relating information to potential users about the company and its services and generally to large groups or companies that were large in size. However, due to the economic downturn and the unemployment rates rising Monster.com is reported to have been "inspired to look for a more cost-effective approach." (p.243)

II. Usefulness of Customer Database and Result if Customer Database Information Not Kept Available

The usefulness of the customer database in the study under reviewed enabled the companies to use effective predictive modeling to predict customer purchase behavior in the future. If the customer database has not been kept, this predictive modeling would not have been possible.

V. Predictive Analytics Weaknesses -- Management, Organization and Technology Factors

The work of Zaman (nd) reports that analytical tools "enable greater transparency and can find and analyze past and present trends, as well as the hidden nature of data." (p.1) However, today's business organizations require more knowledge about the future and future "trends, patterns, and customer behavior in order to understand the market better." (p.1) Business intelligence vendors therefore, "developed predictive analytics to forecast future trends in customer behavior, buying patterns, and who is coming into and leaving the market and why." (Zaman, nd, p. 1) According to Zaman (nd) "Depending on an organization's needs, some predictive analytics tools will be more relevant than others. Each has its strengths and weakness and can be highly industry -- and model-specific -- the algorithms and models built for one industry are not applicable to other industries. Financial industries, for example, have different models than what are used in manufacturing and research industries." (p.1) Therefore, it is important to choose the proper business analytics tool that is industry specific. Zaman states that the capabilities that must be considered include "algorithm richness, degree of automation, scalability, model portability, web enablement, ease of use, and the capability to access large data sets. The more diversified the business, the more functions and unique models are required. Model portability is important even within different business units in the same company. The scalability of the solution and its ability to handle expanded functionality should also be verified and based on a business' growth." (p. 3) In addition, it is necessary that the right experts test the tools.

VI. Purpose of Target's Predictive Analytics Team and Benefits

Target has been able to use predictive analysis of customers through incorporation "scientific findings about habit formation." (p. 244) Target assigns each customer with a Guest ID, a code utilized for tracking everything the customer purchases and their use of coupons, credit cards, refunds, surveys, email responses, visiting the company's website or calls to customer service. The Guest ID is also complete with the customer's demographic information. In addition the company is able to purchase data about the customer's work history, ethnicity, subscriptions to magazines and even their political stance, contributions to charities and the automobiles the customer owns. The predictive analytics team pushes to increase the company's sales through sue of information concerning habit formation which indicates that 45% of the purchase made by customers are habit-based purchases.

VII. Ethical Issues Related to Mining Customer Database

There are various ethical issues that must be addressed when using data mining. For example, privacy and confidentiality are both important ethical issues. The ASA's ethics guidelines are reported to address "issues of the suitability and validity of methods used in any statistical applications including data mining." (Seltzer, nd, p. 1441)