Building Member Contributions with Database Marketing

 

by Arthur Middleton Hughes

 

 

It is becoming harder to get people to contribute money to non-profit causes. More and more campaigns, committees and associations are all mailing to the same group of households.  Take conservative donors, for example. The number of conservative donors in the US is about 2.5 to 3 million. Many of these names are rapidly wearing out from saturated use and declining average contributions. What is missing in the future of fundraising is not finding out which donor lists respond to a mailing, but what types of people respond. This can be done with a model.

 

Some conservative causes have found that Republicans, aged 40-50, with a household income of $50,000 to $75,000, with higher investment income, interest in books and music, have an average response rate of 7 percent in an overall mailing that only achieves a 1.5 percent response rate.

 

This type of sophisticated analysis is quite new for political fund-raisers. Most of them maintain donor databases that have only the name and address and donation history. They test various lists, but they know virtually nothing about the people on those lists. Where can you find out about the donorÍs books, music, age, and income?

 

One source is the AmeriLINK list files from KnowledgeBase. When you are planning a non-profit mailing, your first step should be to append both voter and consumer information onto a test file of responders and non-responders to the appeal. Using these measures, you will have the income, family characteristics, buying habits, interests and life style preferences of the matched donor candidates. These characteristics can be related to willingness to make contributions. Modeling enables non profits to zero in on those which make the most difference in separating likely donors from less likely donors.

 

One conservative cause mailer found that their house file mailings began losing money. Their service bureau overlaid the file with voter and consumer characteristics and discovered something very interesting, which the client could not have known from the data available to them. They discovered that over the past two years, their membership had changed significantly. They had gained more Republicans with higher incomes, retirement accounts, and other investments. Since they did not know that, they had been relying on softer issue driven appeals. These appeals were not working with their newer, more conservative and affluent members. As a result of this analysis, the client revised their house-mailing program to begin mailing different appeals to different segments of their member base - with significant success.

 

For most conservative fund raising programs, it was often assumed that the political donor is an older, long-standing member of a party or advocacy organization. Modeling showed that in some cases, the older, higher income, longstanding party members have been responding less than the younger lower-income ones. Why? Because too much of prospect testing often fails to measure the intensity and extent of some memberÍs activity on the part of causes and candidates. In other words, many organizations have been sending out entirely the wrong messages to their younger activist members. This information comes from overlaying voter and commercial information on the conservative organization house files.

 

The length of time a person has been a registered voter can be a major predictor of the likelihood of a person contributing. One mailer found that recently registered Republican were more likely to contribute than longstanding party members. This is because the affluent voter who recently became a Republican may be more enthusiastic about certain issue messages than the older member.

 

The level of education is a key characteristic in grassroots and fundraising programs. Issue-driven appeals that do not consider the level of education in their targeting may be missing a major proportion of the prospect group. When an individual is essentially ignorant of the issue involved, no further persuasion will be effective. Ignorance of issues is often a function of educational level, which can be obtained from overlay data.

 

The method is to mail to the best performing individuals across lists rather than to mail to the best performing lists. This can be illustrated by a concrete example:

 

 

 

 

In this example, a illustrative fund raiser rents 50,000 names from 10 lists for a test mailing. After the test, the mailer determines the best five lists, and rents 500,000 names from these five lists. Merge purge would reduce the list to 400,000 names. Using a KnowledgeBase model with data from AmeriLINK the mailer could determine the voting and demographic factors that separate the responders in the test from the non-responders. At a cost of $12,000, it would be possible to determine that only 125,000 of the 400,000 should be mailed. Using the list analysis method, all 400,000 would have to be mailed. The list based method might bring in 6,000 contributions at $22 each, whereas the donor analysis method might bring in only 4,375 gifts at $27 each. The saving comes from mailing only 125,000 instead of 400,000. The donor analysis method might result in an overall profit of $21,275 from what could have been a $38,000 loss. If the list providers were willing to rent their names on a net-name basis, the profit could have almost doubled.

 

More non profits should explore the idea of appending demographics and lifestyle data to their rented lists, using modeling to cut down on their mailing, while boosting their net revenue.

 

 

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Arthur Middleton Hughes is Vice President of The Database Marketing Institute. Ltd. (Arthur.hughes@dbmarketing.com) which provides strategic advice on relationship marketing. Arthur is also Senior Strategist at e-Dialog.com (ahughes@e-Dialog.com) which provides precision e-mail marketing services for major corporations worldwide. Arthur is the author of Strategic Database Marketing 3rd ed. (McGraw Hill 2006). You may reach Arthur at (954) 767-4558 .

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