I’m so pleased to let you know that this week begins a new ongoing series on The Intelligent Edge on a topic that’s increasingly important to successful fundraising: analytics. Every third Thursday of the month, HBG’s Associate Director of Data Insight Steve Grimes will provide insight, tips, tricks, and takeaways on how to get the most out of the information that’s already in your database, and what you can add to make it even more useful. In today’s article, Steve shares his thoughts on beginning. ~Helen
Where do I even start?
It’s 2006 and the prospect research analytics community was young. The ‘L’ (do we even call it that anymore?!) was an actual list-serv. Prospect DMM was fledgling. ‘Social media’ was just the ‘internet’ where the ‘world wide web’ was barely a vestige of the public’s lexicon. In short, learning from each other about our work was nascent.
We are in a different day. The technological limits of yesteryear have aged into robust connections with our colleagues today fostering relationships that are a net good for our community, present company included. As you read this, if I have given you something insightful, thank the community. Any shortcomings in my logic are my own.
This blog series is the start of something I want you to ponder with me. A jambalaya of thoughts and questions about our industry flavored by the seasoning of data analytics. Clumsy flourishes like the prior sentence are also part of the deal, but mainly I would like to provide a space for contemplation on prospect research analytics. Hopefully, I am able to illuminate what analytics within prospect research looks like, why it is important, and, for those who are new to it all, help answer the question of “where do I even start?”
I think it’s helpful to first define data analytics in the prospect research space. This is hard. Defining data analytics is the equivalent of defining institutions like education, religion, government, or media. We might be able to get some general form of it all, but ultimately fall short in agreeing on the specifics. Thankfully, smarter people than me have answered this question for us.
Three such individuals within the fundraising space are Josh Birkholz, Ash Nandeshwar, and Rodger Devine. Josh, writing in Fundraising Analytics: Using Data to Guide Strategy, posits that analytics “is a suite of metrical tools and techniques for understanding the past and predicting the future.” Ash and Rodger provide another perspective in Data Science for Fundraising: Build Data-Driven Solutions Using R, noting that the goal of analytics is to “learn something from your data.”
I like both of these explanations because they provide fertile ground for anyone to apply analytics to prospect research. For our purposes then, we might define data analytics in the prospect research space as using tools and techniques to learn something about our prospect data to enhance fundraising decisions. Simple concept, but so full of possibilities!
Defining prospect research analytics is my roundabout way of establishing that analytics within prospect research is distinct from other parts of fundraising analytics. While prospect research can and should be seen as part and parcel to all aspects of fundraising, prospect research analytics comes with its own concepts, techniques, and types of data that are specific to the work.
Keeping in mind this distinction, the answer of “where do I even start?” becomes clearer: start with what you know. I have advocated that anyone can be an analyst, and that anyone can enter this field and thrive. And while true, if prospect research is any part of your day-to-day activities, the answer to “where do I even start?” is that you have already begun!
To be sure, I do not mean to be daft here. When the royal “we” says analytics, we think of data mining, big data, data modeling. However, none of these things would be possible without data and that is, for sure, what prospect researchers deal in. Jennifer Filla and Helen Brown said it best when they wrote in Prospect Research for Fundraisers: The Essential Handbook, “fundraising asks the questions and prospect research answers those questions,” and we do so using data that we work tirelessly to collect on our prospects.
So, how do we take what we know and apply it to prospect research analytics? Well, there are some other questions that come with this approach. What prospect information is important? How do I get that information into a structured data set for analysis? What techniques and tools should I use for that analysis? Should we term it prospect analytics research so that we can have PAR as an acronym rather than PRA? In following blog posts I hope to answer these questions, connect with you on these topics, and continue to move our community forward together.