Eric Siegel is an expert in predictive analytics and data mining and a former computer science professor at Columbia University, where he won the engineering school's award for teaching, including graduate-level courses in machine learning and intelligent systems - the academic terms for predictive analytics. After Columbia, Dr. Siegel co-founded two software companies for customer profiling and data mining, and then started Prediction Impact in 2003, providing predictive analytics services and training to mid-tier through Fortune 100 companies.
Dr. Siegel is the instructor of the acclaimed online training program, Predictive Analytics Applied. He has published over 20 papers and articles in data mining research and computer science education and has served on 10 conference program committees.
- How Predictive Analytics Fortifies Healthcare
Predictive analytics addresses today’s pressing challenges in healthcare effectiveness and economics by improving operations across the spectrum of healthcare functions.
- Driving Decisions with Predictive Analytics: The Top Five Business Applications
The value proposition is straight-forward and proven: Predictive analytics produces business rules that deliver. The customer predictions generated by predictive analytics’ business rules deliver more relevant content to each customer, improving response rates, click rates, buying behavior, retention and overall profit. Harnessing value with predictive analytics depends on some careful choices: What kind of customer behavior you predict and which operational decisions you automate with it. This session will guide you in making these choices, and cover a healthy dose of the core technology along the way - in a
“user-friendly” manner that makes the concepts intuitive, illustrating with detailed case studies.
- Uplift Modeling: Optimize for Influence and Persuade by the Numbers
Data driven decisions are meant to maximize impact - right? Well, the only way to optimize influence is to predict it. The analytical method to do this is called
uplift modeling (aka,persuasion modeling). This is a completely different animal from standard predictive models, which predict customer behavior. Instead, uplift models predict the influence on an individual’s behavior gained by choosing one treatment over another. In this session, PAW founder Eric Siegel provides an introduction to this growing area.
- Five Ways to Lower Costs with Predictive Analytics
Question: How does predictive analytics actively deliver increased returns? Answer: By driving operational decisions with predictive scores - one score assigned to each customer. In this way, an enterprise optimizes on what customers WILL do. But, in tough times, our attention turns away from increasing returns, and towards decreasing costs. On top of boosting us up the hill, can predictive analytics pull us out of a hole? Heck, yes. Marketing more optimally means
you can market less. Filtering high risk prospects means you will spend less. And, by retaining customers more efficiently, well, a customer saved is a customer earned - and one you need not acquire. In this keynote, Eric Siegel will demonstrate five ways predictive analytics can lower costs without decreasing
business, thus transforming your enterprise into a Lean, Mean Analytical Machine. You’ll want to run back home and break the news: We can’t afford not to do this.
- Predictive Analytics for Marketing: Learning from Data to Predic
Prediction is the holy grail of marketing. Foreseeing each customer purchase, click, and cancellation is the ultimate means to drive more effective, per-customer decisions. And today’s enterprise has a wealth of marketing experience from which to learn to predict - aka, data. This learning process is called predictive analytics. In this keynote session, Predictive Analytics author and Predictive Analytics World founder Eric Siegel describes how this technology leverages big data, learning from it in order to drive more effective marketing.
- Weird Science: How to Know Your Predictive Discovery Is Not BS
An orange used car is least likely to be a lemon.” At least that’s what was claimed by The Seattle Times, The Huffington Post, The New York Times, NPR, and The Wall Street Journal. However, this discovery has since been debunked as inconclusive. As data gets bigger, so does a common pitfall in the application of standard stats: Testing many predictors means taking many small risks of being fooled by randomness, adding up to one big risk. John Elder calls this issue vast search. In this keynote, PAW founder Eric Siegel will cover this issue and provide guidance on tapping data’s potential without drawing false conclusion.
- Four Ways Predictive Analytics Leverages Social Med
Prediction delivers the ultimate payoff by driving millions of more effective, per-customer decisions. But prediction is the ultimate challenge; predictive analytics can use all the help -- and all the data -- it can get. No data predicts a customer’s behavior like social data: who the customer knows, what sentiment he or she expresses, and which things the customer Likes. In this session, Predictive Analytics World founder and Predictive Analytics author Eric Siegel describes four ways in which predictive analytics drives better business decisions with the use of social data.
- Predictive Analytics: Delivering on the Promise of Big Data
The excitement over “big data” has grown dramatically. But what is the value, the function, the purpose? The most actionable win to be gained from data is prediction. This is achieved by analytically learning from data how to render predictions for each individual. Such predictions drive more effectively the millions of operational decisions that organizations make every day. In this keynote, Predictive Analytics World founder and Predictive Analytics author Eric Siegel reveals how predictive analytics works, and the ways in which it delivers value to organizations across industry sectors.