Course Catalog
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Many of these classes are presented in cooperation with
SAS Institute as part of their Business
Knowledge Series. Others are offered through TDWI. All are also available on-site for your organization. For more information, send us email or
call us at +1 617 742-4252.
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Data Miners will customize a data mining seminar for
your organization. Depending on the number of topics
covered, the number of case studies used, and whether
or not there is a hands-on component, the class can
run from one to three days. Most of the material for
this class is derived from the book Data Mining
Techniques for Marketing, Sales, and Customer Relationship
Management by Michael J. A. Berry and Gordon S.
Linoff. |
| A typical one-day agenda: |
- Brief introduction to data mining
- Data mining tasks
- Data mining techniques
- Where can data mining help?
- Acquisition
- Retention
- Customer segmentation by behavior
- Customer valuation
- Forecasting
- How can data mining be applied?
- Understanding customer behavior through
exploratory data mining
- Discovering customer segments through
undirected clustering
- Discovering customer segments through
directed clustering using decision trees
- Scoring customers for attrition risk
using predictive models
- Using survival curves to quantify differences
in retention properties of different customer
segments
- Case Studies
- Building binary outcome churn models
for an Asian wireless phone service provider
- Understanding customer value at a North
American newspaper
- Forecasting future subscriber counts
for a North American wireless phone service
provider
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| The three
day course as taught in the SAS Business
Knowledge Series. |
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This course is designed for anyone who wants to learn about applying time-to-event analysis to business problems, including business managers, SAS programmers, and programmers using other software. Statisticians might also find the course useful, although the content is focused on business solutions rather than statistical rigor.
This course introduces survival analysis in the context of business data mining. The focus is on understanding customer behaviors that have a time-to-event component. Understanding how long a customer will remain active is the first step in calculating the future value of that customer. Two data characteristics, discrete time and large volume, make it possible to estimate hazards without making restrictive assumptions about the underlying distribution or form or the hazard function. Empirical hazards provide a window on customer behavior that is useful in itself and also provides the basis for calculating survival curves.
Complete
course description and registration information through SAS Institute. |
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This one-day class is designed for people who are familiar with data and databases, but unfamiliar with the modeling techniques used to perform important tasks such as scoring customers for likelihood to make a purchase, likelihood to default, channel affinity, and expected remaining lifetime. This class takes the point of view that a model is simply a formal description of relationships that exist in data. Good formal descriptions have many uses. A good description of a profitable customer can be used to classify new customers as likely or unlikely to be profitable by measuring their distance from the prototypical profitable customer. A good description of who has responded to past offers can be used to predict who will respond to future offers. A model may take the form of a set of rules or a mathematical formula. Either way, it can be tested for stability and accuracy so that it can be applied with confidence. The class will teach you what it takes to build stable models that remain effective for a long time and generalize well to new datasets. Several popular modeling techniques will be introduced and demystified including decision trees, contingency tables, and linear regression. These techniques will be applied to real data from a real product penetration case study. By studying the same business problem using several different modeling techniques, the class teaches a modeling methodology appropriate for all models while demonstrating the particular strengths of particular modeling approaches.
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Analytic customer relationship management is the art
and science of using data mining to improve your organizations
learning relationship with its customers. The course
includes case studies from several different industries.
The class includes a small amount of hands-on time
using SAS Enterprise Miner or SPSS Clementine however
no previous knowledge of these tools is assumed or
required.
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