Leveraging AI for Donor Pipeline Development: Practical Strategies for Fundraising Teams
Pipeline development is one of the most demanding aspects of fundraising. Identifying, nurturing, and retaining supporters requires a profound understanding of their behavior and the ability to analyze and apply relevant data to drive decision-making.
Not surprisingly, fundraising leaders are intrigued by the potential of artificial intelligence (AI) to make this process more efficient and effective. Harnessing AI to accelerate fundraising starts with prudent planning, awareness of your ethical responsibility, and an understanding of the various options available in both generative AI (such as ChatGPT and large language models) and predictive AI, which can identify historical trends in your data and make recommendations based on sophisticated algorithms.
AI is not a futuristic concept; it’s practical, innovative technology that can transform your fundraising enterprise.
While machines cannot entirely replace humans when it comes to the painstaking discovery, qualification, and stewardship work involved in prospect development, they can reduce the time and effort needed for certain tasks. And they can free up fundraisers to focus on the interpersonal and relational dimensions of donor acquisition, engagement, and retention, while possibly alleviating a need for additional staff.
As you explore the myriad capabilities of automation and machine learning, here are three steps you can take to prepare for the integration of AI and identify the applications that might be right for you.
1. Familiarize yourself with available AI tools.
Before integrating AI into existing workflows, leaders must develop data literacy. Below are some of the exciting ways advancement teams are currently using machine learning to support pipeline development work.
- Predictive Analytics for Discovery. AI-driven predictive analytics can revolutionize how professionals identify potential supporters. These algorithms analyze historical giving data, demographics, and other factors to pinpoint individuals or groups likely to align with your mission. Tools that allow advancement divisions to run these kinds of models are becoming increasingly democratic. Organizations that do not have a data scientist available to them may benefit from designating someone on staff as an in-house resource on AI and machine learning, especially if they have a staff member whose interests and skills align with this field.
- Tailored Engagement Strategies. AI-powered CRMs can segment your database based on various attributes, such as giving history, interests, constituent types, and engagement levels. The newer, more robust CRMs allow you to create highly personalized communication and engagement strategies, sharing content with audiences on a schedule that is ideal for each individual recipient. AI can suggest tailored content, recommend suitable engagement levels, and even determine the best times to connect with constituents.
- Virtual Assistants for Immediate Interaction. Engaging with supporters is an integral part of the process, and AI-driven virtual assistants can provide immediate interaction with your audience. Some solutions offer chatbots that can answer common questions, provide information about your organization, and initiate discussions, making it convenient for potential supporters to get involved.
- Prospective Donor Insights. Because analytics tools can employ AI to quickly analyze publicly available data and determine a person’s potential to support your cause, it can significantly reduce the time and effort required for prospect research. This data can also assist with prioritizing potential supporters and developing balanced gift officer portfolios.
- Donor Behavior Analysis. An understanding of constituent behavior informs effective pipeline development strategies. AI can help analyze behavior by tracking online interactions, responses to communications, and social media engagement. Social listening tools can provide deep insights into advocate behavior to guide your outreach, enabling you to adapt and refine your approach for better results.
- Predictive Lead Scoring. Lead scoring, a technique borrowed from the world of sales and marketing, can be a game-changer in pipeline development. AI can assign scores to potential advocates based on their behavior and attributes. The newer CRM options developed for marketing offer lead scoring features to help you focus your efforts on leads with the highest potential for conversion.
- Continuous Learning and Adaptation. AI systems continually learn and adapt from new data. This means that as you collect more supporter information and engagement data, AI algorithms become more accurate and effective. It’s an investment that pays off over time.
2. Employ change management strategies to increase stakeholder buy-in.
Earlier in my career, I led an initiative introducing AI and machine learning into direct response fundraising to quantifiably improve outcomes. As a result, I recognize the challenges that come with driving and managing the change surrounding new technologies.
Looking at AI through a change management lens allows you to separate the buzz about these innovations from the specific technologies that have a clear application for improving fundraising at your organization. One common change management framework speaks to creating a sense of urgency that validates why change should happen now.
For example, my earliest experiments with AI in direct response marketing involved optimizing the send time of an organization’s emails. I looked at the differences in engagement rates of emails sent at a predetermined time, versus those that were sent based on recipients’ past behaviors, at the times they would be most likely to engage. It was important for me to point to significantly improved email interaction metrics to win over stakeholders and to continue to explore additional AI technology.
Now, the newest AI analytics tools can not only screen your de-identified donor data to identify trends or correlations that a human analyst could miss, but they can also allow you to look backward and forward to predict donor behavior and to help you build a stronger case for why change may be necessary.
3. Define and apply KPIs to measure outcomes.
Wherever you can quantify the improvements that AI and machine learning tools provide (as I was able to do with email engagement metrics), it will help you manage resistance to change.
In situations where it is more difficult to measure the impact—for instance, employing generative AI tools for content development—it can be harder to manage the change, but even in these circumstances, you may be able to streamline your processes enough to document time saved and substantiate the use of these technologies.
Once you identify the key performance indicators that support optimal advancement outcomes, consider how you will measure the impact of introducing AI tools on these outcomes, and allow this data to shape your broader AI adoption strategy.
In the realm of donor engagement and pipeline development, AI is not a futuristic concept; it’s practical, innovative technology that can transform your fundraising enterprise. By incorporating AI into your strategies, you can identify potential advocates more effectively, engage them on a personal level, automate tasks, and make data-driven decisions. With AI’s assistance, fundraisers can enhance pipeline development efforts—ultimately leading to increased support for your mission.
Dana Gresko, Vice President, specializes in leveraging data and analytics to help clients improve fundraising performance and outcomes. For guidance in philanthropic analytics, machine learning capabilities, and technological change management, connect with Dana at email@example.com.