How Machine Learning Can Support Business Development with Institutional Donors

Institutional fundraising teams are under pressure. Budgets are tight. Proposal deadlines are nonstop. And competition for funding has never been fiercer. That’s why more NGOs are looking at data and automation, not just to save time, but to make smarter decisions about where to focus limited resources. 

Machine learning is one way to do that.

It’s not futuristic or overly technical. In fact, many of the tools your team already uses are powered by machine learning, and the methods behind them are more accessible than you might think.

In this article, we’ll explain how machine learning methods like supervised and unsupervised learning can support institutional business development, particularly when it comes to identifying, prioritizing, and preparing funding opportunities.

What Machine Learning Can Help You Do

In simple terms, machine learning is about recognizing patterns in data and making predictions or decisions based on that.

For fundraising and BD teams, that translates to:

• Identifying the right funding opportunities

• Prioritizing which RFPs are worth your time

• Strengthening proposals with data-backed insights

Below, we’ll walk through how different machine learning methods show up in practical tools and use cases - no technical background required.

Supervised Learning: Predicting What Works

Supervised learning is what most people think of when they hear “predictive analytics.” It’s based on having labeled historical data: like past proposals, win/loss records, budgets, partners, and timelines. You can use this data to build simple models that estimate what’s likely to happen in the future.

Regression

Regression models estimate numeric outcomes. Linear regression helps you forecast things like expected grant size. Logistic regression, on the other hand, estimates the likelihood of a binary outcome—for example, whether a proposal will be successful or not.

You don’t need a data scientist to start. These models are available in tools like Excel, Google Sheets (via extensions), and more advanced platforms like Google AutoML.

Example use case: Estimate your chances of winning an RFP based on features like sector, consortium size, and past funder history.

Decision Trees and Random Forests

These are transparent, rule-based models that can help you understand what combinations of features lead to a higher chance of success. Think of them as automated “if/then” logic based on data.

Example use case: You might find that proposals in health sectors, with budgets over €5 million and at least one UN agency partner, have a significantly higher win rate.

Boosted Trees

Boosted trees (such as XGBoost or LightGBM) are advanced models that are particularly good at ranking. They’re often used in situations where you want to predict relative likelihoods.

Example use case: Rank dozens of open RFPs based on how likely you are to win them, helping you decide where to focus proposal development resources.

Unsupervised Learning: Finding Patterns Without Labels

Unsupervised learning is used when you don’t have labeled data—for example, you don’t know the outcome yet, but you want to find meaningful patterns in your dataset. These techniques are particularly helpful for donor analysis and market segmentation.

Clustering

Clustering algorithms group similar items together based on their characteristics. For fundraising, that might mean segmenting donors by funding behavior, thematic interests, or grant size.

Example use case: Identify one group of donors that tends to fund pilot projects under €500,000, and another that focuses on scaling proven programs in specific regions. These insights help you tailor your outreach and proposals.

Clustering is built into tools like Tableau and Power BI, and available in more advanced analytics platforms.

Dimensionality Reduction

When you’re working with large datasets—think hundreds of RFP features or thousands of proposal records—dimensionality reduction helps simplify the data while preserving the most important patterns.

Example use case: Reduce a dataset of 50 features down to 5 key variables that actually explain most of the variance in award outcomes. This helps you focus on what matters most in your proposals.

How BD Teams Are Already Using These Tools

Even if you’re not building models from scratch, many BD professionals are already using machine learning–powered tools in their workflow. These include:

• Funding search platforms that recommend opportunities based on your organization’s profile and past interests

• Proposal development tools that highlight key language used in successful grants

• Internal dashboards that use clustering and forecasting to analyze donor data

• Text generation tools that help streamline drafting when combined with strong human oversight

The point is: machine learning isn’t a separate thing. It’s already embedded in tools your team may be using. The opportunity now is to use these capabilities more intentionally—especially when it comes to targeting and prioritizing opportunities.

Why This Matters for Institutional Business Development

Machine learning methods can support stronger decision-making across the BD pipeline. Here’s what that looks like in practice:

• Better targeting. Use donor segmentation models to match the right funders with the strengths of your organization.

• Smarter prioritization. Use predictive models to assess the likelihood of winning before investing time in proposal development.

• Stronger proposals. Use data from past awards to guide structure, language, and content—especially in sections like methodology, MEL, or budget justification.

• More credible projections. Use regression or forecasting tools to provide evidence-backed estimates for outcomes, especially in long-term proposals.

The Role of People Still Comes First

No tool can replace trust, relationships, or institutional memory. Business development in the nonprofit world will always require the human touch—understanding donor intent, navigating complex partnerships, and telling compelling stories that resonate.

But machine learning can help you focus your time where it matters most. It can reduce the guesswork. And it can help you turn past experiences into better future decisions.

This Is the Kind of Work Mission AI Builds for Clients

The tools, logic, and examples in this article aren’t just theory. They reflect the kinds of systems Mission AI builds and trains NGO teams to use every day.

From scoring RFPs to clustering donor behavior to integrating AI into your proposal development workflow—Mission AI helps business development teams work smarter, not harder. And always in a way that keeps the human layer front and center.

If you're looking to build these capabilities internally, you don’t have to start from scratch.

Want to Put This Into Practice?

This article gave you a clear overview of how machine learning methods apply to fundraising and business development. Now it’s time to go from reading about it to using it — consistently, and with confidence.

Join our 2-hour interactive workshop this October, built specifically for professionals who’ve tried AI tools like ChatGPT or Claude AI — but haven’t gotten the results they want.

This session is designed to move you beyond casual use and into repeatable, high-quality outcomes.

You’ll learn:

• How to use ChatGPT, Claude AI, and other key tools more effectively

• Prompt engineering techniques for sharper, more reliable outputs

• How to choose the right AI tool for the task

• Practical examples of AI in business development (plus ideas you can apply elsewhere)

• How to integrate AI into your workflows for better efficiency and faster decision-making

Date: October 14, 3pm CET

Format: Live online (Zoom)Price: €50 (live session only) 

Reserve your spot: signup here

If this article resonated with you, this workshop will show you exactly how to apply it — and help your team make smarter moves with less effort.

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