How Modern Database Marketing Builds Member Contributions
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. One man who has succeeded in this field is Tom Halatyn, owner of PAC-COMM and Director of Database Marketing at Campaign Mail & Data, Inc. in Falls Church, Virginia. He specializes in increasing response rates to messages for contributions, membership, civic action or sales.

"The number of conservative donors in the US is about 2.5 to 3 million", Tom explains. "Three million seems like a large number, but 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."

To build response for each type of appeal, Tom turned to modeling to determine exactly who was responding. He found, for example that Republicans, aged 40-50, with a household income of $50,000 to $75,000, with higher investment income, interest in books and music, had an average response rate of 7 percent in an overall mailing that only achieved a 1.5 percent response rate. To increase the yield to each type of appeal from such mailings, he had to direct his effort to these valuable individuals.

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 did Tom find out about the donor’s books, music, age, and income? Tom’s company manages 105 million voter records and 165 million consumer records. Each record has from 15 to 45 pieces of data associated with it. He uses this data to enhance his non-profit donor databases.

When Tom is gearing up to rollout a non-profit mailing, he first appends both the voter and the consumer information onto a test file of responders and non-responders to the appeal. Voila, he has the income, family characteristics, buying habits, interests and life style preferences of the matched donor candidates: more than 60 characteristics in all. Not all of these characteristics, of course, can be related to willingness to make contributions. Modeling has enabled him to zero in on those which make the most difference in separating likely donors from less likely donors.

One client came to him because their house file mailings began losing money. Tom 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. He 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. The analysis that Tom has done has shown 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 member’s 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. What he discovered is that a recently registered Republican is more likely to contribute than a longstanding party member. 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. Tom discovered that issue-driven appeals that do not consider the level of education in their targeting may be missing a major proportion of the prospect group. Follow up calls to non-responders confirmed the pattern: 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.

In short, Tom’s 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:

  List Based Donor Based
50,000 Test Mailing

$15,000

$15,000

Merge Purge of 500,000 records

$1,000

$1,000

Cost of names (400M * $75)

$30,000

$30,000

Response Analysis

$6,600

Surviving Names

400,000

125,000

Mailing cost @ $0.25

$100,000

$31,250

Total Program Cost

$146,000

$83,850

 
Response Rate

1.50%

3.50%

Number of Responders

6,000

4,375

Average Contribution

$20

$25

Total Revenue

$120,000

$109,375

Net Revenue

($26,000)

$25,525

In this example, Tom’s client rented 50,000 names from 10 lists for a test mailing. After the test, the client determined the best five lists, and rented 500,000 names from these five lists. Merge purge reduced the list to 400,000 names. Using the donor analysis method, Tom determined the voting and demographic factors that separated the responders in the test from the non-responders. At a cost of $6,600, he determined that only 125,000 of the 400,000 should be mailed. Using the list analysis method, all 400,000 were mailed. The list based method brought in 6,000 contributions at $20 each, whereas Tom’s donor analysis method brought in only 4,375 gifts at $25 each. The saving came from mailing only 125,000 instead of 400,000. The donor analysis method produced an overall profit of $25,525 from what would have been a $26,000 loss. If the list providers had been willing to rent their names on a net-name basis, his profit would have increased to $44,150.

The biggest problem that Tom uncovered was the idea that many clients have that one message fits all. He proved to several clients that a well-written fundraising letter with personalized features that target households or individuals can make a significant difference in the response. In one test, a public affairs client mailed two versions of a call to action appeal. One had reference to children, the second version omitted such reference. The appeals were sent to four randomly selected groups: two with children in the household, and two with no children in the household. The response rates were as follows:

HH w/Children HH w/o Children
Appeal with Children

3.8%

1.9%

Appeal w/o Children

1.7%

1.5%

The analysis shows that the message about children had better results in both types of household, but that when properly targeted to a household with children, the response was twice as effective as the same message to a childless household.

For a fund raising client, Tom suggested two messages: one with a personal appeal mentioning elderly dependents and the other not mentioning these old folks. The lists were targeted to households with and without elderly dependents. The response was similar to that for children:

HH w/Elderly HH w/o Elderly
Appeal with Elderly

3.2%

1.3%

Appeal w/o Elderly

1.1%

1.2%

Here, again, the appeal mentioning elderly dependents did better in both cases, but was almost three times more effective in homes that actually had elderly dependents.

Non-profit and political fundraisers are among the oldest database marketing organizations. They were at it long before commercial organizations discovered this valuable technique. But many of them have failed to keep up with the advances being made by commercial marketers. They have failed to:

  • Segment prospect universes based on data available from outside sources, including voter files and commercial sources

  • Use new creative and personalized messages that could enlarge the cumulative size of their prospect universes from thousands to potentially millions.

Clearly the work of people like Tom Halatyn is today bringing new techniques to an old industry that badly needs modernization.

 


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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