Month: October 2025

Every fundraiser knows the challenge: reaching the right donors with the right message is harder than ever. Mailboxes are full, budgets are tight, and the pressure to deliver results never lets up. Too often, nonprofits lean on singular targeting strategies—like mailing based on past giving alone—that miss the bigger picture.

Most organizations still rely on traditional targeting strategies that focus on a single data point or outcome model. While these methods feel comfortable and tried-and-true, they’re leaving money on the table. The solution? Multivariate modeling backed by Donor Science™ offers a smarter, data-driven path to stronger targeting and higher ROI.

Traditional Donor Targeting: A Single-Lens Approach

Many organizations still rely on one-dimensional metrics, like last gift, total giving history, or simple demographic markers, to decide who receives a direct mail appeal. This method is familiar, straightforward, and easy to apply—which explains its staying power.

But here’s the drawback: single-lens targeting overlooks the complex reality of donor behavior. A donor who gave $50 last year might be ready to upgrade to $100, but your model, focused only on past gift amounts, might miss this opportunity. Meanwhile, you’re sending expensive mailings to donors who show no real engagement signals, leading to wasted resources and missed connections with your most promising prospects.

The Case for Multivariate Modeling

Multivariate modeling takes a different approach by using multiple metrics simultaneously to capture different aspects of donor potential. Multivariate modeling shifts the focus from one data point to a richer, layered view. Instead of just asking, “Will they give?,” these models answer multiple questions at once:

• Response likelihood – Who is most likely to give?
• Gift amount prediction – How much will they give?
• Channel preference – What’s their favored way to connect?
• Retention potential – Who is most likely to give again?

The math is simple: more dimensions equal better predictions. A donor who scores high on response likelihood but low on gift amount gets a different message than someone who’s less likely to respond but typically gives larger gifts when they do.

Donor Science: The Power Behind Multivariate Models

This is where Donor Science™ comes in—the blend of data analytics, behavioral science, and fundraising expertise that makes sophisticated modeling actually work in the real world. Donor Science™ doesn’t just crunch numbers; it identifies the key variables that matter, ensures model accuracy, and uncovers hidden patterns in your donor data that spreadsheets alone might miss.

For example, Donor Science™ can uncover mid-level donors with high upgrade potential—a segment you might never have identified using traditional methods.

How Multivariate Models Strengthen Direct Mail Campaigns

Applied to direct mail, multivariate modeling ensures nonprofits aren’t just reaching donors—they’re reaching them in the most effective way. Consider these scenarios:

• Combining response likelihood with upgrade variables allows an appeal to not only secure more gifts but also increase average gift size.
• Pairing loyalty and channel preference variables helps retain donors by meeting them where they’re most comfortable.

The impact of multivariate mailing on ROI is significant: fewer wasted mailings, higher response rates, and better donor lifetime value. When you stop treating all donors the same, you start seeing results that reflect the true potential of your supporter base.

Overcoming Barriers to Adoption

Some nonprofits hesitate, worrying about complexity, cost, or whether their data is “good enough.” But today’s tools and methods have made multivariate modeling more accessible than ever.

The key is partnering with experts in Donor Science™ who understand both the technical side and the nuances of successful fundraising. They can help you navigate the transition and ensure your models actually improve campaign performance rather than just adding complexity.

The Takeaway

Traditional one-dimensional approaches served nonprofits well in the past, but they’re outdated in today’s data-rich fundraising world. Multivariate modeling—fueled by Donor Science™—offers a smarter, more effective way to connect with donors who are ready to support your mission.

The question isn’t whether you have enough data to make multivariate modeling work—you probably do. The question is whether you’re ready to use that data to transform your direct mail campaigns and turn information into donations. Your donors are complex, multifaceted people. Your targeting strategy should be, too.

What makes Queen such a great band? For one thing, the tone and timbre of their songs are distinctive. Was their sound due to Freddy Mercury? His voice is among the most celebrated in the pantheon of rock singers. Was it the blend of vocals from all members of the band? Well, both things can be true. In their opus, Bohemian Rhapsody, Mercury provides a soulful expression of regret and despair. As the song progresses, his bandmates, Brian May, John Deacon, and Roger Taylor, create an air of tension that builds to an energetic crescendo. An individual singer can create a distinctive identity. At the same time, multiple singers create a unique experience. The same principle applies to predictive analytics—and it’s central to how VeraData approaches donor modeling.

Ensemble analytical models operate like multiple singers. They improve performance by smoothing rough edges and amplifying their combined forecasts. Ensemble models combine two or more predictive models to improve accuracy and robustness. Instead of relying on a single model, they leverage the strengths of various models, often resulting in better overall performance and reduced generalization error. A model created from past performance, wealth, and financial data will offer some predictive qualities. The same is true for performance and propensity data. Performance and giving behaviors will also yield forecasts. Yet, when viewed in the aggregate, the three models offer better predictions of donor behavior than any one model alone. This ensemble approach is one of the foundational elements of VeraData’s Donor Science™ methodology. 

Ensemble models provide three key benefits:

Improved Accuracy

Blending multiple models produces predictions that align more closely with real-world outcomes. Differences between individual models balance each other out, sharpening the overall result.

Reduced Overfitting

When models rely too heavily on narrow training data, they can miss the variety of outcomes we see in practice. Ensemble models counter this by pulling from different data sources, making the predictions more realistic and broadly applicable.

Increased Robustness

Data isn’t always perfect. Missing values, anomalies, or inconsistent sources can throw off a single model. By combining models, those disruptions are minimized, resulting in forecasts that hold steady across uncertainty.

Why This Matters for Donor Science

No single dataset tells the entire donor story. Ensemble models also provide different perspectives, which have the potential to create better outcomes. Ensemble models built from various datasets provide a diverse expression of behavior and characteristics. They enable the model to reflect the actual performance of those individuals.

Like Freddie Mercury, the performance of any single model can be extraordinary. Yet, Queen isn’t the same if it’s only him. The blend of voices, coupled with the musicianship of the rest of the band, creates a unique quality of greatness. Ensemble models contribute their individual predictions to a more effective combined forecast. At VeraData, our Donor Science approach brings these ensemble models together to give nonprofits clearer insights and stronger predictions that lead to better results. Providers who don’t use ensemble approaches may still find value in their analytics—but they’ll miss opportunities for the deeper accuracy and robustness that VeraData delivers.

Written by Tom Hutchison, Product Manager at VeraData