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 form 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 automatised 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 humans 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 amount 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?