Gain a better understanding of donors to maximize return on investment.
Segmented donors by analyzing five years of historical data using the SPSS CHAID procedure.
With over 1 million donors, UNICEF Germany finances its projects to help children in less-developed countries through fundraising in the form of donations and sales of greeting cards.
Faced with an increasingly tight budget and a continuously growing number of competitors, UNICEF Germany turned to Ogilvy & Mather Dataconsult (O&MDC) to better segment its target donors. The goal was to uncover particular donor characteristics to maximize returns, said Matthias Singer-Fischer, Senior Consultant, Ogilvy and Mather Dataconsult.
Using five years of historical data from the UNICEF database, O&MDC gathered approximately 30 variables, including standard demographics, donation frequency, date of last donation, sum of all recent donations and several variables specific to UNICEF, such as preferred causes.
Because of the host of variables and different scale levels, O&MDC used a Chi-square-based (CHAID) segmentation method. Analysts chose SPSS because its CHAID procedure - currently one of four powerful decision-tree algorithms in AnswerTree - handles multiple variables, integrates all scale levels and adjusts the method through interaction. It's also simple to use and reports results that are easy to understand.
Results gave UNICEF Germany a clearer understanding of its donors. Compared to a control group,
We increased direct mail response by up to 80 percent.
"We developed models that enabled UNICEF Germany to identify optimal target groups, thus increase its direct mail response by up to 80 percent," Singer-Fischer added. What's more, it showed a return on investment of more than 65 percent.
This enabled UNICEF Germany to dramatically decrease their mailing volume without affecting their revenue from donations.
As a result, UNICEF Germany targets donors who are partial to the featured topic, thus ensuring a better distribution among UNICEF donors.
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