Using Predictive Modeling to Solicit Major Donors
Today's post is an excerpt from a recent post by WealthEngine, the leading wealth research services firm for nonprofit organizations. WealthEngine and Guthrie Theater join us for a webinar April 30 to discuss Best Practices in Soliciting Major Donors, where you can learn how prospect research can play a pivotal role to increase the effectiveness and efficiency of major gift campaigns. Register here.
Among the top philanthropy buzzwords in 2012 were “data” and “data scientist”. Certainly the phrases “predictive modeling” and “data analytics” have made their way into the fundraising lexicon. Predictive modeling can have a significant impact as part of a data-driven annual fund in helping to take some of the unknown variables out of the equation.
To help explain modeling and analytics further, we spoke with WealthEngine’s Vice President of Analytics, Cong Qian.
Q. How would you describe Predictive Modeling or Predictive Analytics?
Cong: Essentially, predictive modeling is trying to summarize what you have accumulated from the past and apply that knowledge to predict future events. In other words, assuming future events will be relatively similar to the past, then let’s see what the future could be, so if this happened then these things are also likely to happen.
Q. What is the process for creating or building a predictive model?
Cong: Usually, a model is trying to answer a specific question, such as “Which of my donors are most likely to give more than $250?” or “Which of my non-donors are most likely to make an annual fund gift?” Statisticians then try to identify the most predictive data elements that can be grouped into a formula to answer the question. At WealthEngine we leverage not only our client’s important data such as giving history, demographic information, etc., but also data from over 60 sources that we are able to find through our wealth screening process, including Propensity To GiveTM codes (P2G), Estimated Giving Capacity, lifestyle and business information and other additional demographic information. All of these data points are evaluated to determine their relationship to each other and their impact upon the desired behavior.
Q. How can this type of information help an organization’s annual fund?
Cong: Modeling is designed to take some of the guess work out of fundraising and therefore target resources more efficiently. By focusing the annual fund on the prospects with a higher likelihood, the response to the solicitation should improve and the return on investment (ROI) should be stronger as well.
For more info on modeling and how WealthEngine uses it to build donor data, read the full post on WealthEngine's blog.