Looking over Dataro’s suggestions, there are some surprising inclusions. Are they correct?

When it comes to identifying potential donors to contact, machine learning can offer some surprising inclusions that might not have been identified using more traditional models like RFM or RFV. 

Unlike RFM or RFV, which only consider three features (Recency, Frequency, and Monetary Value), Dataro takes into account up to 400 different features, such as a donor's giving history, demographics, interests, and behaviours. By considering a wider range of features, Dataro can provide a more nuanced view of a donor's propensity to give, allowing fundraisers to make more informed decisions.

It's not uncommon for Dataro to include some surprising inclusions in its suggestions for donors to contact. However, this doesn't mean that the suggestions are incorrect. Machine learning works by identifying patterns and trends in data that may not be immediately apparent to humans. So while a suggestion might seem surprising at first glance, it is based on sound data analysis and provide valuable insights into a donor's behaviour and likelihood to give.

Ultimately, the key benefit of using machine learning to inform fundraising decisions is that it can identify potential donors that might otherwise be missed using more traditional models. By leveraging the power of Artificial Intelligence, organisations can unlock new opportunities for donor engagement and increase their fundraising success.