Decision Science

Healthcare fundraising presents unique challenges, particularly relating to the prospect pipeline. Unlike educational institutions with naturally engaged alumni populations, most healthcare organizations and foundations have only a kernel of highly engaged and well-known prospects, donors, volunteers, and patrons. Outside of that kernel, however, the universe of constituents expands rapidly.

In every healthcare community, there is a vast population of less well-known constituents in varying stages of engagement and connection. The outer periphery of this population being those individuals with essentially transactional relationships. Furthermore, there is a high degree of churn within this population. New records are created constantly, with constituents increasing or decreasing in engagement relative to their experiences as time goes on. With such a large, ever changing database, how can healthcare institutions strategically and efficiently identify top prospects?

Fundraising shops employ various methods to optimize resource allocation among their constituent segments. Tactics range from overlaying wealth information in broad batches, to higher touch (and higher cost) individualized efforts like physician referrals, research verifications, and discovery calls. Streamlining this process to ensure the best prospects are filtered to the high-touch, high-cost efforts is critical for organizations with limited human time and resources.

Wealth overlays are key to understanding capacity and potential, but can’t tell the whole story. Based on BWF research, they have an average match rate of 40%–60% of any given population. Moreover, there is often a glut of matches at the $25K–$50K capacity level. For many organizations this is a critical giving level—there needs to be a way to further prioritize this grouping.

Increasingly, the solution lies in leveraging existing in-house data in real time to quantify engagement, calculate likelihood, and inform next steps. We recommend the following key steps to impactful grateful patient identification.

  • Leverage In-House Data. Your internal data is the ultimate proprietary data set. No other external resource can provide raw data that tells an organization more about how their constituents interact directly with the organization. When combined with overlays like wealth, social/digital sentiment, and interest data, HIPAA-compliant internal data is extremely powerful.
  • Identify Top Priority Programs. Work with your leadership team to clearly define your strategic needs, then use your data to identify those key prospects, donors, volunteers, and patrons who compose the target groups you’re looking to grow. Maybe these are your current major donors, donors who were acquired through a specific program, or donors who show an interest in supporting specific types of projects—like capital campaigns.
  • Isolate Distinguishing Predictive Characteristics. Once you know who those target groups are, you can evaluate all their behaviors, characteristics, and attributes in your data to see what, exactly, distinguishes these groups from everyone else. There are many different types of predictive models that can do this—I’ve found binary logistic regression to be a popular and effective choice. Thus, your custom-engineered predictive models use the distinguishing characteristics to identify new donors and segment donors likeliest to respond to solicitations.
  • Score the Constituency. And that’s the ticket; once you’ve built your bespoke model, it’s a simple next step to apply it to everyone in your database based on how they match up against that yardstick of predictive variables. This custom score thus reflects each constituent’s relative likelihood of becoming a donor to your target program, improving your return on investment in resources applied to those constituents.
  • Align Score Refreshes with Key Milestones. Organizations with relatively stable populations that grow and change slowly can get away with scoring their populations every year or two. Organic constituencies are dynamic, however, and the most successful model implementations should reflect this. Especially in the case of high-churn constituencies like those found in healthcare or arts and culture organizations.

Due to the rapid evolutions in patient populations—for instance, the volume of new records created each month—it is critical to integrate this scoring formula dynamically into the database or data warehouse. This ensures new records are scored and hot prospects pop to the top in real time. In instances where dynamic integration is not yet possible with current technology, quarterly refreshes can be timed to coincide with organizational milestones (such as major events, galas, or mailing campaigns).

Grateful patient fundraising programs are uniquely positioned to implement successful predictive modeling projects that connect engaged prospects with the causes they care about the most. Adopting a dynamic scoring integration or a routine refresh schedule further increases the efficacy and shelf life of the original modeling project.

Arts and cultural organizations have leveraged dynamic scoring technology to dramatic effect for many years. A small theatre in Houston incorporated dynamic scoring in 2014, and their models net an additional $100,000 every six months.[1] Healthcare has the opportunity now to do the same—using the very same data already owned in-house.

To learn more about grateful patient predictive modeling and dynamic scoring, including training on these analytical tools, contact the author, Emma Hinke.

[1] Wallace, Nicole. “Data and the Search for Big Donors.” The Chronicle of Philanthropy. 2016.