Guest post by Jessica Kostuck, Data Analyst, Annual Giving, Queen’s University
During my first few weeks on the job, my AD set me up on several calls with colleagues in similar, data-driven roles, at universities across the country. One such call was with Kevin MacDonell, keeper of CoolData, with whom I had a delightfully geeked-out conversation about predictive modeling. We ran the gamut of weird and wonderful data points, ending on the concept of donor volatility.
When a lapsed high-end donor has no discernable annual giving pattern, is it possible to use his or her years of non-giving to predict and influence their next big gift?
Our goal for our Annual Giving program was to identify these “volatile” donors (lapsed high-end donors with an erratic giving history), and reactivate (ideally, upgrade) them, through a targeted solicitation with an aggressive ask string.
(For more on volatility, see Odd but true findings? Upgrading annual donors are “erratic” and “volatile”, which describes findings that suggest the best prospects for a big upgrade in giving are those who are “erratic”, i.e. have prior giving but are not loyal, every-year donors, and “volatile”, i.e. are inconsistent about the amounts they give.)
I did some stock market research (see footnote), decided on a minimum value for the entry-point into our volatility matrix ($500), and, together with Senior Programmer Analyst, Kim Wilkinson, got cracking on writing a program to identify volatile donors.
Our ideal volatile donors had given ≥ $500 at least twice in the last 10 years, without any consecutive (“stable”) periods. Year over year, our ideal volatile donor would act in one of three ways – increase their giving by at least 60%, decrease their giving by at least 60%, or not give at all. Given the capacity level displayed by these volatile donors, we replaced years of very low-end giving <$99) with null values (“throwaway gifts”).
We had strict conditions for what would remove a donor from our table. If a donor had two years of consecutive giving within a ±60% differential from their previous highest giving point (v_value), we considered this a natural (or, at least, for this test, not sufficiently irregular) fluctuation in giving, and they were removed from the table. If the donor had two consecutive years of low-end (but not null) giving ($99-$499), this was considered a deliberate decrease, and they, too, were removed. Conversely, if a donor had two consecutive years of greatly increased giving, this was considered a deliberate increase, and they were also removed.
At any point, a donor could be admitted, or readmitted into our volatility matrix, by establishing, or re-establishing, a v_value and subsequent valid volatility point.
The difference between a lapsed donor and a volatile donor
Below is a sample pool of donors we examined.
Donor 1 is volatile all the way through, with greatly varying levels of giving, culminating in two years of non-giving. Donor 1 is currently volatile, and thus enters our test group.
Donor 2 is volatile for two years – FY07-08 and FY08-09 (v_value of $5,000 in FY07-08, followed by a valid volatile point in FY08-09 with a decrease of -80%), but then is removed from the table in FY09-10 with only a -50% decrease in giving. They do not establish a new v_value, even though their FY09-10 giving meets the minimum giving threshold for this test, because of their consecutive, only marginally decreased giving in FY10-11. This excludes Donor 2 from our test.
Donor 3 enters our volatility matrix in FY04-05, leaves in FY07-08, reenters in FY10-11, and maintains volatility to current day, and, thus, enters into our test solicitation.
While all three of these donors are lapsed, and are all SYBUNTs, only Donor 1 and Donor 3 are, by our definition, volatile.
Solicitation strategy and results
We now had a pool of constituents who were at least two years lapsed in giving, who all had a history of inconsistent, but not unsubstantial, contributions to the university. In an email solicitation, we presented constituents with both upgrade language and an aggressive ask matrix, beginning at a minimum of +60% of their highest ever v_value, regardless of where they were in the ebb and flow of their volatility cycle. Again, the goal of this test was to (1) identify donors with high capacity (2) whose giving to the university was erratic in frequency and loyalty and (3) encourage these donors to reactivate at greater than their previously-established high-end giving.
In our results analysis, we broadened our examination to include any gifts received from our testing pool within the subsequent four weeks, not just gifts linked to this particular solicitation code, to verify the legitimacy of tagging these donors as volatile – that is, having a higher-than-average probability to reactivate at a high-end giving level.
An important part of our analysis included comparing our testing pool to a control pool, pairing each of our volatile donors with a non-volatile twin who shared as many points of fiscal and biographic information as was possible.
Within the four-week time frame, our test group had about a 7% activity rate, whereas our control group had an activity rate of about 5% (average for the institution during this timeframe). Within our volatility test group, 50% of donors gave an amount that would plot a valid point on our volatility matrix.
Conclusion and next steps
Through our experiment, we sought to identify volatile donors, and test if we could trigger a reactivation in giving, ideally at, or greater than, their highest level on record.
Since not all of the donors within our test group made their gifts to the coded solicitation with the volatile ask matrix, it is indiscernible whether being presented with language and ask amounts that reflected their elusive giving behavior prompted a gift – volatile or otherwise. However, we do feel confident that we’re onto something when it comes to identifying and predicting the behavior of a particular, valuable set of donors to our institution.
Our above-average response rate (both versus the control group, and institution-wide) supports our “theory of volatility”, insofar as that volatile donors are an existing pool with shared behaviors within our donor population. We plan to re-run this test again at the same time next year, continuing our search to find a pattern within the instability.
Were we able to gather definitive results that will define and shape future annual giving strategy? Not exactly. But as far as data goes, this was definitely cool.
Jessica Kostuck is the Data Analyst, Annual Giving at Queen’s University in Kingston, Ontario. She can be reached at email@example.com.
1. Varadi, David. “Volatility Differentials: High/Low Volatility versus Close/Close Volatility (HVL-CCV).” CSS Analytics. 29 Mar. 2011. Web. Winter 2015.