Data Mining In Tracking Customer Behavior Patterns Essay

Data Mining In Tracking Customer Behavior Patterns - Essay Example The computing power is increasing at the rate specified by Moore's law, doubling every eighteen months. The technology upgrade to parallel processing has vastly contributed to more powerful machines. There have been a number of statistical applications and algorithms that were waiting for larger computing power to arrive. Data mining makes use of these algorithms to enable data mining possibilities. In addition to these, data is being collected on a very large scale at all levels. More the data better the data mining exercise has been the watchword of most of the work that is carried out. All these combine to make data mining. Using this data and applying appropriate models, the results of the data mining is obtained. This would enable businesses to identify buying behavior patterns from customers; identify customer demographic characteristics and predict customer response to emails. Most of the cases, both commercial and scientific establishments report a condition where there is a large quantity of data which is collected and stored. But there is hardly any information for the people to make use of. In its basics, the data mining efforts start with employing appropriate data models that would help in understanding the system and its behavior (Hand D J, 2001). This would further help in augmenting the nature of work executed and the future of the object becomes more predictable. This is possible to do only if the object is understood well and the modeling is realized to the closest possible accuracy. A number of modeling tools help in data mining. Typically, Decision Trees, Rule Induction, Regression Models and Neural networks. All these contribute to extracting needed data from the databases using data mining tools. These are not simple straight forward SQL statements. Qualitative analysis is possible with the predicate data that would use this to identify and get an objective visualization of the object being modeled. Whereas in quantitative analysis, the data is used for automatic processing based on specific input data or time. Based on the model the information and data available in the system are extracted to meet the requirements. In the case of the banks, this would help them in identifying and detecting patterns of fraudulent credit card usage. The banks might like to identify loyal customers and those who might change their loyalty even with a minor issue. It also helps in identifying credit card spending by customer groups and finding any specific correlation between different financial indicators.