Machine learning and first-party data. One’s a tool, and the other is an ingredient to really drive improved return on investment (ROI). Knowing how machine learning can be applied to drive some pretty amazing results empowers your nonprofit to improve its reach and generate more donations. It’s even more important now, during a time when many traditional targeting methods that have worked great for decades are getting a little dated.
Each day, the experiences your donors have, the decisions they face, and the opportunities that arise for them are shaped by their past behaviors. The for-profit world monitors these decisions and turns that knowledge into a curated experience.
For example, when a donor logs in to Netflix, wanting to watch a show or movie, they’re presented with choices based on what they’ve liked, finished, and stopped watching in the past. On Facebook, the targeted ads are (some would say eerily) more and more targeted to extremely nuanced interests, from the style of clothes the users wear to what they tend to buy.
At the grocery store, the prices and what’s available in stock at your store depend on your ZIP code and buying trends. This is captured using your loyalty discount card. Retailers don’t want to give you $10 cash back on every $1,000 you spend just because they want to — they do it because they want to know what you’re buying.
Even life-or-death decisions are now informed by very advanced mathematical algorithms. This includes serious decisions about the choice of medicine that has the best chance of curing a disease or determining the best course of treatment. The nation’s top hospitals are all data-science–oriented now.
We’ve been surrounded by AI (artificial intelligence) and machine learning in the corporate world for many years. Unfortunately, most nonprofits still use segmentation strategies that date back to when donor records on cards were physically shuffled in a backroom to decide who should receive marketing or mail.
None of the elements of AI or machine learning are cutting edge anymore for most industries, and it’s time for the nonprofit sector to catch up to gain all of the benefits of using this technology in donor engagement, outreach, and marketing efforts.
One of the most common concerns nonprofits bring up when considering how to implement machine learning, AI, and other data-science opportunities is the cleanliness of their data. They believe their data is a mess, and many fear that data hygiene might be a barrier to using these tools.
The good news is that most organizations already have the data necessary to capitalize on machine learning, AI, and some other tools. Often, you don’t have to overstretch or be too intrusive. In most cases, you’re already collecting the right data in terms of donor histories and who your donors are. It’s the math and how this data is used that can be improved.
For many nonprofits, the idea of implementing machine learning and AI can feel overwhelming. However, getting started doesn’t have to be complicated or expensive if you consider four critical focuses.
Before diving into AI, take an inventory of the data you already collect. Most nonprofits already gather valuable first-party data, including:
Identify what you want to achieve with AI and machine learning. Some common nonprofit goals include:
You don’t need a dedicated data-science team to begin leveraging AI. Many user-friendly, no-code tools exist that integrate with common nonprofit software. A few examples include:
Even free or low-cost solutions such as Google Analytics can provide AI-driven insights into donor behavior.
Rather than overhauling your entire approach, start with a small-scale AI project. For example:
Track performance metrics to see what’s working and iterate based on results.
This blog is only is a start. Learn more about machine learning and AI for nonprofits in our Quick Byte webinar Your Donor Data + Machine Learning = Your Most Powerful Levers for Improved ROI.