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Review: Data Mining for Fund Raisers

Data Mining For Fund Raisers: How To Use Simple Statistics To Find The Gold In Your Donor Database Even If You Hate Statistics: A Starter GuideData Mining For Fund Raisers: How To Use Simple Statistics To Find The Gold In Your Donor Database Even If You Hate Statistics: A Starter Guide by Peter B. Wylie
My rating: 4 of 5 stars

My spouse, a development researcher of high-net worth individuals, was given this book because she was the 'numbers' person in the office. Since my undergraduate was focused on lab-design, including analysis of results using statistics, I was intrigued and decided to read it. Considering my background, I found some of the material obvious, while others aspects were good refreshers on thinking in terms of statistics.

Below is the synopsis I wrote at the time I read it:

Purpose of Book

* To provide a general outline of a statistically-oriented method to improve funding activities by mining your current donor database
* To provide general techniques for analyzing data, as well as provide cautions against bad techniques

How the Process Can Improve Endowment Activities

* Allows the organization to more accurately target quality prospects, either to increase participation rates, or to find major givers more inclined to donate
* Allows the organization to reduce costs, or more effectively use limited resources, i.e., phone smaller sets of people, limit the size of mailings, while increasing donations

Outline of Method (Non-Technical)

1. Export sample of donor database
2. Split sample into smaller components
3. Find relationships between donor features and giving
4. Select the significant variables
5. Develop scoring system
6. Validate findings
7. Test finding on limited appeals and compare results

Assumptions

* Assumes the donor data is extractable and randomized
* Requires export from donor database, or access via SQL
* Assumes additional software for statistics (DataDesk, SAS, SPSS)

Limitations

* Requires IT staff, analytical staff, donor contacts, and management to coordinate efforts
* Requires IT and analytical staff have adequate skills to implement
* Judges variables of data by both its intrinsic value and based upon its inclusion in database

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