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 Guide

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This is a repost of a Goodreads' review I made in 2013, for a book I read in 2005, which seems relevant now, as the industry is adding a data-driven focus. Plus, the world is now being transformed by advances in artificial intelligence and machine learning (AI/ML), particularly deep learning, and the large data sets and complexity of donor actions should greatly benefit from analysis. Note, the tax changes for 2018 and beyond will increase the importance of major donors, attenuating the benefits of AI/ML, as data for high-net-worth individuals is sparse.

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, at times 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 other aspects were good refreshers on thinking in terms of statistics.

Below is the synopsis I wrote at the time:

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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