Predictive Analytics Essay

3810 WordsMay 30, 201216 Pages
PREDICTIVE ANALYTICS: TEN ESSENTIAL STEPS Vishnu Prasad Predictive Analytic Models are built with 3 major objectives in mind. The first one is to understand new or unknown patterns, correlations and trends in the data in order to strategize on the new knowledge. Building customer profiles, market segmentation, identifying and estimating employee attrition probabilities etc. generally fall in this category. Once the knowledge is discovered through analytics, the strategization can take place and appropriate recommendations can be made for actions based on the results. The second objective is to create a set of rules that would make the decision making easier. The predictive models can be made a part of a business process application such as credit rating, fraud detection, risk analysis etc. Such models are known to be applied for automatic short listing of potential applicants for employment. Similarly, a predictive model may be integrated into a mortgage loan application to aid a loan officer in evaluating the application for a loan. Income tax and customs authorities are known to have implemented such predictive models to minimize their effort without sacrificing the revenue. Similar applications are made for monitoring credit card transactions in order to detect likely fraud. The third objective is to apply the model to different data sets. The model could be used to flag certain records based on their classification, or assign a score such as the probability of responding to a direct mail campaign or the model can select some records from the database and subject these to further analyses with another analytics model. This paper list 10 important Issues/steps that can help successful implementation of Predictive Analytic Models. 1. SELECTING THE RIGHT TECHNIQUE The selection of the right technique obviously depends on the objective

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