At Vlerick Business School, Bart teaches in the European Executive MBA, the FMCG Bootcamp for Master students and the DBA program. He also teaches in the MBA program at Rotterdam School of Management, and is involved in a variety of open executive programs on the topics of behavioural economics, data analytics and decision making.
Bart have developed a unique approach to data analytics in business called "decision-driven data analytics." By emphasizing the psychology of the decision-maker and leveraging the unique properties of human intelligenge, it can help advanced data analytics and AI intitiatives have greater impact.
Winning Customers With Behavioral Economics
The key to business success in virtually any industry is the customer's experience. This is especially true in today's age of online reviews and social media. Companies like Amazon and TripAdvisor have produced the largest customer satisfaction survey in the history of our planet, and they are adding to it every day. In today's data-driven world, the experience of one customer shapes the experience of the next. Managing this web of social influence is a daunting task.
Fortunately, we now know more than ever about the psychological drivers of customer satisfaction and customer lifetime value. There has never been a better time for companies to focus on customer experience. The main objective of this module is to improve participants' ability to acquire, develop, and retain profitable customers using insights, tools, and frameworks from the behavioral and data sciences.
Executives make hundreds of decisions each day. Some decisions are strategic and have long-term consequences; others are tactical and have immediate effects. The long-term success of any company is directly tied to the ability of its executives to make good decisions. Unfortunately, the human mind suffers from a variety of thinking traps that harm decision quality. This harmful influence is often exacerbated when people make decisions in groups.
A first step toward improving executive decision-making is to learn how to recognize these biases, and understand how they play out at different stages of the decision-making process. The second step is to prevent the harmful influence of these biases by designing and applying debiasing tools. In this module, participants will develop a checklist to screen for decision-making biases in their organizations, and corresponding set of debiasing tools.
Thinking With Data and Machines
A data revolution is sweeping through society. Buzzwords like "Big Data", "data-driven", and "evidence-based" are everywhere. Data has also transformed the business landscape seemingly overnight. Companies that cannot effectively leverage their data cannot hope to compete in the future. As a result, most companies now tout their commitment to making data analytics a central part of their decision-making apparatus. Unfortunately, many data analytics initiatives produce disappointing results.
A major reason for this failure is that people often draw the wrong conclusions from data. The human mind is not endowed with the tools to properly interpret data. Many of the processes that our minds use to make sense of the world lead directly to predictable errors. In order to understand these errors and correct them, it is critical to infuse data analytics with psychology.