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What is predictive analytics?
Business metrics do a great job summarizing the past. But if you want to fully leverage big data and 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 delivers 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.
Predictive Analytics Applied is a self-paced online course instructed by the founding chair of Predictive Analytics World that covers the following topics:
- Applications: Business, marketing and web problems solved with predictive analytics
- Core technology: How a predictive model works and how it's created
- Evaluation: How well a predictive model works and how much revenue it generates
- Management: Project leadership and business process for predictive analytics
- Illustrations: Live demos and detailed case studies
- Hands-on: "Get your hands dirty" with a
revealing Excel-based exercise, bringing a predictive model to life and seeing it improve before your eyes
View a free 13-minute overview
video of this online training program, and access a detailed
outline of its four training modules and a complete summary of course
Sample screen shots:
Click on the following images for larger screen shots of the online training video:
Online course content and format:
Predictive Analytics Applied includes four jam-packed training modules of 60-90 minutes each, totaling 5 1/2 hours of viewing time.
This online program is internationally-friendly (but is only available in English) - registrants have participated remotely from 30 countries: Australia, Belgium, Canada, China, Denmark, Finland, France, Germany, India, Ireland, Isle of Man, Italy, Mexico, The Netherlands, Norway, Poland, Portugal, Russia, Saudi Arabia, Scotland, Singapore, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, United Kingdom, United States, and Uruguay.
Online training participants receive:
(Click to zoom)
Unlimited access for three months: View the training module
videos as many times as desired for three months, easily skipping to
any topic or sub-topic of the training program.
The instructor's acclaimed book: Predictive
Analytics: The Power to Predict Who Will Click, Buy, Lie, or
Die, a rich, entertaining primer by Eric Siegel.
Course materials book:
Over 100 pages of material that corresponds with the presentation
slides shown in the training videos (one book page per slide), and
contains additional detailed notes, references and pointers to online
Certificate of completion: An official Prediction Impact certificate with your name and the instructor's signature.
Book by instructor Course materials book Certificate of completion
Online video format. This online training program consists of
high-quality videos recorded for online viewing (not the recording of
a live seminar). The videos consist of training content, software
demos and intermittent instructor appearances, with verbal instruction
throughout. The video image is large - the resolution
is 800 by 600 (960 by 655 including video control areas), which is a
large portion of your screen - possibly the entirety of your screen,
depending on its resolution.
Each of the four training modules of this self-paced e-learning
program may be viewed at your convenience, pausing, rewinding and
fast-forwarding as needed. Within each module, jump to each sub-topic
immediately with a single click, and, within each sub-topic, step
forward and back at will.
Since the contents are concentrated, the
recommended pace is to view one module per week. On the faster side,
the entire program may be "crammed" in just four days by viewing one
60-to-90 minute module per day.
Who this online course 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, data scientists, BI directors, developers, DBAs, data warehousers, web analysts, and consultants who wish to extend their expertise to predictive analytics.
In order to meet the unique training needs of business decision makers and analytics practitioners, this training program is:
- Business-focused. Unlike other training programs that also cover scientific, engineering and medical applications of data mining and analytics, this seminar focuses squarely on solving business and marketing problems with these methods.
- Comprehensive across business needs. Within this realm, however, we step beyond the standard application of response modeling for direct marketing to solve a wide range of business problems.
- Vendor-neutral and method-neutral. This training program, which is not run by an analytics software vendor, provides a balanced view across analytics tools and methods.
No background in statistics or modeling is required. The only specific knowledge assumed for this training program is moderate experience with Microsoft Excel or equivalent.
Please contact Prediction Impact with any questions about this training program.
Start learning right now
The following short, published articles, written by the instructor, are a great place to get started. Note that these articles are not required reading; most of the material therein will be covered during the training program.
Seven Reasons You Need Predictive Analytics Today
Predictive analytics has come of age as a core enterprise practice necessary to sustain competitive advantage. This definitive white paper reveals seven strategic objectives that can be attained to their full potential only by employing predictive analytics, namely Compete, Grow, Enforce, Improve, Satisfy, Learn, and Act.
Predictive Analytics with Data Mining: How It Works
Get a handle on the functional value of predictive analytics for marketing, sales and product direction.
DM Review's DM Direct
Driven with Business Expertise, Analytics Produces Actionable Predictions
Run data mining as a business activity to generate customer predictions that will have a business impact. CRM Magazine's DestinationCRM.
Predictive Analytics' Killer App: Retaining New Customers
Predictively targeted discounts convert new customers who would otherwise never return to become loyal customers. DM Review's Extended Edition.
About the instructor
Eric Siegel, Ph.D., is a seasoned consultant in data mining and analytics, author of the notable book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, 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. The founding conference chair of Predictive Analytics World and Text Analytics World, Eric has authored 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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