From Unexamined Data to Profitable Information

This year we formalized our longstanding working relationship with fundraising analytics guru Peter B. Wylie. It is now a partnership. Because Peter has many clients and friends in higher education and the nonprofit sector, we have found ourselves working with him on new questions with customers in this currently emerging field. Together we help them get actionable information from mountains of data on potential donors, and sometimes we teach them how to continue data mining on their own.

This was the case with the development team at Ohio Wesleyan University. Like many institutions, Ohio Wesleyan had amassed a huge amount of data on alumni and friends. But, also like many institutions, the university had allowed the database to go unused. Development research analyst Stephanie Jewell was aware that other institutions were analyzing their databases to focus their development efforts. "With development budgets shrinking and expenses going up, everyone in the field is more aware of the value of data mining," she reported. "But even so, the data very often just sit there."

To take matters in hand, Stephanie brought us in to help target those donors on whom the development staff should concentrate effort and budget. We used a statistical technique known as list scoring. Based on some very simple criteria—say, does the donor record include a home telephone number? Or was the prospect a member of a fraternity or sorority?—we developed a score for each person in the database and incorporated the score into that record. The possible donors with the highest scores were the ones the university would target for mailing and telephone efforts.

Her data mining experiment paid off. "Working with Data Description has given our department a way we can do something creative with our data to boost all our programs. Now we're able to focus on who we should target—in our phonathon program, for instance, we're only calling the top 10 percent of the group we phoned last year."

At the same time we did the analysis, we taught Stephanie and her colleagues how to take over the data mining, and they have taken most of the analysis in house. We were gratified by the kudos Stephanie gave our training. "Not everybody who needs to do data mining has a strong background in statistics, but John and Peter made sure we were comfortable with one concept and procedure before moving on to the next. And they have been very accessible for follow-up, say when I needed to explain what I was doing to someone else. What makes their training stand out is that they want you to succeed, and they want your organization to succeed."

How can we help you? Learn more about Fundraising Analytics.