Predictive Analytics Seminar Broadens Offerings for Spring 2008: Includes Toronto and New York City

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Predictive Analytics for Business, Marketing and Web is a concentrated training program that includes interactive breakout sessions.

TRAINING SEMINAR: Predictive Analytics for Business, Marketing and Web April 3-4 (Toronto), May 8-9 (San Francisco), and June 5-6, 2008 (New York City)

(This seminar is offered in conjunction with the eMetrics Marketing Optimization Summit.)

Early-bird special: Sign up one month ahead for $100 off the registration fee.

Business metrics do a great job summarizing the past. But if you want to predict how customers will respond in the future, there is one place to turn -- predictive analytics. By learning from your abundant historical data, predictive analytics provides the marketer something beyond standard business reports and sales forecasts: actionable predictions for each customer. These predictions encompass all channels, both online and off, foreseeing which customers will buy, click, respond, convert or cancel. If you predict it, you own it.

The customer predictions generated by predictive analytics deliver more relevant content to each customer, improving response rates, click rates, buying behavior, retention and overall profit. For online applications such as e-marketing and customer care recommendations, predictive analytics acts in real-time, dynamically selecting the ad, web content or cross-sell product each visitor is most likely to click on or respond to, according to that visitor's profile. This is AB selection, rather than just AB testing.

Predictive Analytics for Business, Marketing and Web is a concentrated training program that includes interactive breakout sessions. In two days we cover:

  • The techniques, tips and pointers you need in order to run a successful predictive analytics and data mining initiative
  • How to strategically position and tactically deploy predictive analytics and data mining at your company
  • How to bridge the prevalent gap between technical understanding and practical use
  • How a predictive model works, how it's created and how much revenue it generates
  • Several detailed case studies that demonstrate predictive analytics in action and make the concepts concrete

No background in statistics or modeling is required. The only specific knowledge assumed for this training program is moderate experience with Excel.

Who this seminar is for:

  • Managers: Project leaders, directors, CXOs, vice presidents, investors and decision makers of any kind involved with analytics, direct marketing or online marketing activities.
  • Marketers: Personnel running or supporting direct marketing, response modeling, or online marketing who wish to improve response rates and increase campaign ROI for retention, upsell and cross-sell.
  • Technology experts: Analysts, BI directors, developers, DBAs, data warehousers and consultants who wish to extend their expertise to predictive analytics.

For more information, visit http://www.predictionimpact.com/predictive-analytics-training.html, or e-mail us at training@predictionimpact.com. You may also call (415) 683-1146.

About the instructor:
Eric Siegel, Ph.D., is a seasoned consultant in data mining and analytics, an acclaimed industry instructor and an award-winning teacher of graduate-level courses in these areas. Eric served as a computer science professor at Columbia University, where he developed data mining technology in the realms of machine learning performance optimization, integrating historical databases, text mining and data visualization. Eric produced 11 peer-reviewed research publications and ran an MIT-hosted symposium on data mining. He also co-founded two New York City-based software companies for customer/user profiling and data mining. With data mining, Eric has solved problems in CRM analytics, computer security, fraud detection, text mining and information retrieval.

Eric has taught industry programs through Prediction Impact, The Modeling Agency and Salford Systems. In addition, he taught many semesters of university courses, including data mining-related graduate courses as well as introductory lecture series for non-technical audiences. Two of these courses have been in syndication through the Columbia University Video Network. Eric also published three peer-reviewed papers on computer science education.

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