A new on-demand e-learning course on profit-driven business analytics is available through SAS. The course is taught by Wouter Verbeke, an assistant professor in business informatics at the University of Brussels (Belgium), and Bart Baesens, a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom).
Recently, Baesens, who also writes the Data Science Deep Dive column for Big Data Quarterly, discussed the role of profit-driven analytics and the objectives of the new course.
What is profit-driven business analytics and why are you focusing on it?
The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support subsequent cost-optimal decision making. The analytics is advanced in that it is tailored for use in a business setting, where it is crucial to account for the costs and benefits that are related to decision making based on the output of analytical models. We call such approaches “profit-driven analytics” and they extend and reinforce the abilities of traditional analytics. The profit-driven perspective toward analytics that is presented in this course contrasts with a traditional statistical perspective, which ignores the costs and benefits related to decision making based on analytical models.
What do you cover in the course?
We discuss both profit-driven descriptive and predictive analytics, and as well introduce uplift modeling as a stepping stone toward developing prescriptive analytical models. We also discuss a range of profit-driven evaluation measures for assessing the performance of analytical models from a business perspective. Finally, we conclude by looking into the economic impact of adopting analytics and zoom into some practical concerns related to the development, implementation, and operation of analytics within an organization.
Can you provide a practical example of how a profit-driven perspective is different from a statistical perspective?
Let’s take the example of customer churn prediction. Many organizations nowadays develop and operate customer churn prediction models, allowing them to select and target the customers which are most likely to churn in a retention campaign. Adopting a profit-driven perspective in developing, evaluating and operating a customer churn prediction model means that you account for the value of customers. The higher the value of a customer, the more important it is to accurately estimate the risk of churn. Profit-driven predictive analytics allows you to do so. For evaluating the performance of a customer churn prediction model, it is the profit that should be assessed. That is what profit-driven evaluation measures allow you to do. Uplift modeling, finally, allows to further refine the analytical model setup by estimating the net effect of targeting a customer in a retention campaign in terms of the net decrease in probability to churn. This allows you to select so-called “persuadables,” meaning customers which can be persuaded by the campaign, rather than lost causes, which are customers who have already made up their minds and will churn even when offered an incentive to remain loyal. As such, the return of marketing efforts can be optimized and significantly increased, as several case studies show.
Why have you developed this course, and why now?
Over the past years, we have witnessed a remarkable and expansive growth in the adoption of data analytics, which is being used nowadays for a large variety of applications across industries. About a decade ago, analytics was mainly used in finance and marketing. Nowadays, we see it everywhere, since many organizations see the benefit, see the competitors adopting analytics, and want to capitalize on heavy investments they made in data warehousing and BI facilities to support their operations. Since the analytical skills and maturity in the industry have significantly lifted, we felt the time was right to introduce the next generation of analytical techniques. These build and expand upon the traditional analytical approaches. They provide greater power and refinement to solve business problems in an optimized manner.
What are the implications of a profit-driven perspective?
The implications of adopting a profit-driven perspective toward the use of analytics in business are significant. A mind shift is needed both from managers and data scientists in developing, implementing, and operating analytical models. This, however, calls for deep insight into the underlying principles of advanced analytical approaches. Providing such insight was our general objective in developing this course. We want to assist practitioners in gaining a deeper practical understanding of the inner workings and underlying principles of profit-driven approaches. At the same time, in order to support the adoption of the presented approaches, we wish to advance managerial thinking on the use of advanced analytics by offering insight into how these approaches may either generate significant added value or lower operational costs by increasing the efficiency of business processes.
How do you expect the course being used?
We envision that this course will facilitate organizations stepping up to a next level in the adoption of analytics for decision making by embracing these profit-driven methods. Doing so requires an investment in terms of acquiring and developing knowledge and skills, but, as is demonstrated throughout the course, also generates increased returns.