Data Science and Artificial Intelligence for Good

Artificial Intelligence can learn and tell jokes:

ai tells jokes neural network

Credit: Janelle Shane,

And in its free time, AI helps businesses increase revenue and customers.
Applications of AI and machine learning in the for-profit world are our signals to what’s about to happen in the non-profit world. If you haven’t seen the current applications in the business world, I recommend reading Eric Siegel's book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

The most exciting technology, in this space, is natural language generation, which is exactly what it sounds like. Using text mining and natural language processing, a computer can create language, text, and narratives that read like a human wrote them. Companies like Google, AP, and the LA times have found ways to create narratives using facts and natural language generation. In the non-profit space, companies like Gravyty generate natural looking emails to engage prospects.

natural language generation ai

Credit: Slideshare by Automated Insights

An organization that can combine these applications of AI and natural language generation to create small chunks of tasks distributed through a single, easy-to-consume platform will differentiate itself from all the other organizations.

Rather than asking people to look at reports and Excel files, if we can generate small tasks, true next best actions, we are likely to see more action on our insights. This HBR article by none other than Tom Davenport explains how Morgan Stanley is using the principles of next best action.

An organization that can combine these applications of AI and natural language generation to create small chunks of tasks distributed through a single, easy-to-consume platform will differentiate itself from all the other organizations.

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Let’s explore this "next action" idea using a real application in the fundraising world.

USC's Action Center

Using the idea of one action at a time and with the help of our fantastic Salesforce development team, we built an app at USC within our Salesforce mobile instance called Action Center, as seen in the images below. A fundraiser with active assignments sees some of these action items on his or her Salesforce mobile app.

The four main categories a user sees are:

  1. Donor News
  2. Prospect Recommendations
  3. Gift Alerts
  4. Proposal Cleanup

Donor News

We have built a crawling engine that looks for assigned prospects who may be mentioned in the news. We then perform entity matching to ensure that the entity mentioned is the one we were looking for. Later, using all of the news items, we select and display news on the most important and relevant entities.


Prospect Recommendations

If a fundraiser’s portfolio is active with constant visit and qualification activity, we show them an unassigned prospect who they might be interested in qualifying.

These recommendations are based on the characteristics of the fundraiser’s existing assigned prospects. We try to recommend a prospect that is most similar to the majority of the existing prospects. We measure similarity using the prospects’ addresses, degree departments, giving likelihood, and other factors.

With the simple click of a button, a fundraiser can request an assignment or see more details on the prospect without leaving the interface.

Gift Alerts

If a donor from a fundraiser’s portfolio makes a recent gift, we show this to the fundraiser for an easy touch point with the click of a button. Often, if a donor has multiple giving areas, the fundraiser managing the relationship may be unaware of other gifts.

Proposal and Portfolio Cleanup

If a prospect has not been contacted over a certain period, we suggest that the prospect be removed. Similarly, if a proposal has been open over a year, we suggest that the fundraiser update the proposal. Again, with one click a fundraiser can complete both tasks.

Currently, USC Advancement’s fantastic data folks (James Sinclair, Michael Pawlus, Jing Zhou, and Rodger Devine) are exploring network graphs of interests, prospects, opportunities, experts, funders and donors to create auto-generated proposals, leads or opportunities. It may look something like this:

Where do you think AI will take us in the non-profit space? Are you doing something exciting that you would like to share?

This is a modified excerpt from Rodger Devine and my book Data Science for Fundraising: Build Data-Driven Solutions Using R. If you’re excited about these applications, you can find many recipes in this book to create your own novel applications.

About the Author

A co-author of Data Science for Fundraising, an award winning keynote speaker, Ashutosh R. Nandeshwar is one of the few analytics professionals in the higher education industry who has developed analytical solutions for all stages of the student life cycle (from recruitment to giving). He enjoys speaking about the power of data, as well as ranting about data professionals who chase after “interesting” things. He earned his PhD/MS from West Virginia University and his BEng from Nagpur University, all in industrial engineering. Currently, he is leading the data science, reporting, and prospect development efforts at the University of Southern California.