Sample Research Paper

Kotler (1997) defines customer database as an organized collection of comprehensive data about individual customers or prospects that are current, accessible, and actionable for marketing properties. A similar but broader definition, than this, is found in www.answers.com. These help in maintaining data about customer demographics, psychographics, and purchase habits. Various analyzes such as trend analysis can be performed on the information to discover unobvious patterns of customer traits and buying habits. Goods and services can be marketed to them accordingly. These are the advantages the dissertation focuses on. Barnes and Howlett (1997) state the same that the use of customer databases has become commonplace in many companies resulting in a close link between CRM and database marketing.

Furthermore, Payne (2006) supports that as business shifts from product-based selling to customer-based marketing after implementing CRM and the supporting systems. He asserts that it is logical and beneficial to have one repository for data, that is, a data warehouse.  The need for having one central data warehouse due to the evolving marketing continuum is also emphasized upon by Dwyer and Tanner (2002).

Baker (2003) agrees with the above-mentioned authors that a data warehouse contains data from all organizational functions. All the information is worked upon to perform analysis on customer behavior and characteristics.

A data mart is a new concept. Payne (2006) describes data mart as a “single subject” data warehouse implying it is not as grand in scope. A similar definition is given by www.learndatamodeling.com.  This states that “A data mart is a subset of data warehouse that is designed for a particular line of business, such as sales, marketing, or finance”.  This far more useful as customers can be directly targeted for marketing of goods and relationship building.

Antoniou (1997) gives a simple definition of data-mining stating that this “is a process of extracting hidden or previously unknown, comprehensive and actionable information from large databases”.

InNewton’s Telecom dictionary, data-mining is defined as a sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data. Edelstein (1997) agrees withNewtonon the “discovering capability” of data-mining pointing out that data-mining discovers patterns and relationships hidden in your data. He states that data-mining is part of a larger process called knowledge discovery, in which advanced statistical analysis and modeling techniques are applied to the data to find useful patterns and relationships.  Gonzale’s (2005) agrees with the above definitions but he explicitly emphasizes that the analysis of the data yields to better decision making explaining that data-mining uncovers new contexts and insights into customer behavior so decision makers can take action based on real knowledge rather than gut feelings.

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