With the proliferation of data, non-profit organizations may start to wonder how they can maximize the value of their data. Take for example the Better Health Foundation (BHF), a hypothetical non-profit organization, which wants to mine its donation data to identify potential opportunities to improve fundraising and grow their revenue. Below is part of BHF’s analysis process.
To begin, BHF would like to get a high-level view of their fundraising performance over the years (Figure 1. Revenue Trend). It sees that its revenue has been in steady decline since 2014. However, BHF was reassured by the fact that their 2014 and 2015 revenue was disproportionally high, possibly due to some major donations.
In fact, despite a decline from the previous two years, 2016 revenue outperforms the revenue from 2008 to 2013, which has consistently been below $1.5M in the final three years (Figure 2. Revenue from 2008 to 2013).
To ensure that major donations were the main cause of the drastic revenue increase in 2014, BHF decides to go with a bar chart view, and changes the breakdown to a monthly basis (Figure 3. Monthly Revenue from 2008 to 2016). By hovering over the March 2014 bar, it sees that although the median Donation was $100, the average donation was almost $9,000. This drastic disparity between median and average indicates that there were indeed a few major donations in that month that drove the revenue up.
To determine why 2015 saw the second highest revenue since 2008, with ~$1M more than the 2016 revenue, BHF looked at the comparative figure (Figure 4. Monthly Revenue of 2015 vs. 2016). By hovering over the bars, BHF discovered that for December 2016, although there was ~6% increase in the number of donors and donations, there was ~75% decrease in revenue. And for January 2016, the percent decrease in number of donations (~2%) was disproportionate to that of the average revenue (~89%). This means that major donations were again the determining factor in 2015’s high revenue. For the other months, both 2015 and 2016 scored wins.
After better understanding their revenue, BHF decides to dive deeper into other performance indicators with a detailed data view (Figure 5. Detailed Donation Statistics View). Since 2009, the number of donors has steadily decreased by ~850; however, the number of donations have increased by ~500, which means that BHF has been doing well in multiple asks, but will benefit from cultivating more new donors and making gift appeals more effective for different constituents.
Because the annual galas are BHF’s most important fundraising event, BHF wants to know how well they are performing. After filtering the donations by appeals related to galas only (Figure 6. Gala Performance from 2008 to 2016), BHF sees that gala revenue has been steadily increasing, after a dip in 2013.
After analyzing the revenue, BHF wants to take a closer look at the breakdown of the donors by analyzing their recency, frequency, and monetary (RFM) value. To begin, BHF investigates its donor retention rate from one year to another (Figure 7). Recency is defined as how long ago since the constituent donated. For example, a recency of “1 to 2 years” on December 31, 2016 means the constituent last donated during the year of 2015. By selecting the desired recency at the bottom of the graph, BHF sees that the retention rate () has been consistent (~60%), as it fluctuates almost proportionally with the number of donors from the year before.
Since one of BHF’s goals is to increase its donor retention rate, it downloads the list of constituents (Figure 8. Detailed Recency Breakdown of 2016) who donated in 2015 but not 2016, in hopes to send a donor survey to learn how BHF can improve their fundraising approach.
Finally, BHF assesses the monetary breakdown of the donations (Figure 9. Monetary Breakdown of Donations from 2016). The top two tiers, “$1000+” and “$100 – $500” donations, account for 83% and 8% of the 2016 revenue, respectively. Thus, BHF determines that they will be important constituent segments for fundraising. As for the segment with the largest amount of donations (31% of total number of donations), “$10 – $25,” BHF will put more efforts into transitioning them into the “$25 – $50” segment. In addition, BHF is pleased to see that the “$1000+” segment has been increasing over the past three years (Figure 10. Trend of $1000+ Tier from 2009 to 2016).
This concludes BHF’s brief survey of its data. We would love to hear from you about how else you think BHF can mine its data! As a side note, BHF uses Iceni Data to create the dashboards, but with some manual work, you could just as easily accomplish all of the above in Excel!