Abstract This paper describes a business research problem on a theoretical business, the methods for testing the hypothesis, the data collection method, sample size, assumptions, levels of measurement, and the descriptive statistics that will be needed. The study uses statistical datagathered from initiating research in a business, examining problems related to customer retention in a retail environment. The business is a large chain of bicycle shops in the Northwest United States.
The Business Research Problem
Methods for Testing a Hypothesis
Data Collection Method
Sample Size
Assumptions
Levels of Measurement
Descriptive Statistic Needed
From the Paper "The business research problem is how to retain first time customers and bring back former bicycle shop customers. The bicycle shop carries a database of 6,000 total customers. Of the 6,000 customers, 2,000 are current customers. The bicycle shop's new customers are approximately 23 per week. The percentage breakdown for all categories is 65% men, 35% women. A current customer is someone who has used a bicycle shop service in the past six to eight weeks. A former customer is someone who has not used any of the services offered in the past year. The hypothesis to be tested is as follows: New customers and former customers will be retained and visits increased by mailing customer service oriented surveys. Through mail-in surveys over a period of time, analysis will be able to prove if this is correct."
Abstract This paper explores the kind of background and knowledge an accountant requires to be an effective internal consultant. The author explains that datagathered by accountants is not only used internally but also externally. The paper relates the ways in which information gathered by accountants at Marmon and other corporations is used by individuals and organizations outside of the company.
From the Paper "There is still a role for external consultants as they provide flexible resource of manpower and specialist expertise that an organization often lacks. Internal consulting offers a new model for many highly skilled accountants whose abilities are under-utilized. A common failure of internal auditors becoming internal consultants is that the company and the consultants both expect their job duties to be driven by functional department manager they are assisting. However, successful consulting demands a proactive approach where the relationship is between equals. Consultants often must challenge the d
Tags: Accountants, Marmon, internal consultants, Datagathering, internal and external customers.
Abstract Data mining has become a very important concept today and is used by companies all over the world to increase their profits and target the right market. The paper talks about the different aspects of data mining, tools used, and future trends in data mining. Data mining benefits are discussed in detail, and an entire discussion related to the trends in data mining is presented.
1-Background
2-Introduction
3-Data Mining Growth and Tools
4-The Data Mining Process
5-Data Mining Market Place Trends
6-The Data in Data Mining and Meta Data 7-Types of Data Mining Problems
8-Privacy and Ethical Sensitivity in Data Mining Results
9-Future Prospects of Data Mining
10-Works Cited
From the Paper "Data, particularly in the vast diversity and immense quantity that it is available to modern business, was till recently almost very hard to find and understand. Yet, the comprehension of data is the most crucial step to extracting the knowledge that it contains. The scenario has drastically changed today where data is much more easily available and has become more "meaningful" with the utilization of Data Mining. Today, technology offers business managers powerful new tools for gleaning knowledge from data-the essentials of data mining. Data mining has become increasingly important to mainstream companies to become more competitive both in their workings and their customer based relationships. Data mining, as such is of great interest because it is imperative for organizations to grasp the competitive value of information contained within their data repositories. There are a number of pertinent benefits of data mining. First of all, data mining provides the tools and techniques that are essential for optimization of customer relationships. Secondly, data mining provides an automatic method of discovering patterns in data. Thirdly, but not the least, data mining tools can identify the relationships that are actually present in historical data."
Abstract The paper discusses how the many advances in data and text mining are already revolutionizing the librarian profession. The paper explores how the ability of data mining tools to extract, transfer and load (ETL) massive amounts of data at a single time, is changing how all tasks in an organization get completed.
Outline:
Executive Summary
Content Integration Is Key
Data Mining
i) Principles of Data Mining
ii) Data Mining Timeline
Data Mining Implications for Librarianship
Text Mining
i) Text Mining Timeline
ii) Data Mining versus Text Mining
iii) Mining Blogs: An Example of How Text Mining Works
Text Mining Implications for Librarianship
Conclusion
From the Paper "At the intersection of text mining, linguistic analysis, statistical analysis, and latent semantic indexing techniques (Wikipedia Latent Semantic Indexing 2006). is the future of text mining that has the power to discover and report trending in highly unstructured content. At the center of text-mining's' rapid growth is the increasing sophistication of Natural Language Processing (CRM Buyer 2005). IBM and their significant research efforts in natural language processing are well documented on their website, as are the efforts and investments Microsoft is making."
Tags: latent, semantic, indexing, clustering, hosted, applications, Island, Data, Attensity
Abstract This paper relates that the use of data mining, its adjunct technologies for text mining and the ability to interpret, analyze and create linguistic models from unstructured content is revolutionizing the concept of data mining away from being purely used for structured content in data warehouses to now encompass unstructured content found throughout organizations globally.
The paper then provides insights into various areas of data mining, and the currently high levels of growth analytics use and applications software are experiencing as a result.
Outline:
Executive Summary
Using Data Mining in Business Research
Exploring the principles of Data Mining in Business Research
Predictive Methods in Data Mining
From the Paper "A second predictive approach is called deviation detection. The purpose of this method is to discover the most significant changes in data from previously measured or median values. An example of the type of use for this predictive approach would be the development of strategies for selling tickets to frequent flyers who booked months in advance versus those that consistently book within a few weeks of their departure. A third approach to using data mining to predict future outcomes is using the classification approach, or technique. This predictive approach of classification uses a collection of records (training set) -- each record contains several attributes, one of them is the class (Ng & Han, 10). The task is to find a model for the class attribute as a function of other attributes, so, after that, previously unknown records can be assigned a very accurate class."
Abstract The paper explains that data mining is a process whereby enterprises or organizations in any industry approach their respective data and databases in a more constructive and targeted manner to produce actionable business strategies. As some researchers have observed, data mining and data warehousing are becoming more prevalent because of the large quantities of data stored in various systems and the number of business decisions made based on the data.
From the Paper "Thus, data mining and data mining techniques have risen to prominence with the elevation in importance of databases and, more recently, the development of data warehouses that have changed the complexion of industry in all sectors. Data mining and data warehousing solutions have been especially important in customer relations management (CRM) and in the healthcare industries for example."
Abstract This paper describes that data structure is a way of storing data in a computer so that it can be used efficiently. The paper then goes on to describe how computers store the said data in terms of binary data presentations using Boolean logic. Furthermore, the paper describes data in the form of bits, along with converting binary data into decimals. Lastly, the paper talks about a computer's physical memory, which is based on one of two systems: (1) Random access memory (RAM), or (2) Read-only memory (ROM), and goes on to talk specifically about different coding systems.
From the Paper "Data directly supported by CPU are called primary data type or machine data type computers. CPUs also process complex data type such as string, array, text files, databases, and image data such as MP3, jpeg, and mpeg. However, 64-bit and the 128-bit use different math functions in order to maintain portability. In each case, there is a signed and unsigned integer type associated with each. Excess notation is a format that is used to represent a signed integer and represents numbers in order and at the transition point; the high-order bit is set at zero. This represents the excess number. Positive numbers are above in order, negative below (Burd 78). Zero represents the excess identifier therefore; the excess 16 notation shows the value for zero is the bit pattern for 16 that is 10000."
Tags: RAM systems, data structure, software, boolean logic, memory
Abstract This paper explores data warehousing in terms of data mining with intelligent agents such as bots and ants and clarifies the ethical dilemma posed by the use of such data.
From the Paper " Data warehousing is no longer simply a storage system for data. Today's data warehousing involves innovative technological software, automated agents known as intelligent agents robots-or bots and ants. These agents ..."
Abstract This paper considers key factors regarding data warehousing. It looks at the goal of data warehousing and the differences of data warehousing and relational databases.
From the Paper "Data warehousing is particularly popular in environments which have complex data requirements and a broad spectrum of data types contained in its database. The goal of data warehousing is to take full advantage of the power of hardware to contain large quantities of data and use the databases to manipulate that data. Although not yet implemented across all computing environments data warehousing is becoming popular as hardware becomes more powerful and cost effective..."
Tags: distributed data warehousing systems, data warehouses
Abstract This paper discusses three articles on data collection and analysis tools and their applications. This includes data mining, data warehousing and software packages used in the collection. This paper also analyzes the needs of the business upon which the correct data collection and analysis tools are selected.
From the Paper "Business today has more and more need for external consultants to use data collection and analysis tools in order to make assessment of business operations and processes. Many of the methods used today are computer-based, including software that does much of the job but still requires an able human operator to make decisions and input the correct information. Various analysts have made assessments of these methods to see how they are used and how effective they may be. Such tools are also used for analyzing performance in education, for assessing public programs, and for other tasks requiring a decision as to the value of a program or process. Bielski (2001) discusses the use of CRM, or Customer Resource Management system, which is used to track customer purchases while providing access to customer information using the computer. "
Abstract This paper examines data mining in e-commerce and discusses the various types of modeling used to make the data meaningful to e-tailers. The advantages and pitfalls of data mining and an explanation of how it has transformed e-commerce are detailed. The paper includes an abstract and table of contents.
From the Paper "Data mining as applied to e-commerce is a breakthrough technology that can gather information in an automated fashion and build models used to predict customer purchasing decisions with remarkable accuracy ..."
Tags: e-commerce, Internet, data mining, personalization, logistic regression
Abstract This paper explains the general concept of data warehousing . The paper explores the use of data warehousing at Humana, Inc., a giant healthcare company. The paper explains the strengths and weaknesses of data warehousing at Humana.
From the Paper "Humana Incorporated is a healthcare company with ... billion in revenue and stakeholders that include medical professionals, employees, corporate clients and other agents. Until early ..., the company had separate databases for various parts of its business making it difficult to understand the large amounts of data that was being generated by the organization and even making it difficult for healthcare providers to have access to all appropriate information on occasion. As a result, the company developed a data warehouse that uses two discrete data ..."
This paper discusses the use of data mining, a technique using sophisticated computer software to scour a company's database looking for specific management information.
Abstract This paper defines data mining to help senior management to manage and direct the company more effectively. The author points out the types of data. The paper reviews methods of data mining including the software.
From the Paper "Domestic and global business competition is fierce. Companies must look for new tools to give them a competitive advantage. Technology has added an additional layer of competitive tools commonly called data mining technology or data mining software. For many firms seeking to improve their competitive edge, the ability to perform data mining is rapidly becoming a necessity rather than a luxury. Data mining involves the extraction of hard to find information from large databases that companies normally maintain. Data mining tools can help businesses predict future ..."
Tags:data mining, techniques, tools, models, practical applications, competitive advantage, public and private sector applications, widespread acceptance
Abstract This paper discusses the realities of a new company in the data warehousing/data services industry and the exigencies of thriving in this field. The types of database products, services and supporting infrastructure are discussed as well as business processes and market requirements. The corporation as a business entity is also discussed in terms of its use and implementation of current and emerging technologies, change management techniques and the Internet as a tool and device.
From the Paper "PanData is a data intelligence business concentrating on the data services industry: warehousing, intelligence, customer relations management (CRM) and list generation. PanData amasses data on the Retail & Foodservice Industries across the North American continent. It has over 70k unique companies in its database. The collected data consists of the following data elements: company contact information, personnel--CEO to mid-level management & buyers, trade areas, products, franchise information, parent companies, locations--geo-codes and addresses, market share information, technology related information--POS hardware/software, scanners, software systems, servers (corporate and in-store), databases/data warehouses, communications and connectivity, EDI, RFID, and Wifi. The types of data are considerable and this list is not all-inclusive. PanData envisions revenue in excess of 10m annually and this revenue is PanData's long-term goal. "
Abstract The paper discusses how data mining was employed to customize offerings for goods and services to the paper's author during recent weeks. The paper explains how the avalanche of marketing materials received as a result of data mining can be traced back to many everyday activities. The paper therefore shows that by employing data mining techniques of tracking customer transactions and analyzing the statistical models for anticipated behavior, companies are able to deliver timely, pertinent, and coordinated messages and value propositions to customers and prospects.
Outline:
Introduction
Transactions
Conclusion
From the Paper "One method for helping a company decide how best to select and interact with customers is to collect and analyze vast amounts of information about people's preferences. The detection and use of statistically relevant patterns to build models that predict customer behavior is the process of "data mining". For a data mining process to be successful, database marketers must first identify market segments containing customers or prospects with high-profit potential. They then build and execute campaigns that favorably impact the behavior of these individuals (Thearling, 2001). This paper will discuss how data mining was employed to customize offerings for goods and services to the paper's author during recent weeks."
