After witnessing the world spiral in the past few months (and seeing how far we must go to achieve social justice), I think we all can agree that there are no quick routes to work through the social injustices around us. In fact, our efforts must go beyond bringing alive the concepts of diversity, equity, inclusion and accessibility to frontline fundraising and donor relationship only. Regardless of our titles and departments in the organization, each of us must ask self what is my role in contributing towards equity through my work? I do the same as research and analytics professional.
In this article, I want to share 4-ways in which I pushed for a different than usual flavor of research and analytics for my consulting engagements. Ways that were aimed to bring all constituents together so the decision-making from the analysis would be inclusive of the whole community they served. In these 4-ways I will share not only what we did but also how those research activities shaped the next-steps conversation.
Here they are:
1. During engagement analysis: extend RFM and include all constituents.
My task was straightforward – based on the available data, evaluate the engagement of donors. But, instead of restricting to the current donors, the Campaign Director and I decided to include all constituents. Thus, this constituent group had volunteers, gala attendees, members (as a membership-based organization), former supporters, and all board. This decision took our number of records from 450-ish to close to 1500. Including all constituents in the analysis allowed us to expand the team’s outlook of “engagement” beyond current mid-level/major donors only. Besides, they were a team of 4 fundraisers with not an entirely optimized portfolio. So, broadening the population for assessing engagement also created a path for portfolio optimization dialogue later.
Now, because we included a broader population than the donors, I realized data for RFM score (Recency, Frequency, and Monetary aspects of giving) would not be available for all constituencies. So, we decided to extend RFM to a score composed of
participation (event and online program participation), and
relationship (where a current volunteer/donor/member has higher weightage than former volunteer/donor/member; former being anyone not associated, in any for for the last three years).
This analysis allowed us to look at the constituents from a broader lens than pure giving. Outcome? Conversations of holistic portfolio optimization and leveraging the engaged names for their upcoming fall campaign.
2. During stewardship survey: extend the participant list to all constituents.
Last year, I was a pro-bono research advisor for an immigrant mentorship organization that wasn’t very old. The organization was celebrating its third anniversary when I volunteered with them. Back then, the organization had about 1500 constituents – primarily volunteers, mentees, and mentors. About 70-75 of those constituents were the organization’s donors. My job as part of the volunteering was to develop a survey tool for stewarding their donors. The Director of Development and I decided to evaluate if a strategy change could be worth it instead of jumping straight to questionnaire design. And we began with the question, why do we only need to steward the donors? I loved the direction that conversation went! We decided we are only everyone who has some association with the organization and, of course, has a valid email address (duh!).
That survey was so wildly successful that it led to two reasons for celebration:
We crafted a focused strategy for 2021 summer and fall programming (that has been so far very popular) and
They used the results of the survey to include in their corporate sponsorship. Not only did they secure funding more than needed, but they also opened channels for more mentors and mentees from those corporate offices. Win-win!
3. During campaign dashboard assessment: ensure inclusivity for all dashboard users
A few months back, I was working for an organization in the middle of their 7-year campaign. They had a lean (and vital) prospect research team for roughly 35 fundraisers. The organization had set up necessary dashboards to track their campaign progress. I was brought as an interim program manager to evaluate dashboards and identify any inefficiencies in campaign-related processes.
Two issues (as observed for many organizations) were abundantly clear from the start:
The dashboards lived in two places (SharePoint and DropBox) in 2-3 different formats: PDFs, images, and linked URLs.
Of the 35 fundraisers (including the campaign leadership team), only 7-10 accessed the dashboard regularly to follow the real-time updates. Others either depended on the bi-weekly strategy meetings (when the campaign progress was announced) or asked their colleagues for printouts. And, upon a closer look to understand the why behind this behavior, it seemed the dashboards, even though they reported the correct numbers in visual charts, lacked some inclusivity basics.
So, we composed a subset team (having a representative of their IT team, two fundraisers who were very engaged with the dashboard, and two who weren’t so engaged with the dashboard). And from there, we identified the critical challenges in the holistic usage of those dashboards. We concluded our final discussion with four major action items:
Remove all redundant formats of the dashboard only to keep one linked URL to the live dashboard.
Develop a “help” page on the dashboard that explains all metrics, calculations, and underlying data.
Create a limited-time learn-and-learn session for no more than 7-10 fundraisers at a time – where the nuts and bolts of the dashboard were shown and discussed. This strategy created a safe space for the fundraisers to ask their technical questions without hesitation.
Build a 20-mins in every team-wide donor strategy meeting agenda to chat about “so what does this mean?”. And, the Campaign Director was encouraged to welcome different fundraisers for every meeting to share their perspectives. That way, no fundraiser had to feel pressured to explain the metrics/charts but can still bring forward their questions/perspectives/interpretation.
Though initially, it took some time to pick the pace for adopting the ideas (especially lunch and learn), the organization is now at a robust place where the overall comfort level of all their fundraisers is higher than before.
4. During predicting major gift donors using predictive analytics: review how you set up your algorithm.
Machine learning-based analytics can be used to predict if your prospect is likely going to be a major gift donor or not. That is what I did in my engagement with a mid-size membership-based cycling advocacy organization. My task was to leverage their data and identify their likely major gift donors for the year-end campaign. To give a simplified version – in predictive algorithms, we use a set of independent variables (e.g., giving, volunteering, event participation, etc.) to predict a dependent variable (e.g., an indicator for “major gift donor?” that can be a yes/no).
In setting up our model, we changed how we defined that dependent variable (i.e., the indicator “major gift donor?”). Usually, it would be described as - someone who gives more than the major gift threshold dollar amount is set to be “yes”, else “no”. Instead, we approached it differently. The Director of Development shared that the organization was old (more than 25 years old), but the fundraising team had always been lean. Their retention was notoriously low, so he suspected that the organization never really had a solid approach to identifying and soliciting major gifts. That means the major gift donors were those whom the then fundraisers asked for gifts. To give numbers – this organization had about 5000 constituents, and they had roughly 150 major donors. The DOD suspected that number should have been way higher with the proper research long back.
So, I went back to conduct an engagement analysis and a screening from their prospect research tool. Combining those two, I created a score to identify who was likely to make a major gift. And that changed our dependent variable described above (i.e., the indicator “major gift donor?”) to include not only those who had made a major gift but also those who were likely to make one (based on their engagement and wealth). This modeling allowed us to understand engagement patterns from a much broader population than merely the 150 major donors.
Remember, to achieve that equity we strive for, we must take a closer look at our “usual” research practices and be willing to change those practices with a clear why. It is not going to be easy, but we must take the first step.