Beyond the Model: The MiLo Intelligence Approach

Machine learning has become an essential tool for nonprofit organizations to improve audience targeting, increase donor engagement, and drive stronger fundraising performance. Looking beyond RFM and a limited set of donor attributes for segmentation and audience selection is an easy decision for organizations. But delivering real results from machine learning requires more than just algorithms. It requires an approach that is transparent, adaptable, and grounded in the realities of nonprofit operations. You get no value from a model if you can’t incorporate it into your constituent-interaction processes.

MiLo Intelligence is designed with this in mind—combining advanced modeling techniques with human expertise and a client-centered process to ensure models deliver measurable value. We are committed to delivering each organization the scores they need on schedule and in a format they can use for direct marketing programs or other outreach. MiLo offers machine learning for nonprofit fundraising.

Purpose-Built for Nonprofit Fundraising

Many machine learning solutions rely on prebuilt, generalized models. While these can be deployed quickly, they often fail to reflect the unique characteristics of an organization’s donors, campaigns, and channels. Each organization is different, and the measures of engagement are nuanced.

MiLo Intelligence takes a different approach.

Each model is custom-built for the specific client and use case, ensuring that outputs align with the nuances of the organization’s fundraising strategies. Whether optimizing audience selection, improving response rates, addressing sustainer retention challenges, or increasing donor value, models are designed to support real-world execution—not just theoretical performance. A model that works great in the lab has no value in the real world if it doesn’t increase response or retention across your constituents.

Built and Guided by Human Expertise

Machine learning is most effective when paired with domain expertise.

MiLo Intelligence models are built and monitored by teams with deep experience in nonprofit data and fundraising operations. We don’t just feed your data into a model; we use our expertise to determine which data is pertinent and revise the modeling features as needed to achieve the best results.

Developed in Partnership with Clients

Models are developed with direct input from clients to ensure they address high-value business questions, align with existing processes, and can be implemented effectively. Client input on what signals engagement for their constituents is essential to capturing what makes a member loyal to the organization. Are all constituents who open emails highly engaged with your organization, or only the ones who click through to take an advocacy action?

Designed for Practical Implementation

Predicting constituent behavior is only useful if you can take some kind of action as a result of the score. Knowing who is likely to churn from your sustainer file is great, but only truly valuable if you can develop an intervention to reduce that risk. We strive to deliver data in a timeframe and format that makes it truly actionable and can be incorporated into existing workflows so organizations can act on insights without added complexity.

A Recent Example: Ask Amount Modeling

About a year ago, a longtime client asked how they could optimize not only response rates but also revenue. With mail costs rising, they wanted to make sure every package was working as hard as possible. They were already comfortable testing different packages and messages to learn what resonated with constituents, so our conversation turned to whether they could apply that same testing mindset to ask strings. While they had experimented in the past, those tests focused on different ask strings for broad audience segments rather than tailoring asks to individual donors.

The model we built uses prior solicitation and giving history to predict whether a constituent’s next gift is likely to be higher than, lower than, or in line with previous giving. After evaluating performance across multiple campaigns, we confirmed that the model successfully made these distinctions for most donors. On their own, the scores were not the end goal, so we worked closely with the client to interpret them in the context of existing giving levels and design ask-string tests aimed at maximizing revenue per thousand across mail segments. In some cases, a less aggressive ask string can improve response enough to produce stronger overall ROI. After several months of testing, the client developed a clear path forward: using the model’s predictions to tailor ask strings within giving levels. For example, three donors with a prior gift of $100 might now receive different asks—one an aggressive string ($100, $200, $300), another a lower string ($100, $150, $200), and another a standard string ($100, $200, $250)—based on what the model predicts they are most likely to give next.

In a recent review of the ask amount model testing the client has been running since last summer, we were able to confirm that deploying the ask strings drove additional revenue per thousand mail pieces, and the client is adopting the model moving forward.

Continuously Monitored and Optimized

In nonprofit fundraising, model performance is not an abstract measure of how accurate the algorithm is or how the data sent to the model changes over time. Those are both critical to ensuring accurate models and actionable predictions, but the true measure is whether it moves the needle on renewal, retention, or whatever fundraising challenge the model was designed for. For that reason, our models are regularly evaluated, adjusted, and rebuilt as needed to reflect changes in donor behavior and market conditions. 

A More Effective Model for Machine Learning

The MiLo Intelligence team strives to keep the human in the loop on model builds and performance monitoring to ensure that models are driving better results. The true value of a model is the outcome for your mission, and we never forget that.

If you have constituent management challenges that you think a model could help with, please reach out. We love nothing more than working directly with an organization to help them define the specific use of model scores and the best way to test them. Collaboration is the key to success for the development and implementation of machine learning models.

Are you ready to start exploring sophisticated, fundraising-informed predictive models? Let’s Talk!