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Analytics off the shelf: Using commercially available tools now
Getting started in predictive analytics is arguably quite difficult. The tools used are not the standard statistical tools taught in most universities today. This article is not a course in predictive analytics, but it is a path through which to begin learning to use the tools effectively. The example of a firm attempting to reduce their churn rate is used as a medium for exploring one commonly used predictive analytics algorithm and its most commonly used diagnostic test statistic. A CART model is used to make predictions in two different manners: first, with purely numerical data, and secondly with added text data. The use of social media as a source of information to marketers has become quite useful and this example give the reader some insight as to why this is so.
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Barry P. Keating is a professor of economics and business analytics in the Department of Finance at the University of Notre Dame. Barry has also served on the faculty of Virginia Polytechnic and State University, as chair of the Notre Dame Department of Finance, as manager of the Journal of Public Choice, and as Salvatori Faculty Fellow at the Heritage Foundation in Washington, DC. Barry has consulted for Accenture, the Institute of Business Forecasting and Forecast X. As co-author of ‘Business Forecasting’ (McGraw-Hill), his primary area of interest involves the use of analytics in solving corporate supply chain problems and forecasting.