By: Eric Siegel, Founder, Predictive Analytics World for Business
In anticipation of his upcoming conference presentation at Predictive Analytics World for Business Las Vegas, June 16-20, 2019, we asked Bob Nisbet, Instructor at University of California, Irvine, a few questions about their deployment of predictive analytics. Catch a glimpse of his presentation, Effective Data Preparation, and see what’s in store at the PAW Business conference in Las Vegas.
Q: In your work with predictive analytics, what behavior or outcome do your models predict?
A: Customer/student retention, cross-sell, credit risk, customer value, customer acquisition probability.
Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?
A: Currently, I am retired, but I still work on contract. In my recent contract with Western Seminary, I developed a student retention model, in which I combined structured and unstructured data to predict the probability that a student will remain in the school and graduate with a degree. In my former jobs, my results helped to drive relationship management operations to reduce churn, predict the probability of acquisition of commercial banking customers, and to model credit risk.
Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?
A: One cross-sell marketing project led to a $1.5 million new revenue stream for Bell South Telecommunications. A recent student churn model helped Western Seminary to reduce student churn from 83% to 65% over a 4-year period.
Q: What surprising discovery or insight have you unearthed in your data?
A: Demographic data can enhance the quality of model predictions significantly.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.
A: Many of the most predictive variables of my models were those that I derived myself from available data elements, using various data preparation operations.