Say the phrase "recency, frequency, and monetary" to the wrong person and they will probably be skeptical. "RFM is DEAD," they might proclaim.

Ever since self-driving cars and artificial intelligence became all the rage, descriptive analytics - the idea that you can look at historical data to inform future results - has taken a back seat.

"Why use RFM when I could use a predictive model?"

You might not get that question posed to you very frequently, but I sure do. Working in the analytics business is tricky. Everyone loves being sold the flashy solution: "Give me your donor data, and our artificial intelligence will predict who is the most wealthy and ready to give!" And, if you're anything like the development staff I partner with every day, you've probably tried one of these whiz-bang solutions.

Unfortunately, these predictive solutions tend to do too much. Not too much in a good way, but rather too much in a "this is taking my focus away from something else that could be more beneficial to the organization" kind of way.

Descriptive analytics on the other hand are less sexy but more practical. And you don 't need to spend thousands of dollars on some computer model to get value out of them. You know that donor database you have? Yeah, that one, with the really difficult to use interface? Yep, we can export data straight out of that and end up with some meaningful and practical action items.

It 's 2017 (soon to be 2018!), and certainly predictive analytics have a place in our society. But there 's still plenty to be gained from looking historically at your existing data. Let 's start there.

For this blog post, let 's simply lay down a foundation. After reading this you should have a brief, but important overview of the data landscape and how you can leverage it at your organization. For more tactics and actionable next steps, I'd suggest you join me on December 7th, 2017 for a webinar on just that!

What are descriptive analytics?

Descriptive analytics is a subset of data analytics that solely analyzes historical information. Remember "RFM" from above? Recency, frequency, and monetary value? They are data sets used for descriptive analytics, and they have been staples of the descriptive analytics realm for years. The direct mail sector has used RFM as a guiding light for decades to decide what to mail and to whom.

Descriptive analytics, at least in our context, will tend to deal with transactional behaviors. For example, when a volunteer donates her time, that transaction is data that could be part of a descriptive analytics analysis. Now, don't let all the jargon intimidate you, a "descriptive analytics analysis" of volunteer data could be as simple as asking, "I wonder who has volunteered the most this year?" That 's not too complex, right?

How do I use descriptive analytics?

Descriptive analytics take on great meaning when you pair them together or build a model. While a predictive model guesses who is the "best" or "worst," an RFM model is built around historical data.

For example, you could build a model that shows you which volunteers have given 20+ hours of their time this year, and have also donated for three consecutive years. If you 're thinking of end-of-year action items, you could find out which volunteers who have given 20+ hours of their time this year and made a donation last year, but have not yet renewed their donation this year.

Using descriptive analytics can provide you with a list of people who match the parameters described above, and that list is useful. It isn't sexy; it isn't flashy, but it's useful and pertinent. Your donors' historical behavior can speak volumes about their current mindset and consideration status as well as what they might need from you. When you pair lists like these with email and call scripts you end up with something special: actionable data-driven fundraising. And, the best part is, it's within reach.



ZACH SCHEFSKA is an entrepreneur and data analytics nerd. As Director of the Fundraising Report Card, he helps non-profits harness the power of their data to better carry out their missions. In his spare time, Zach enjoys cooking, exercise, and spending time with his family. You can keep in touch with Zach through his website,

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