In our recent Water Cooler Chat, Greg Duke, Partner for Database Services, shared the Monte Carlo technique – a way of understanding what the likely range of results would be for a given scenario. In our blog post today, we expand on the concept of using this forecasting technique to take care of three different fundraising needs.
In our recent Water Cooler Chat, we talked about using IRS data to understand charitable giving among US residents. Of course, that afforded me an opportunity to make lots of graphs and charts to study this data more deeply. Here is a look at some interesting statistics related to different US states.
First, notice in this graph that California, Texas, and New York all have the highest levels of median income, but are also the states where people claimed a negative adjusted gross income (see how the little red boxes drop below zero).
“Little by little after a while makes a big pile.” --Anonymous
Over the course of a career, a professional learns a lot of new skills, new locations, new jobs, and new methods. Every time something new gets added to our learning agenda, we can either be excited at the opportunity or resentful of someone else changing our routine. Often we are also feeling too busy doing our normal routines to add in a new skill.
While discussing new skills and ideas in our Water Cooler Chats, I remind myself that not everyone spends entire Sundays learning new skills or playing with new tools. However, I’m a big believer in doing so, and when I teach, I like to break new skills down into workable pieces. This method may also work for you if you are hoping to gain a new skill but also coping with continual interruptions, meetings, deadlines, and other demands.
In our recent Water Cooler Chat, we talked about using sentiment analysis for getting a glimpse into the minds of our constituencies. For instance, is there a way to understand our donor clusters? Do donors to the new computer lab like to give cryptocurrency, for instance? Gift data itself is the most tracked data that we have, since it is audited. This blog post explores more deeply the idea of uncovering donor interest through relationships among pieces of giving data.
Relationship mapping helps identify natural clusters of constituent characteristics, which can then be used to form segmented solicitation efforts. In other words, moving from a “September direct mail” campaign to an “Online donors who give in September to the computer lab” campaign would help us connect much more personally to members of our audience.
In the Water Cooler Chat, I showed the machine learning technique, cluster analysis. Here is the illustration from my presentation.