“Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” is a rich, entertaining primer that reveals the power and perils of predictive analytics, showing how predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime-fighting, and boosts sales.<\/p>\n
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.<\/p>\n
Dr. Siegel is the instructor of the acclaimed training program, Predictive Analytics for Business, Marketing and Web, and the online version, 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.<\/p>\n\u00a0SPEECHES:\u00a0<\/span>\n The excitement over “big data” has grown so dramatically as to assume the status of a Movement. But what is the the value, the function, the purpose? The most actionable thing 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 effective 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.<\/p><\/blockquote>\n Prediction delivers the ultimate payoff by driving millions of more effective, per-customer decisions. But prediction is the ultimate challenge and the ultimate mystery. How does predictive analytics work? At the heart of this technology is segmentation. It’s a kind of automated, hyper-segmentation on overdrive that leverages the best of machine learning and big data technology. In this keynote, Predictive Analytics World founder and Predictive Analytics author Eric Siegel shows how predictive analytics works and serves to personalize marketing.<\/p><\/blockquote>\n Prediction delivers the ultimate payoff by driving millions of more effective, per-customer interactions. 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 keynote, Predictive Analytics World founder and Predictive Analytics author Eric Siegel describes four ways in which predictive analytics drives better marketing engagement with the use of social data.<\/p><\/blockquote>\n 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 Eric Siegel describes four ways in which predictive analytics drives better business decisions with the use of social data.<\/p><\/blockquote>\n How does predictive analytics actively deliver increased returns? 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 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.<\/p><\/blockquote>\n Data driven marketing decisions are meant to maximize impact – right? Well, the only way to optimize marketing influence is to predict it. The analytical method to do this is called uplift modeling. This is a completely different animal from what most models predict: customer behavior. Instead, uplift models predict the influence on customer behavior gained by choosing one marketing action over another. The good news is case studies show ROI going where it has never gone before. The bad news? You need a control set… But you should have been using one anyway! The crazy part is that “marketing influence” can never be observed for any one customer, since it literally involves the inner workings of the customer’s central nervous system. If influence can’t be observed, how can we possibly model and predict it?<\/p><\/blockquote>\nContact us to book this speaker<\/span><\/a>\n
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<\/br><\/p>\nPredictive Analytics: Tapping the Potential of Big Data<\/h3>\n
Segmentation and personalization with predictive analytics<\/h3>\n
Customer conversion with predictive analytics and social media<\/h3>\n
Four Ways Predictive Analytics Leverages Social Media<\/h3>\n
Five Ways to Lower Costs with Predictive Analytics<\/h3>\n
Persuasion by the Numbers: Optimize Marketing Influence by Predicting It<\/h3>\n