Best Practices for Donor Reactivation

Reactivating donors holds immense potential for nonprofits that want to maximize the value of their donor base. Traditional methods of targeting lapsed donors have relied heavily on recency, frequency, and monetary (RFM) models. Although RFM has been a functional approach for years, it presents limitations that can hinder the effectiveness of donor reactivation efforts.  

With the advent of machine learning and advanced analytics, organizations now have the opportunity to make donor targeting more precise, individualized, and forward looking. By adopting current technologies and making informed decisions, nonprofits can see significant returns even from lapsed donors.  

The Limitations of Traditional RFM Targeting 

Historically, nonprofits have segmented donors based on broad recency and donation amount buckets. This approach assumes that all donors within a segment behave similarly. Although it has been effective to an extent, it suffers from several key issues: 

Lack of Individualization 

Traditional segmentation treats all donors within a category the same, even though they may have widely varying giving patterns and potential values. 

Flaw of Averages 

RFM segments are based on average performance, which can obscure individual donor potential. A segment with an average gift amount of $50, for example, may include donors who give significantly less and some who give significantly more, leading to suboptimal targeting. 

Backward-Looking Analysis 

RFM models rely on past data to make future decisions, assuming that the same donors will behave in the same way. This assumption can be flawed and may lead to missed opportunities. 

Inability to Identify Hidden Opportunities  

Traditional segmentation excludes potential donors who might have been overlooked due to arbitrary cutoffs in recency and giving levels. 

Identify Goals and Seek Expertise for AI 

AI is everywhere. Everyone's talking about it. Everyone claims they’re doing it. Anyone can dabble in machine learning, AI, and advanced analytics. However, experts can be trained in the academic and statistical rigor needed to execute through these tools and these solutions. But different users will come up with different outcomes. 

There are different ways to use AI within your organization, depending on your goals. You must identify what you want to accomplish with AI and machine learning before you come up with a strategy. Do you want to use these tools to better target campaigns? Do you want to use them to identify your very best donors? 

There may not be an easy, off-the-shelf solution for your needs, so in many cases, you may need to do research and find someone with expertise in setting up these tools for your specific use case.  

Leveraging Machine Learning and AI for Higher ROI 

Machine learning and advanced analytics offer a paradigm shift in how organizations approach donor reactivation. Instead of relying on static segmentations, these models analyze vast amounts of data dynamically to make individualized predictions. 

Some of the key advantages of using machine learning in donor reactivation include:  

• Individualized Targeting: Machine learning allows nonprofits to assess donors on a case-by-case basis rather than making broad segment-level decisions. This helps in identifying donors with the highest potential for reengagement. 

• Removing Low-Value Targets: Although all donors are charitable, not all provide a strong return on investment (ROI). Machine learning enables organizations to filter out donors who are unlikely to reactivate, thereby focusing resources more effectively. 

• Discovering Hidden Donors: Traditional methods often ignore deeply lapsed or lower-value donors who may still have potential. Machine learning uncovers those “hidden gems” who might otherwise go unnoticed. 

• Factorizing Complex Data: The human brain can process only so many variables at once. Machine learning models can analyze thousands of data points, identifying intricate patterns and correlations that would be impossible to detect manually. 

• Forward-Looking Predictions: Unlike traditional RFM, which looks at past behaviors, machine learning models predict future behavior, allowing organizations to make strategic decisions with confidence. 

Data-Driven Decision Making 

By incorporating machine learning into donor reactivation strategies, organizations can make smarter investment decisions in their marketing and outreach efforts. 

• Eliminating Wasteful Spending: Traditional models leave nonprofits guessing which half of their marketing dollars are wasted. With machine learning, organizations can pinpoint exactly which donors are unlikely to respond, thereby optimizing their budget allocation. 

• Improving ROI: Machine learning makes it possible to adjust targeting strategies dynamically based on evolving donor behaviors, ensuring that marketing efforts generate higher returns. 

• Enhancing Strategic Planning: Nonprofits can use predictive modeling not only for reactivation, but also for long-term cultivation strategies, improving lifetime donor value predictions. 

Machine learning provides a powerful tool for nonprofits, enabling precise targeting, eliminating waste, and identifying hidden opportunities for reengagement. By leveraging advanced analytics, organizations can improve donor retention, increase ROI, and make smarter, forward-looking decisions that maximize the impact of their fundraising efforts. 

The worst result is a model unused. If a tree falls in the woods and no one’s around to hear it, you don’t improve your ROI. Learn more about machine learning and AI for nonprofits through our Quick Byte webinar Your Donor Data + Machine Learning = Your Most Powerful Levers for Improved ROI.