A Roadmap to Using AI in Nonprofit Fundraising

AI in fundraising isn’t just a trend or a choice anymore; its the new standard in fundraising. Though many nonprofits hesitate to implement AI technologies like machine learning due to concerns about the quality or quantity of their data. Others assume that implementing AI-powered donor analytics software is too complex, especially for smaller nonprofits. These concerns create a barrier to nonprofits adopting AI-driven fundraising solutions, despite the potential benefits they offer.

It’s true that the technology underlying AI-driven tools like machine learning models is sophisticated and does require large amounts of data to provide accurate predictions and insights. But it requires less data than you may think and the process is remarkably straightforward and attainable, even for the modest-sized organizations.

This blog outlines what you need to get started on your AI fundraising journey and provides a roadmap for implementing AI in your nonprofit fundraising.

A quick word on machine learning

Machine learning is a subset of AI that identifies patterns in data in order to predict the likelihood of a future event. Machine learning models require a ‘sufficient’ amount of data in order to work effectively and identify emerging patterns in a reliable way. So the key to making this type of AI work for your nonprofit fundraising lies in your CRM and your data.

At Dataro, we’ve been helping nonprofits leverage the power of machine learning in their fundraising since 2017. Our predictive donor analytics software, called Dataro Predict, has helped hundreds of nonprofits of all sizes to unlock the full potential of their donor database, revealing insights about their supporter base that has transformed their fundraising strategies and helped them scale their impact.

Fundraising data & CRM requirements for implementing AI

Integrating with your CRM

Dataro’s AI software solutions can flexibly adapt to many leading CRMs. 

We offer pre-built integrations with Salesforce NPSPS and Raiser’s Edge NXT for free. For other CRMs like Salesforce CRM, RE7, BBCRM, Microsoft Dunamics, ThankQ and Donorfy, we can easily scope out the integration with you to connect. For all other CRMs and database warehouses, we recommend reaching out to our team to chat further about how we can support you with our new BETA ‘Drag & Drop’ integration tool.

For AI to work effectively, there are minimum requirements for your CRM database but you are most certainly capturing the minimum data required as part of your fundraising operations (i.e. soliciting and receiving donations).

To achieve additional predictive accuracy, the following data requirements apply:

Database Size:

• The minimum database size required to implement Dataro Predict is 10,000 donor records (both active and inactive donors).
• There is no minimum database size required to successfully use our Fundraising Intelligence tool.
• The specific data fields and requirements for using our predictive AI software is available  here.

Database Age:

• Minimum Data: Transactions (containing at least 2 years of historical financial transactions)
• Recommended Data: Transactions (containing at least 5 years of historical financial transactions)

A final thought on data

Much of the data required to use machine learning like our Dataro Predict are specific to your organisation, and can only be drawn through your own records pertaining to your donors – this includes information like your transaction histories, unique identifiers for contacts or organisations, or how your donors prefer for you to contact them. This is unique to your donors’ experiences with your nonprofit, and that’s part of what makes AI tools like this so powerful. Because instead of getting generalised trends and insights, you receive predictions and recommendations for your donors specifically, based on your unique interactions them in the past.

As long as your CRM and data meets the above minimum requirements, your nonprofit is a good candidate for using Dataro’s donor propensity software to improve your fundraising operations. 

Additional fundraising data tips from data experts DCA

There are some steps that you should certainly take if you’re considering adopting machine learning in your fundraising. The old adage “garbage in, garbage out,” is never more important than when it comes to the donor information you’re feeding into your machine learning model. In order to get high-quality recommendations from your predictive analytics, your data will still need to meet a minimum standard of quality as well as content.

Here’s some additional tips from our data expert partners DCA to make sure you are AI-ready and in the best position to get started with Dataro:

Critically, your data should be:

Free from duplicate records

If there are many duplicate records in your database, they’ll influence the predictions you get out of your data. Those duplicated records will be overrepresented in your analytics, and it will impact the accuracy of your model. 

Correct and verified

DCA’s long experience in the nonprofit sector has taught them that there are many, many ways for incorrect or outdated data to end up in a CRM system. This can happen as a result of manual data entry, confused customer service interactions, or just data degradation over time. But, the more incorrect information there is sitting in your database, the more likely it is that any analysis produced from that information will reflect inaccuracies too. Consider having a look at data verification and validation processes to make sure your data is as accurate as you can make it.

Consolidated

Some of the information required to use predictive analytics may seem daunting, on the surface. The minimum data requirements ask for two years of transaction history – but in the world of data, change comes rapidly. Your organisation might not even be using the same systems or data fields now that you were using two years ago. Or maybe, you might be using many, separate systems to do the job of a single CRM. These are common issues DCA encounter in the nonprofit space, and they can be intimidating to address. But taking a good look at the structure of your database and homogenising it into a single source of truth as soon as possible will save you so much time and effort later. It will also make it much easier to meet the data requirements for implementing products like Dataro Predict.

Still unsure if your organisation is ready for AI? Take this 1 minute quiz to help you find out whether you are ready to use AI at your nonprofit!