Editor's Note: This article is featured in Best of Connections 2023. Read Apra Content Development Committee Chair Jennifer Moody's editor's message to learn more about the top articles of the year. 

 

I recently received a shout out from one of my campus colleagues: The analysis I prepared for their college was allowing for more strategic conversations with their dean. It gave them insights into new trends and next steps they could implement to fill gaps and improve on past fundraising success. Afterward, colleagues from other schools and colleges asked if I could provide that analysis for their units.

Given the focus around data-driven decision-making in our industry, our colleagues are increasingly asking that we amp up our analytic offerings and capabilities, often without knowing what that means. 

What is a “fundraising analysis?” When you say “model,” does that mean a predictive model, or just a prioritized list or score you can use to figure out who your next call should be?

At the University of Colorado, our research and analytics team (a recent name change from being called the “research team”) has become respected and trusted partners in data, both in looking at data for individual prospects and fulfilling traditional research requests, but also in looking at the big picture in our prospect pools or for donor trends to help with decision-making. It was a journey to get here, with lots of lessons learned along the way.

Our beginnings were very humble. Many of our campus gift officers had inherited bloated portfolios with ad hoc recommendations from years earlier. We began helping them prioritize portfolios, highlighting (literally) donors with higher capacity, those with more recent, loyal and greater giving, incorporating our affinity scores and eventually looking at wealth and outside giving data from our screening vendor. These lists have gone through many iterations, too.

As we helped gift officers focus on prospects more likely to engage with them and remove lapsed and unlikely major gift donors, the next step was to adapt this technique to a pool of potential new prospects, using tiering (or segmentation) to recommend top prospects. For our more data- and excel-savvy gift officers, we include pivot tables to help them prioritize and identify new prospects, featuring indicators they have found success with and standards we know to be effective.

Once we prioritized the prospect pool, it was an intuitive next step to begin analyzing. In the beginning, this meant running basic statistics — how many prospects are in the pool? How many have given recently? How many are managed? From there, we identified criteria for top prospects — those with higher capacity, more recent giving and giving above a particular threshold — and used our top prospect ranking to help assess the strength of the pool. Just because there were a lot of potential donors, did that mean many of them were good prospects for the project or campaign at hand? If so, were these discovery prospects? Prospects who were a good fit for a principal gift officer, or the annual giving or mid-level pool?

Along the way, we’ve needed to enhance the relationships we have with our campus gift officers. The stereotypical introverts in the background now train new gift officers and other advancement staff at orientation, showcasing how we can help. We also attend portfolio reviews and pipeline meetings to hear donor stories and learn who to prioritize. We listen to who they want in their portfolios and who is a better fit for the annual fund. We learn about the giving and donor trends that their partners, deans, chancellors and faculty are interested in.

We have transformed our manual excel highlighting into a points system, utilizing SQL to quickly identify top prospects in specific prospect pools (e.g. our incoming freshman parent class). We’ve begun including analyses of giving trends of the prospect pool along with our pool analyses, creating a framework for the fundraising analyses mentioned earlier.

We have a long way to go, but the things that enabled us to get started — mastering Excel, meeting with gift officers, learning from our Apra community — are straightforward to implement. These skills build with time.

The next time I’m asked for an analysis that I have no idea how to approach, I now have a suite of tools, experience and colleagues to approach who will help me ask the right questions and provide the best insights.

Check back for Part 2, where Katie will share a sample analysis and video tutorial of how to get started with analytics in Excel.