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8 tips on using predictive analytics in fundraising

The evidence is clear: data-mature nonprofit organizations are more likely to hit or exceed their fundraising goals than less data-mature nonprofit organizations. They’re also more optimistic about their organization’s future ability to raise major gifts.

And with data science advancing and becoming cheaper, it’s no surprise that 97% of surveyed health care development teams use some type of prospect screening and/or predictive analytics software. But identifying the right model or vendor can be challenging if you aren’t clear on what behavior you want your model to zero in on—or your donor database doesn’t have a consistent process for keeping information current.

A computer screen showing data

Predictive analytics in fundraising uses data, statistical algorithms, and machine learning techniques to locate patterns in giving and make predictions about future giving behavior. These models often use both public and private data sets to generate propensity scores which quantify an individual’s giving likelihood.

Based on 30 interviews conducted with industry leaders, here are 8 considerations for your predictive analytics strategy in fundraising.

Prior to making new investments in predictive analytics, consider the following five tips:

  • 1. Identify the question(s) or behavior(s) you want a predictive model to answer.
  • 2. Take a hard look at current prospect research tools and data sets.
  • 3. Evaluate staff expertise and your budget.
  • 4. Clean up your data.
  • 5. Compare multiple vendors.

If you decide to invest in a predictive model, here are three key components to success following implementation.

  • 6. Overcommunicate data insights.
  • 7. Track the return on investment.
  • 8. Engage in process improvement.

Additional resources

For more resources on using technology and data in fundraising, review our library below:

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