Looking for the perfect donor

DSC improves response rate and donation amount for fundraising campaigns

The objective of Direct Social Communications (DSC) was a better selection of target groups for fundraising campaigns that would result in more donors and larger donations. SAS partner Python Predictions used the existing DSC database to define better profiles for the most interesting donors and gain greater insight into their behavior. Using this knowledge, it then selected individuals with similar profiles that had the potential to become new donors, to receive direct mailings. This resulted in a vast increase in the success of acquisition mailing campaigns. Two test campaigns with the new target groups revealed a substantially higher response rate and an increased donation amount for each response.


DSC is a Brussels-based communication agency with 13 employees. They carry out fundraising activities for humanitarian organizations using principally direct mail. DSC develops communication campaigns and sends out postal mailings to meticulously selected groups of individuals.

As a leading fundraising company for humanitarian organizations since 1985, DSC has assembled a detailed database of almost 250.000 donors. The database contains hundreds of donor variables, such as how often people donate, the donation amounts, and the preferred organizations to receive donations. In addition, the database was enriched with numerous socio-demographic parameters such as age, gender, education and housing comfort.


Finding new donors is the most difficult task in our work. Thanks to SAS and Python Predictions, we now have the right tool to create more effective donor profiles, enabling us to better target and tailor our direct mail fundraising campaigns.

Ludo Longin
Representative Delegate, DSC

Improving the acquisition of donors

Recruiting new donors is the most difficult task in fundraising. Over the years, database analysis has enabled DSC to define donor profiles. Based on these profiles, segments are defined and selected for new direct mail fundraising campaigns. However, DSC wanted to improve the success rate of its campaigns. They called upon SAS partner Python Predictions, a Brussels-based service provider specialized in predictive analytics in marketing, risk, and operations. Their approach consists of using all available data to predict future events and improve decision making.

Python Predictions helped DSC construct more accurate donor profiles. "We used the existing DSC database for advanced predictive analysis. Specifically, we analyzed donor behavior and its relationship to key objective socio-demographic factors. In this way, we could select a combination of parameters that are good predictors for donor behavior and create ideal donor profiles. We then looked for individuals with similar profiles within the Belgian population to identify new donors. The improved donor profiles made it also possible to develop campaigns that are better tailored to specific target groups," reports Geert Verstraeten, partner at Python Predictions.

Achieving higher response rates and donation amounts

DSC and Python Predictions tested the new donor profiles using two fundraising campaigns and achieved positive results in the acquisition of new donors.

During the first test campaign, direct mailings were sent out for Licht en Liefde, an organization that helps blind people in Belgium. The mailings were sent to both the old and new profiles, making a direct comparison possible.

"The new profile had a 37% higher response rate and a 32% increase in the amount of donations compared to the old donor profile. The revenue per letter sent increased by 82%. This was an excellent result and provides a sound basis for further fine-tuning of our primary campaign," says Ludo Longin, representative delegate at DSC. The second test campaign, performed for the Muco vereniging, proved even more successful. The response rate more than doubled and the donations increased by 11% compared to previous results using the old profiles.

Precisely selecting variables and testing methods

Python Predictions chose to compare different techniques in order to maximize the results. "Building a model includes the selection and combination of the best predictors, in such a way that they accurately indicate the donor potential in each geographical area. The model not only quantifies this potential, but also delivers valuable insight into the location and the detailed profile of the most valuable potential donors," explains Verstraeten.

Continuing with ready-to-use tool

DSC now has a ready-to-use SAS-based tool to select good potential donors without the help of Python Predictions. Ludo Longin notes, "The entire analysis and development of this tool took only three months. As a result, we can now swiftly create more effective new donor profiles, select target groups, implement campaigns, and generate significantly improved results. Python Predictions helped us improve our donor profiles, enabling us to select better potential donors and raise more money from more donors."

Direct Social Communications


Fine-tuning donor profiles for the acquisition of new donors for humanitarian organizations


SAS® Enterprise Miner
SAS® Base
SAS® Stat


  • Better tailored fundraising campaigns
  • Improved selection of donors
  • Increased response rate and donation amounts

Lessons learned

  1. Target similar areas
    Do not simply target the areas where the current donors live. Instead, try to profile the areas where current donors live, and target the areas that contain a great number of similar profiles
  2. Test several methodologies
    Do not simply proceed using a single familiar statistical method; instead test your data with several methods and then choose the best method before engaging in a field-test.


Python Predictions
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

Back to Top