Wealth Screening Versus Modeling: Making the Best Choice for your Healthcare Organization | npENGAGE

Wealth Screening Versus Modeling: Making the Best Choice for your Healthcare Organization

By on Mar 12, 2018

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A well-run grateful patient program—one that supports top-notch clinical care, nurtures true philanthropy, and is efficient and effective to manage—is at the top of the list for many healthcare organizations.

Rightfully so.

Whether patient fundraising is a new idea at your organization or your development team wants to evolve a long-standing program, the first question is a frequent and practical one.

 

How can we identify our best potential donors when we have so many patients coming through our doors?

Analytics and data are often the answer. They make lighter work of big questions like this and can help inform decisions about organizational focus, resources, and strategies.

The decision that follows—which tools are the right fit—is often much harder than asking the initial question. There are more options than ever before, which is good news. But with more options come more things to consider, and it can be confusing to make sense of it all.

Where do you start?

 

First, understand what “best potential donor” means to your organization.

The answer needs to be more thorough than “people who appreciate our mission” or “wealthy people.” These characteristics are a start, but understanding the balance your organization needs of affinity and capacity in best potential donors is important.

To explain why, let’s consider the two most frequently used tools: wealth screening and custom modeling.

 

Let’s start with a little about wealth screenings. This process represents a fast, reliable way to uncover summary wealth insight for large patient populations. The approach typically looks something like this:

  • Using name and address information, wealth screenings identify prospects through publicly available wealth, biographical, and philanthropic detail.
  • Users often enjoy flexibility in the timing and frequency with which screenings are processed—most often daily, weekly, or monthly—and vendors typically only need a matter of hours to return insight.
  • Results facilitate rounding programs, prospect research, and basic segmentation of prospects into major and annual giving prospect pools.
  • Offices that can manage large amounts of data confidently and have at least one qualified staff person with dedicated time to lead the process (like a prospect researcher or professional database manager) are most successful with wealth screenings.

The second option, called modeling, provides custom, predicative insight about prospects who are likely to take an action with a specific organization.

  • With grateful patient programs, organizations are likely to focus on converting patients to In this case, modeling could combine meaningful data provided by the client (HIPAA compliant, of course), robust publicly available information, and statistical analysis to surface patients who are likely to become donors.
  • Custom modeling is time-consuming and requires more data from the organization; therefore, results are typically produced a couple times a year instead of on demand.
  • Results facilitate acquisition efforts, attention to return on investment (ROI), and pair well with other data for advanced, custom segmentation and prospect Additionally, the process can help you understand patterns, like which service lines are more frequently associated with patient-to-donor conversion.
  • Offices that prefer to manage fewer data points less frequently and prioritize having more patient donors (over patient donors making sizeable gifts) might appreciate this approach.

How about some examples to illustrate these options?

Let’s say I trip and fall in my yard, end up with a sprained ankle, and ultimately seek treatment at my hometown healthcare provider, that has also helped manage my routine preventive and wellness care for more than a decade.

If my provider ran a wealth screening on me post-treatment, it would have basic insight—like a summary of my publicly available assets and a few other things. My indicators are fairly modest and typical for my area, so I’d probably look like a good annual fund prospect and get something in the mail from the organization within a couple weeks asking me for a gift.

Now, let’s imagine I am traveling, trip over my suitcase, sprain my ankle, and end up at the closest urgent care to the airport or hotel.

If this other healthcare system is also wealth screening its outpatients, it would have the same publicly available information to make decisions about asking for my support.

The pros of wealth screening each provider? It’s a fast, easy, and repeatable way to get some basic details about me, and the insight can inform my prospect status based on the providers focus areas and available resources.

If there’s a challenge, it’s that the wealth data makes me look like exactly the same prospect at both places—despite the fact I have an ongoing relationship with the first provider and a one-time interaction with the second provider.

Wealth screenings speak to capacity but not necessarily affinity.

And that’s why modeling has a place in the discussion—to predict affinity. I’d expect to appear more likely to convert to “being” a donor for my hometown provider because it is my medical home, and modeling aims to understand my relationship to help predict behavior.

As a third illustration, what about providers for places or populations that are naturally transient? If the people you serve likely won’t live in your community for many years—and therefore won’t have the same opportunity to build lasting relationships with you—what then?

For example, organizations that work around big military bases might experience this. These providers likely expect a certain percentage of their patients to move away every few years, and they expect to work with people consistently (like I have with my hometown provider) who are new to the area and looking for healthcare. In this case—when modeling results might not be as strong or fully address the need—wealth data can be really helpful. The development staff members might assume that people who own property in the area are invested longer term in the community and, therefore, might be stronger prospects for their grateful patient efforts.

 

More importantly, what does all this mean for our question about best potential donors and how you identify potential grateful patients?

Organizations looking for capacity—and that want to know about it immediately—will likely find a fit in wealth screening tools.

Organizations focused on affinity will likely appreciate modeling—especially if the fundraising office prefers to manage less data and to do so infrequently.

And, those organizations looking for a balance of capacity and affinity in their grateful patient prospects? Consider using wealth screenings and custom modeling together.

ABOUT THE AUTHOR

Liza Turcotte is a Senior Consultant with Blackbaud’s Target Analytics and helps not-for-profit organizations increase support from donors and further their missions through data-driven solutions. 

Before joining Target Analytics, Liza was the Executive Director of the USS Yorktown Foundation, the fundraising partner of Patriots Point Naval & Maritime Museum in Charleston, South Carolina. She also served as the Director of Development for One80 Place, South Carolina’s largest provider of homeless services. Liza began her fundraising career with the Roper St. Francis Foundation, the fundraising arm of a multi-hospital, regional community healthcare system in South Carolina.  Liza grew leadership level annual giving, structured an employee campaign and grateful patient program, and played important roles in the areas of stewardship, communications, and volunteer management.   

Liza earned a Master of Public Administration degree from the Graduate School of the College of Charleston and a Bachelor of Science in Sociology from College of Charleston.  She was a five-year member of the Board of Directors for the Lowcountry Chapter of the Association of Fundraising Professionals.

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