In today's big data world, many companies have gathered huge amounts of customer data about marketing success, use of financial services, online usage, and even fraud behavior. Given recent trends and needs such as mass customization, personalization, Web 2.0, one-to-one marketing, risk management, and fraud detection, it becomes increasingly important to extract, understand, and exploit analytical patterns of customer behavior and strategic intelligence. This course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data science techniques for advanced customer intelligence applications. This highly interactive course provides a sound mix of both theoretical and technical insights as well as practical implementation details and is illustrated by several real-life cases. References to background material such as selected papers, tutorials, and guidelines are also provided.
- apply a series of powerful, recently developed, cutting-edge analytical and data science techniques
- ensure the practical application of these techniques to optimize strategic business processes and decision making
- explore a futuristic vision of how emerging data science techniques might change your key business processes
- deploy, monitor, and optimally backtest analytical models.
Who Can Benefit
- Those involved in estimating, monitoring, auditing, or maintaining models for various types of customer intelligence; those involved with using data mining techniques for various types of customer intelligence, job titles including business analysts in various settings (e.g. risk management, manufacturing, telco, retail, advertising, public, pharmaceutical, and so on), marketing/CRM managers, fraud managers, customer intelligence managers, risk analysts, CRM analysts, marketing analysts, senior data analysts, and data miners
- Before attending this course, you should know how to
- preprocess data (such as sampling, missing values, outliers, categorization, and so on)
- develop predictive models using logistic regression
- develop predictive models using decision trees
- develop descriptive models using basic segmentation techniques
- quantify the performance of predictive models (such as lift curves, ROC curves, and so on).