Fundraising with AI: Five steps to improve your charity’s data readiness

Many charities give only minimal attention to their data. While some have moved forward with robust, data-centered strategies for fundraising and supporter engagement, many still view data strategy as an IT /infrastructure issue.

As AI-powered tools for fundraising are adopted more widely, charities will look to improve fundraising results by focusing on effective management and leveraging of supporter data, and putting more attention on data quality and strategy.

Here are five steps to start improving your charity’s data readiness for fundraising with AI.

  1. Take stock of your existing data resources. Many charities lack a comprehensive audit of their own internal data stores. Older charities likely have a deep supporter database history that can be tapped for knowledge. Newer charities may have thinner donor CRMs and more supporter data in other sources such as social media/digital platforms.
  2. Bring scattered data files into a connected data system. It’s still common for supporter data to be locked away in Excel spreadsheets or in CSV files on a file server. Where possible, this data should be moved into a data warehouse where it can be part of your living, active data ecosystem.
  3. Interconnect data systems. As supporters interact with different parts of your organization, they may be leaving a trail that is scattered across different data sources. This includes 3rd party digital platforms that you may be using for email or events. Setting up data flows between different data sources helps unify these pieces into complete profiles to better understand donor relationships and behaviors.
  4. Clean and align data. Your data almost certainly contains a lot of incomplete, out-of-date, badly-formatted and just plain incorrect data. This is especially true if you are interconnecting databases with lots of historical data. It can be a monumental task to clean large datasets and consistently align key variables, but it is a critical step to building accurate AI models.
  5. Consider improving data through augmentation. Your supporter datasets will primarily contain information from past and current interactions within your operations. Adding external information about your supporters from publicly available sources, such as census, real-estate, wealth index, or political/voting data sources will bring a richer, more complete set of data points into reach for AI modelling.

These steps all build toward a robust, in-house data-management strategy for your organization that will pay off in many ways beyond readiness for AI-powered fundraising.

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