| What is Data Mining |
"A new discipline lying at the interface of statistics,
data base technology, pattern recognition, and machine learning,
and concerned with secondary analysis of large data bases in
order to find previously unsuspected relationships, which are
of interest of value to their owners."
Hand, American Statistician. |
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| Experts Corner |  Communicating intelligently with existing customers is critical, and marketing spend is heavily weighted towards this pool. But businesses cannot afford to neglect prospecting activity. So, what techniques can businesses adopt to ensure that the search for new customers is both targeted and cost-efficient ?
David Jefferies, Marketing Director, Pitney Bowes
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| Highlights |
 Getting Off on the Right Foot: Avoiding Common Master Data Management False Starts
Companies wishing to start a master data management (MDM) project may be unsure where and how to begin. After all, MDM is a journey and success or failure at the first step either defines or dooms the further evolution of the project.
by Ravi Shankar, Director of Product Marketing
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| Abstract: Not all 45- to 55-year-olds with a household income between $50,000 and $75,000 have the same purchase interests and spending habits. For this reason, static demographic data should not be used as the building blocks of a well-defined customer segmentation system. Demographic data may be used to describe customer segments (profiling), but it is much less effective in distinguishing interests and spending habits than customer behavioral data. |
| Topics:
Customer Analytics | Data Mining | |
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| Abstract: Senior executives often try to gain competitive advantage for their organizations through massive investments in information technologies. But the promised payoffs of operational effectiveness, improved decision-making and bottom-line returns can be elusive. |
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Data Mining | |
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| Abstract: Those new to database marketing often have trouble distinguishing a marketing database from an operational database. When you finish reading this editorial, you will know the difference, and why it is important. |
| Topics:
Customer Analytics | Data Mining | |
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| Abstract: The discovery of poor data quality has alerted companies to the costs and risks associated with this problem. As a result, companies in every industry are beginning to investigate the extent of poor data quality within their organizations. This paper presents an overview of the major types of data deficiency, the costs and risks associated with each, and a simple guide to achieving and maintaining excellent data quality. |
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Data Mining | |
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| Abstract: One may claim that the exponential growth in the amount of data provides great opportunities for data mining. Reality can be different though. In many real world applications, the number of sources over which this information is fragmented grows at an even faster rate, resulting in barriers to widespread application of data mining and missed business opportunities. Let us illustrate this paradox with a motivating example from database marketing. |
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Data Mining | |
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