Predictive Modeling vs. Wealth Screening

I recently read an article about the value of wealth screening versus the value of predictive modeling. The headline immediately made me think, “is the author suggesting using one versus the other to research and find potential donors?” After reading more, it made sense. The two applications work together to effectively help drive donor cultivation. 

Predictive modeling focuses on the inclination to do something such as give, while wealth screening focuses on the capacity or wealth of a potential donor. Predictive models are built from historical data and analyze patterns of those people that have and have not given to identify indicators or patterns of a giving behavior. Data sources for a predictive model include public record data and utilize a wide range of identity and consumer data to define the characteristics of those that will likely donate. On the other hand, wealth screening looks at or uses wealth indicators such as real estate ownership, stock holdings and business affiliations to flag potential donors.

So why use both?

You can definitely use one or the other, but they are both more powerful when used together. Here are reasons why:

• Using data from multiple sources to include more than wealth will improve your prospect pool and uncover more potential donors.
• Utilizing predictive modeling, you have the ability to control the flow and number of prospects into your CRM and the costs associated with the importing process. By using wealth screening AFTER predictive modeling, you can then look for additional financial attributes of a prospect.
• Using a combination of both techniques allows you to segment audiences against major giving, annual fund, direct mail, planned giving and specific causes based on the scoring of the prospect.
• Utilizing predictive modeling helps you to maintain a clean database by identifying exclusions from list processing.

One final thought on the source of the data. Most hospital foundations use the same source data and lists as a wide range of other non-profits. They are all competing for the same dollars. Therefore, you should really think about which data will give you competitive advantages over these other non-profits. Not all predictive models are built the same or produce the same predictive outcome so look for something will add a new dimension to your existing processes.

The value of using both predictive analytics and concrete wealth data will set your organization up for success. The continuing evolution of Big Data will force us to rethink how to use internal data effectively against strategies to find donors willing to act. Using both creates efficiencies with managing your database of potential donors and will improve your speed to donation. 

Any good recipe includes multiple ingredients. It is the same for your business process.