Q: In your work with predictive analytics, what behavior or outcome do your models predict? A: Although we predict a variety of different outcomes, we specialize in predicting retention, calculating the probability that individuals in a university, business, or program will stay or leave. Because the costs of acquiring a customer far exceed the costs of retaining one, this focus allows us to bring great value to our clients. Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations? A: We employ our analytics not only in businesses and universities but also in the public policy arena. Working with a major city whose homicide rate was recently 2nd in the country, we used our methodology—which entails not just specialized modeling but also sophisticated appends and measurement of both individual and contextual data—to provide metropolitan police with lists of individuals at risk of committing murder, so that the police could intervene. The result: murder in this city has been driven down by 50% since 2012, translating to 100 lives saved from a tragic end and 100 potential offenders whose lives were turned around. Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative? A: We work with a major university for whom the leading predictive analytics solution in the university retention market had failed—leaving the university at risk of losing millions of dollars. By applying our methodology and including contextual (e.g., supra-individual) and behavioral data and measures, we created a model that was more than 99% accurate in predicting the overall percentage of students who would return the next year and 96% accurate in predicting which specific students were at risk of leaving the university. This resulted in millions in savings for this university. Q: What surprising discovery or insight have you unearthed in your data? A: The example above, in which the leading predictive analytics solution in the university retention market failed to deliver acceptable results, documents two important findings. First, this industry can serve our clients far better by offering custom solutions than by applying off-the-shelf models. Second, it demonstrates the enormous increase in predictive power that we can gain from including contextual and behavioral data guided by behavioral and social science. Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World. A: Whether we work in business, health care, financial services, our models predict human behavior; As our successes illustrate, big leaps in the power of predictive analytics come not just from model optimization but also from a sophisticated understanding of the role carefully-selected individual and contextual data, and carefully-crafted measures, will play in predicting outcomes. These gains come not from mindlessly appending data, or blindly applying the latest statistical technique, but from drawing on behavioral and social science to identify data and measures that truly increase predictive power. In other words, it’s about the carpenter, not the hammer.