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"Peter Wylie's work brings a much needed element of science to advancement, and I've become a real advocate for his brand of predictive modeling... Brandon Ferris
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Learn how to focus on the most likley candidates Mine your own data. In addition to offering TargetDonor's modeling services, we train advancement professionals to use TargetDonor's conceptual framework to analyze their own donor data and build predictive models focused on your organization's needs - whether those needs are prospect identification or annual fund or membership appeals. In conjunction with data mining expert Peter Wylie we work with you personally via telephone and real-time web sessions to teach you how to build your own predictive models. This web-delivered approach to training saves your organization travel expenses and travel time and saves you from the fatigue of all-day, traditional training courses. Between training sessions, you and your staff can practice what you have learned. You'll get plenty of hands-on experience with your own data and guidance on how to apply the principals and techniques you learn during training to other challenges as they arise in your day-to-day fundraising work. As you'll see from their comments on this page, the professionals we've trained have high praise for our help. They come away with a new appreciation for data mining and how it can improve their work. And they are enthusiastic about applying their new model-building skills to fundraising questions they've always wanted to address. Talk to Our Clients, Test Drive Your Data If you are interested in talking with some of our current clients in institutional development, contact us for customer references. If you'd like to find out how analyzing your donor database can help improve your fundraising performance, get in touch to arrange for a complimentary analysis of a sample of your data. |
 Peter Wylie is a national recognized advancement consultant who has worked with major colleges and universities and taught many advancement professions how to mine donor data to find simple predictors of giving. |
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