By Ruthie Giles
Nonprofits are continuously combing through various lists of constituents in order to find those who will be the right prospects for a particular initiative. They may be looking for those who can make a certain sized gift, those who can give to a particular fundraising effort, those who may be able to step up their giving level, and those who might be able to become our major donors over time. Whatever the purpose is for this type of proactive prospecting, there also needs to be a strategy for how to approach the data in order to make informed decisions as to how to proceed. No fancy tools are necessary.
As the end of the fiscal year approaches, there’s always a need for some last-minute prospecting strategies to help meet your fundraising goals. Learn to identify prospects who may be willing to give and how to use RFM values to support year-end prospecting. Additionally, understand the importance of going back to deferrers and how to keep your pipeline fed to avoid arriving at the end of the fiscal year with a deficit.
By Dr. Greg Duke
During our Water Cooler Chat, I presented some powerful Excel techniques— Watch the replay video for tips on:
In our February Water Cooler Chat, we were asked if it were possible to do a word cloud from contact reports using Excel because it’s a tool that is familiar and used often.
Although it is possible, the process is clunky. Since Excel wasn’t built to do word clouds, the process does require the use of a macro.
Typically when the Staupell team does word clouds, our tool of choice is Tableau.
If you’re adventurous and want to dive into using Excel for creating your Word Cloud, here are the steps. Watch this video for detailed instructions.
In the world of nonprofit data management, we work really hard to smart code all of our interactions with our audience so that we can successfully report on them. However, we work in a relationship industry and that often requires detailed explanation, which, translated to data speak, is free text. And there is our conundrum.
Processing free text is the domain of artificial intelligence, a discipline that we nonprofit data scientists are learning now. The new processing program, Python, even has a package called Beautiful Soup which parses websites, the text within them, and the HTML tags marking any variety of content. The R program also has a package called SentimentAnalysis which assigns a sentiment to different words by using the package’s dictionary, called a lexicon.
But we can do some of this analysis without an artificial intelligence program. Here are some steps to work your way into trying out text parsing, starting with the easy stuff and working toward the sophisticated stuff.