Predictive Modeling vs. Wealth Screening: Effective Segmentation Programs Require a Healthy Mix
Whenever I am on the road with clients the conversation always turns to a debate about the value of predictive modeling vs. the value of traditional wealth screening. It’s a great debate. Some tend to gravitate toward wealth data alone because it feels more tangible—or understandable—than predictive modeling. While others like that predictive models can focus on the likelihood to do something – make a gift, perhaps – and not just whether a prospect looks “rich.”
The most successful and fully developed prospecting and segmentation programs require a healthy mix of multiple techniques.
I have seen blind spots in every type of strategy. We’ve all heard the stories of the retiree living a modest lifestyle leaving millions of dollars in their bequest. Or, the incredibly wealthy neighbor who has no interest in supporting the local organization down the street. In reality, some likely and capable prospects won’t have publicly identifiable assets but they will have strong modeling scores. Other prospects may look incredibly wealthy from a screening but not be philanthropic. While still others may be statistical outliers that don’t model well but have assets that pique a fundraiser’s curiosity and turn into large major gift donors. The scenarios are endless.
In today’s age of big data, staff at the most sophisticated programs that I have worked with understand that advanced analytics should be used to fuel a well fleshed out prospect identification program for all program areas. Applications include major giving, annual fund, direct mail, planned giving or even niche/sector specific programs like athletic fundraising or grateful patient programs.
Understanding there are blind spots in both predictive modeling and traditional wealth screenings, while living in the age of big data, what new techniques are available for assuring you are creating the most actionable segmentation strategies?
Some of my clients are now turning to predictively derived estimates of wealth and assets—such as household income, investments, discretionary spending and net worth. By marrying both techniques, predictive analytics and the concrete wealth data that many fundraisers crave, you will be positioning your organization well! Target Analytics Affluence, Blackbaud’s newest data set, is an example of where to obtain this type data.
Here are some of the ways in which I have seen organization leverage this type of data:
- Need help trimming down your major gift prospect pool after a large scale likelihood-to-give modeling project? Since major gifts typically come from invested assets, overlay investments or net worth estimates onto your results to further refine your segments
- Need help expanding your major gift prospect pool? Similar to the above, look for prospects with high investment or net worth estimates as an additional tool identify additional prospects that you might have missed via other segmentation techniques
- Want to better understand who might have the financial base to increase their financial support to your annual fund? Leverage discretionary spending estimates that provide insight into the share of wallet a household may have to support the philanthropic sector
- Trying to determine who might be a good prospect for an Annuity? Identify older prospects with higher invested assets and modest annual income or discretionary spending who might be looking an additional guaranteed income stream
- Building your own predictive models? Add all of these types of data points as additional variables.
- Need a descriptive analysis to illustrate household trends in your database? Use these data points as additional markers in your storyline
The debate of wealth screening vs. predictive modeling might not go away any time soon. But, with big data continuing to push us to think about our strategies differently, I am inspired to see our industry continue to evolve with cutting-edge techniques. As a frontline fundraiser, researcher, data analyst or executive at your non-profit, I encourage you to continue to push your colleagues to think strategically – and creatively – about the strategies that you are using.
ABOUT THE AUTHOR
Melissa Bank Stepno Melissa Bank Stepno is Director of Professional Services for Target Analytics, a division of Blackbaud, Inc. and has been consulting with non-profit organizations for 15 years. Her areas of interest include the impact high net worth philanthropy on major giving, using analytics to effectively segment and prioritize within fundraising programs, and working with organizations to assure successful research and prospect management operations. Melissa sits on the Board of Directors for Apra and is an instructor for the Rice University Center for Philanthropy and Nonprofit Leadership. Previously, she has served on the boards of NEDRA, AFP’s Northern New England Chapter and Brandeis University’s Alumni Association. She received her BA from Brandeis University and masters’ degrees in Arts Administration and Higher Education Administration from Boston University.