Unleash the potential of your advancement shop’s database.
Join Academic Impressions for an online course that will enhance your current data mining efforts and help you wrangle your database to find the patterns that will focus your advancement efforts on your most promising prospects. Following each of the course's sessions, you will have the opportunity to submit - for instructor review - exercises based on the application of the statistical methods learned to your own shop's data. During the course, you will learn how to use:
Unlike a traditional webcast series, this online course emphasizes hands-on application of statistical techniques to your shop's daily development work and features in-depth, personalized interactions with the instructor. You will receive access to a course site that will enable you to complete many of the components on your own time and at your own pace between the live sessions. Like a traditional webcast series, you can comprehensively train as many people from your staff as you like by purchasing a single site connection.
All registered institutions will receive an Academic Impressions certificate of completion for each participant upon a satisfactory rating by the course's instructor. To ensure maximum interactivity and personal attention, enrollment will be limited. Take advantage of this exceptional value while space is still available!
Each session in this online course is designed to build toward a comprehensive knowledge of data mining and modeling techniques. As such, we strongly urge your institution to attend all four offerings. If you are planning on only attending the later sessions, be sure you can meet the following prerequisites:
Most Academic Impressions fundraising-related events are applicable for continuing education credits toward the CFRE International application for initial certification and/or recertification. This program is currently being considered for approval.
The content delivered in this online course will be based in part on the IBM SPSS Statistics 20 data mining software package. Included with your registration fee, IBM will provide up to four complimentary, 90-day trial editions of the software for your institution to use during the course.
Information on obtaining the software package will be emailed to participants after registration. To ensure ample time in acquiring and installing your software, please register at least 10 days prior to the first session you wish to attend.
After participating in this online course, you will be able to mine your existing data to improve the effectiveness of your advancement efforts.
Session 1 Learning Outcome: After participating in this session, you will be able to prepare your shop's data for mining by using participation essentials, prospecting essentials, and data workarounds.
Session 2 Learning Outcome: After participating in this session, you will be able to improve your shop's annual giving, membership, and event performance through the techniques of measures of spread, measures of center, and the bell curve.
Session 3 Learning Outcome: After participating in this session, you will be able to find patterns in your shop's membership and annual giving efforts through the techniques of correlation and chi square.
Session 4 Learning Outcome: After participating in this session, you will be able to analyze your shop's major gift pool through the techniques of linear and logistic regression.
Marianne is currently managing the prospecting effort for Cornell’s $4.75 billion “Cornell Now” campaign. She has been in the fundraising field since 1988, when she joined the research team at Harvard’s University Development Office. Marianne has served as a researcher for Harvard, Lesley University, and Carnegie Mellon University, where she directed the research department. She previously served as director of annual giving and development services for Southern New Hampshire University. A board member of the Association of Prospect Researchers for Advancement, Marianne is also an associate with the Helen Brown Group, where she serves on HBG’s data mining team.
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