Analytics: Putting It All to work

Many companies are flooded with huge amounts of data available in corporate databases and/or data warehouses. A key challenge is how to optimally manage this data overload and use analytics to better understand, manage, and strategically exploit the complex dynamics of customer behavior. This class starts by giving an overview of the steps involved when working out an analytics project in a practical business setting. After discussing the key data preprocessing activities, this course elaborates on how you can efficiently use and deploy both predictive and descriptive state-of-the-art analytics to optimize and streamline your strategic business processes such as marketing campaigns and/or risk management. Examples of business applications that are covered include credit scoring and risk modeling, customer retention and response modeling, market basket analysis and cross-selling, customer lifetime value modeling, and Web intelligence and social network analytics. You receive extensive practical advice and guidelines on how to put all the analytical tools and concepts to work in a real-life setting. The class focuses on analytical concepts, techniques, and methodologies and their applications. Software demonstrations illustrate and clarify the concepts, but no hands-on use of software is included. The class includes self-study sections with additional real-life case studies.

Presented by Bart Baesens

Bart Baesens, Ph.D., is an assistant professor at K.U.Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). Bart has done extensive research on predictive analytics, data mining, customer relationship management, Web analytics, fraud detection, and credit risk management. His findings have been published in international journals such as the Machine Learning Journal, the Management Science Journal, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, and the Journal of Machine Learning Research. Bart has presented at numerous international conferences, and he is also co-author of the book Credit Risk Management: Basic Concepts, which was published in 2008. Bart regularly tutors, advises, and provides consulting support to international firms on data mining, predictive analytics, and credit risk management policy.

Learn how to:

  • develop high-performing analytical business models using state-of-the-art analytics and data mining
  • get more in-depth knowledge about your customer equity using analytics
  • optimally prepare and enrich your data as a key ingredient to powerful analytics
  • predict customer behaviour using regression and decision tree approaches
  • describe customer behaviour using association rules, sequence analysis, and clustering
  • use social network data and analytics to better understand and manage collective customer dynamics
  • put analytics to work in a practical business setting.

Who should attend

Business analysts, senior data analysts, quantitative analysts, data miners, senior CRM analysts, marketing analysts, risk analysts, analytical model developers, online marketers, and marketing modelers in the following industries: banking and finance, insurance, Telco, on-line retailers, advertising, Pharma

Prerequisites

Before attending this course, you should have a basic background in statistics.

 
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