Multivariate Models for Donor Targeting

Traditional donor targeting methods often rely on simplistic metrics that fail to capture the complexity of donor behavior. Wealth indicators and past donation amounts alone do not provide a complete picture of a donor’s potential.  

Multivariate models, on the other hand, integrate multiple factors, uncovering nuanced relationships among donor characteristics that can lead to more accurate predictions and higher engagement. By leveraging artificial intelligence (AI) and machine learning, nonprofits can move beyond outdated segmentation strategies and toward data-driven decision making that maximizes fundraising success. 

Example of Multivariate Targeting 

Let’s say we’re going to use an algorithm, a model, and a traditional linear approach. Although a linear model assigns weights to variables independently, a multivariate model accounts for the interactions among them, leading to more precise donor targeting. 

For example, consider a nonprofit organization aiming to identify donors who are most likely to contribute to a capital campaign. A linear model might assess individual factors such as past donation amounts, wealth indicators, and event attendance separately, giving each a fixed weight. A multivariate approach, however, recognizes that these factors do not act in isolation. A donor who has given large gifts in the past but has recently stopped engaging with events may require different targeting strategies than one who gives smaller amounts but has shown increasing involvement in the organization’s activities. 

By incorporating engagement metrics such as email open rates, volunteer participation, and donation recency, multivariate models can provide a much richer picture of donor intent. For instance, if a donor’s largest gift coincided with a personal event — such as a reunion or gala — rather than ongoing support for the nonprofit’s mission, the model could flag this discrepancy and adjust targeting efforts accordingly. 

Importance of Variable Relationships 

In a general linear model, scoring might be fairly effective, but it does not capture the deeper relationships among variables. More advanced models introduce the opportunity to evaluate how variables relate to one another, rather than treating them in isolation. This allows for more refined and accurate donor predictions. 

For example, determining whether a penguin is a hockey player could depend on how well it moves on ice, the number of teeth it has, and where it lives. Traditional models might give a simple yes or no answer based on cumulative scores, while multivariate models assess dependencies among these variables, leading to more nuanced conclusions. 

Real-World Application 

The real power of multivariate models lies in their simplicity and effectiveness. By analyzing factors such as largest gift, event-specific giving behavior, and donation frequency, these models go beyond traditional segmentation techniques. A basic method might continually ask for a large donation based on a past gift, but multivariate approaches incorporate recency, frequency, and behavioral data to refine predictions dynamically. 

For instance, if location is the primary factor (e.g., Antarctica versus Pittsburgh), secondary factors such as mobility on ice or dental history may become irrelevant. This decision-tree–like approach ensures that donor targeting remains precise and data driven. 

Using advanced models to understand variable relationships can dramatically enhance the accuracy and value of donor targeting. AI-driven multivariate models move beyond static segmentation to incorporate complex donor interactions, allowing nonprofits to target their most promising prospects effectively.

Learn more about machine learning and AI for nonprofits in our Quick Byte webinar Your Donor Data + Machine Learning = Your Most Powerful Levers for Improved ROI.