Many companies are flooded with huge amounts of data available in corporate databases. 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 lecture 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, we elaborate on how you can efficiently use and deploy both predictive and descriptive 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 crossselling, customer lifetime value modeling, and Web intelligence and social network analytics. We provide extensive practical advice and guidelines on how to put all the analytical tools and concepts to work in a reallife setting.