Before, I walked through an example donor acquisition campaign, and mentioned that non-profits typically rent a list of names and addresses from other non-profits or direct marketing companies. These list sources have a pretty obvious problem: multiple organizations are soliciting the same people. To the extent that people have a limited budget for charitable contributions, organizations end up poaching donors from each other, or the lists' performance degrades.
A different source of prospects is a modeled list. Data companies assemble information on consumers, including demographic data like age and income, as well as buying habits, such as magazine subscriptions or tendency to buy environmentally-friendly products. Non-profits can pay data companies to find out what characteristics their existing donors share. A statistician can then take these characteristics and build a model to show which people who are not existing donors share characteristics with the existing donors. Using this "look-alike model", the non-profit can buy a list of prospects from the data company, and attempt to turn them into new donors.
Next time, I'll go into the process of actually soliciting donations from these prospects.