With a variety of internal and external data easily available, prospect researchers and analysts are able to provide greater insight to reach their organization’s goals. In order for data to help grow the prospect pipeline and inform decisions, one must be able to turn this data into valuable measurements.
I learned this first-hand in my initial attempt to create an affinity score to assist with prospect identification. To prepare for the task, I asked myself a series of questions. Some of those questions included:
What is affinity?
How do I define affinity for my organization?
Who would I consider has a high affinity for my organization?
What data is available to me that supports the statement, “a constituent has a high affinity?”
Do all significant donors or volunteers represent their affinity in the same way?
Which data points that are common between “high affinity constituents” and new prospects are, or are not, coincidental?
Which data points have greater ‘weight’ than others?
Which data points should have a maximum capacity in the resulting total score?
All of these and more were critical in my attempt to create a score.
Please note the use of the word ‘attempt’ above. I stress this because there is quite a bit of trial and error in the path to a final product. This is a project where one must continuously validate, adapt and iterate until the results successfully inform the decisions of your team.
Do not be afraid to try this on your own. There are services that can help with the process, but Excel is a great tool to begin the data manipulation required to calculate your score. Whether or not you use Excel or a specialized application for developing a score, it does not eliminate the need to question and understand affinity for your organization.
So I ask you, what is affinity for your organization?