Compute each customer’s future response.
"100% of October 2007 attendees rated this program Excellent or Very Good."
|Program:||Predictive Analytics for Business, Marketing and Web|
|Dates and Venue:||Sunday, May 8-9 2008|
|Training Schedule:||Day 1: 9:00 am to 5:00 pm
Day 2: 9:00 am to 4:00 pm
There will be a one-hour lunch break and four 15-minute coffee breaks each day.
|Instructor:||Eric Siegel, Ph.D.|
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 marketing, predictive analytics acts in real-time, dynamically selecting the ad, web content or email 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:
No background in statistics or modeling is required. The only specific knowledge assumed for this training program is moderate experience with Excel.
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, web analysts, and consultants who wish to extend their expertise to predictive analytics.
In order to meet the unique training needs of Emetrics Summit attendees, this training program is:
Predictive analytics solves many business problems, offering solutions such as:
In other words, customer prediction drives business actions, which deliver business results. We cover case studies across this range of applications, with detailed examples running through both days of the training program.
Data is your most valuable asset. It represents the entire history of your organization and its interactions with customers. Predictive analytics taps this rich vein of experience, mining it to produce predictive models. Where multi-channel data is available, predictive analytics discovers interactions across customer touch points, such as key online behavior that may predict which customers will respond to direct mail.
Whatever the application, the core methodology of predictive modeling is the same. We will uncover, in concrete terms, how modeling transforms your data into actionable customer predictions. To this end, we will see exactly what a model is, taking a look inside to see how it works and how it is created. Then we will:
Live demos of predictive analytics software. We will include detailed demonstrations of a general-purpose tool that implements multiple predictive modeling methods, as well as CART (Salford Systems), a tool specialized for decision trees. Its friendly GUI-based capabilities make the predictive model transparent so we can drill down and really see the inner workings of specific examples.
In addition to the products demonstrated, we will discuss the full spectrum of today's predictive analytics software, including free tools, cheap tools, and complete software suites.
Once you've got a predictive model, how do you know how good it is? We cover methods to evaluate models, which fall into two groups:
Forecasting: How large a boost in revenue, sales or profit will the model produce?
Accuracy: How well does it predict, how often is it correct, and how much better is it than standard segmentation such as RFM?
Deploying a predictive model is playing a numbers game that puts the odds in your favor and improves the effectiveness of campaigns, operations and web behavior. We create profit curves, ROI calculations and bottom-line analyses and talk through exactly what they're telling us. And we prepare for performance gotchas that sneak up on you.
Although predictive analytics is technical at its core, it must be run as a business activity in order to generate customer predictions that have a business impact. This requires a wholly collaborative process driven by business needs and marketing expertise. This ensures that customer predictions are actionable within your company's operational framework, and that they have the greatest impact within your company's business model.
Referencing the industry standard data mining process model (called CRISP-DM), we break down the requirements of a predictive analytics business initiative. We explore this process, by which analysts and managers collaborate to strategically position predictive analytics, sustain universal buy-in and understanding, and avoid common roadblocks and unforeseen hazards.
Like sky-diving and SCUBA diving, after a few hours of learning predictive analytics, it's a good time to dive right in. To this end, the training program includes breakout sessions, which are integrated with the conceptual flow of topics covered. You will join a small team and actively collaborate to design deployment strategies for predictive analytics. Working together to solve specific business problems, you will design strategic processes that avert organizational challenges, and you will design a broad technical approach, including the data discovery, data preparation and evaluatory metrics needed to direct a predictive analytics initiative.
These engaging breakout sessions are conducive to exercising the concepts you've learned, making them more intuitive and ingrained, and also provide an opportunity to learn from colleagues.
Attendees receive a course materials book and an official Prediction Impact certificate of completion at the conclusion of the Predictive Analytics for Business, Marketing and Web training program.
Feel free to contact Prediction Impact with any questions about this training program.
The following short, published articles, written by the instructor, are a great place to get started. Note that these articles are not required reading; the material therein will be covered during the training program.
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.
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.