“The Purloined Letter” is a well known short story by Edgar Allan Poe. It’s a detective story in which the main character, C. Auguste Dupin, is hired to find a valuable letter that has been stolen. Later in the story, Dupin is offered 50,000 francs as a reward for returning the letter. Dupin accepts the offer and immediately produces the letter. As it turns out, the valuable letter was “full in the view of every visitor” and completely overlooked by everyone searching for it, except Dupin.
Any discussion about nonprofit data or analytics reminds me of Poe’s “The Purloined Letter” because so many organizations have a very valuable asset left out in plain sight. The data nonprofits have is often overlooked and undervalued by them. So many nonprofits don’t maximize the hidden treasure they are sitting on.
Everything begins with data quality.
Because we often call it “data hygiene,” people tend to tune it out. A vision of sitting at the dentist—or another less than desirable situation—comes to mind. That’s why I prefer to call it “data health.” This term implies that it’s not only good for you, but necessary.
Target Analytics, a division of Blackbaud, recently analyzed the data health of thousands of nonprofit organizations. The analysis looked at fundamental areas of nonprofit data like addresses, deceased constituents, phone and email addresses, and other com- monly used data.
The results of the analysis were troubling.
- For the average nonprofit, 26% of its house file was unmailable due entirely to bad data. We’re talking outdated or invalid addresses and deceased supporters still in the data.
- For the worst nonprofits in the analysis, that problem grows to 67% of data being unmailable. That’s either lost opportunity or wasted money, depending on how you look at it.
- For the average nonprofit, they were missing email addresses on 74% of its constituents.
- The worst nonprofits in the analysis were missing 96% of their email addresses.
Now, you might be wondering why something as simple as address and email data can have a big impact on the performance of your nonprofit. First, if you’re unable to capture, maintain, and manage the basic elements of data, then you’ll never get to the more complex information. Second, this type of data allows for a wide range of both descriptive and predictive analytics to be used.
Descriptive vs Predictive Analytics
Descriptive analytics
The first advanced use of data is often called descriptive analytics. It allows a nonprofit to know what happened for a very specific group of constituents. This is why data like address, age, income, and other variables is so important to have and for it to be updated regularly. Descriptive analytics is a very good tool for grouping known constituents into segments for marketing or engagement activities.
Predictive analytics
If you want to know who in your data to engage that you haven’t reached out to already, then you need to use predictive modeling. Predictive modeling takes your data and combines it with external data to help predict what might happen in the future. Be warned that predictive modeling requires some data science to get right. Predictive modeling helps to identify constituents in your data that have a higher likelihood to engage, donate, lapse, or renew support. It is also used to help predict the likelihood that someone is a good prospect for a major, planned, or principal gift.
Recently, Target Analytics has been focusing more on prescriptive analytics as a way to help nonprofits make quicker decisions. Rather than giving a potential donor a score of 1 to 1000, prescriptive models indicate whether this person is a better annual fund or major gift prospect. In this way, the prescriptive model offers real recommendations on how to best use information about the constituent. Even organizations that had never used modeling before are seeing improved results from predictive analytics.
Fundraising is both an art and a science.
There is no question that relationship building is an art. We also know that there is a science to donor segmentation and the use of analytics. This requires performing data science on your healthy data to help you focus on the right relationships.
The use of analytics helps to remove a lot of guesswork around which donors have the most affinity for your cause. Through analytics, you can tell which donors have a better likelihood to give to your organization.
The good news is that the technology making this insight possible exists today. That value hidden deep in your nonprofit’s data can be surfaced and used to meet your goals. The key is moving from just collecting the data to cleaning it, sorting it, refining it, and leveraging it in the right ways. Start by doing your own detective work and you may find something very valuable right there in front of you that you never knew existed.
from npENGAGE http://ift.tt/2a9iUzu
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