Machine Learning and “Prophecy Trees”: How data helps to predict your donors’ behaviour

Published by Eva Hieninger, Daniel Barco and Izeruwawe Blaise Linaniye on

This article was co-autored by Eva Hieninger (Partner, Managing Director), Daniel Barco (Junior Data Scientist) and Izeruwawe Blaise Linaniye (Project Management & Marketing Automation) at getunik What drives non-profit organizations? Next to the challenge of finding new and better solutions to leave the world a better place, non-profits have to make sure that they can finance their ongoing endeavours. New donors have to be continuously acquired and existing ones need also to be addressed appropriately. With the new possibilities that digital fundraising offers, many tend to overlook one important asset: data. In fact, donor data and machine learning can help non-profits to manage their existing donors more effectively or use their already existing assets by helping to predict future outcomes. Therefore, planning ahead becomes easier. The following article outlines how predicting donor behaviour thanks to machine learning can help organizations to become more efficient.

Meet our perfect donor

Imagine Johanna: young, energetic, smart and generally interested in what goes on around her. But one thing concerns her: pollution, especially the pollution of the world’s water supply. One day she decides, she needs to do her part in order to combat this pollution. During her research, she finds the organisation committed to combating the pollution of the oceans. Impressed by the profile and online presence, she decides to subscribe to the newsletter. Over the following weeks, she gets more insight into the organisation’s work and through her interaction with, for example, it’s social media platforms, the organisation also gets to know Johanna a little better. Therefore, the messages she receives from the organisation become more adjusted to her individual interests. At some point, the organisation will ask her for a donation. Since the online communication is convincing and Johanna wants to do her part, she decides to support the organisation by donating some money. However every organisation depends on reliable and plannable income, so Johanna eventually becomes a regular donor. Up to this point, everything sounds simple enough: The organisation’s communication channels helped to acquire and develop a regular donor. But what do we do once our donors agree to commit to us for longer? How do we keep donors engaged and most importantly how can we identify whether a donor wants to continue to support us or not? This is where machine learning comes into play. Through the collection and categorization of donor data, it is possible to make predictions about how your donors, including Johanna, will probably react in the future. Machine learning can help you calculate the probability of whether a donor is going to continue to support your organisation or not. In other words, it helps us to make predictions about the churn rate of donors, the rate of people likely to stop donating.

How can we use machine learning to predict donor churn?

One of the most common and successful models used for (supervised) machine learning is a random forest, which is based on so-called decision trees. Let’s imagine Johanna is standing in front of a tree, a symbolic, prophetic tree that decides whether Johanna will remain a donor or not. For its prophecy, the tree scans Johanna’s data and its roots dig deep into her data and feed on it. Once the information is acquired it travels up through the tree and its different branches, representing different possible analytical pathways. Each individual branch stands for a distinct analysis of a portion of the data. One branch, for example, scrutinizes how often Johanna opened her emails in the past three months, while another branch checks if Johanna’s credit card will expire in the next six months. The more data the tree feeds on, the more branches will split off the tree’s trunk. Finally, the data feeding the tree and the branches will cause leaves to sprout. Since the tree has prophetic qualities, the leaves will be of different colours. A green leaf stands for a positive answer, signifying that Johanna will continue her support for the organisation. A red leaf, on the other hand, represents a negative outcome and indicates that Johanna is likely to leave the organisation. The tree will drop one leaf which fits Johanna’s data best and this will represent the tree’s prophetic decision.
Now, in the world of data, prophetic trees are nothing out of the ordinary and a multitude of them can grow at any time, which then forms what is called a random forest. In fact, several trees feed on Johanna’s data at the same time and analyse different information about her.
If you want to predict her future behaviour as precisely as possible, you need to look at the different prophetic leaves that fell off the different trees. Collecting all of those leaves in the random forest in order to aggregate the different prophecies will give you one final and more accurate answer.

Trees and leaves? But how likely is it that Johanna is going to stay a donor?

This concept can be translated into a percentage calculation. In fact, machine learning defines by itself, from collected data, which trees are important and should be added to a Johanna’s specific random forest. Then it collects all the necessary and prophetic leaves in order to turn them into a probability percentage. It is important to note that machine learning is not applied punctually. It gathers, analyses, evaluates data continuously and in real-time. Thus, once you are able to use machine learning to scrutinize donor behaviour, you can use the probabilities or predictions made by it to adapt your communication in a way that every donor gets the right message, at the right moment and if necessary over the right channel too. This can best be achieved with the use of a marketing automation tool, where you can introduce the findings from machine learning in order to adapt your messages to different donors at risk of halting their support. On top of knowing who needs to be addressed with more caution, machine learning now provides an automatized and self-updating solution for uncertain donors. Let’s come back to Johanna: We gathered all the leaves that might indicate whether she is at risk of halting her contributions to the organization. You realized that her pile of red leaves is higher than her pile of green leaves, which means that she is at risk of halting her donations. In other terms her churn rate or the probability percentage calculated through machine learning is high and once she crosses a certain threshold your marketing automation tool is told to send out an (automated) email containing, for example, a “Thank you for your support” message to Johanna. This concept gets more interesting when we realize that contrary to human’s machine learning algorithms do not tend to get lost in the woods and can, therefore, create ever bigger random forests able to analyse ever-growing amounts of data. The resulting possibilities for predictive measures are countless. Next to predicting the behaviour of existing or even possible donors, organisations can calculate various other probabilities like for example the number of donations that will be collected, who has the potential to become a major donor and other important information relating to the future well-being of an organisation. Now it is up to you: Are you ready to grow your own forest?


Free Fonts · June 29, 2020 at 11:29

Wow post! This post is incredibly good and informative. Thank you for such an insight. If you are curious to know more about your Free Fonts then you may visit here.

    ovo · August 15, 2023 at 08:01

    The insights and perspectives you offer in your article are truly valuable and thought-provoking.

Ofhsoupkitchen · July 28, 2020 at 04:54

Absolutely! Nonprofit organizations can benefit a great deal from the analysis of their data. Using that conclusion, it is easier to forecast the factors that affect their donor membership and donation amounts.

Musicdel · August 7, 2020 at 18:11

Thanks for sharing

used cars for sale · August 26, 2020 at 09:39

I wanted to thank you for this websites! Thanks for sharing. Great websites!

Jumble · October 13, 2020 at 12:42

like it thanks for sharing great hub

geometry dash · January 3, 2021 at 11:48

Ever since face-to-face first took the fundraising sector by storm, becoming the most successful recruitment channel for regular givers, fundraiser retention has been one of the biggest challenges.

html5 games · April 9, 2021 at 05:47

We collect the best free online html5 games to have fun. These games work perfectly on the browser for your computers and mobile devices. Join us and kill the boredom!

dailyuspost · June 10, 2021 at 10:12

Get Latest news here

gurtimes · June 10, 2021 at 10:13

Get US News here

timesnewsuk · June 10, 2021 at 10:14

Get Latest UK news here

Therapservices Net · July 28, 2021 at 23:02

Great article! Agreed, machine learning is the future of everything imo, predicting behavior isn’t just the future of the donations industry, data is actually shaping everything, from business development to sports to learning. At Therap machine learning is used extensively for a range of purposes.

Andrew · October 7, 2021 at 07:43

Great article! Thank you so much for this informative post.

retro bowl unblocked · October 18, 2023 at 04:06

You’ve written so many good articles on this site that I’ve read them more than once. A lot of what you think is similar to what I think. This is great information for your audience. Please share more!

five nights at freddy's · January 23, 2024 at 04:21

There is a plethora of widely recognized video games in the present day, nevertheless, you may not be acquainted with this particular one. Allocate a portion of your time engaging in gameplay to fully comprehend the exceptional quality of this experience.

marvin · April 30, 2024 at 16:26

awesome post Personal Security Guard Services

Leave a Reply

Your email address will not be published. Required fields are marked *