Looking for a shorthand way to understand how you can improve donor lifetime value by 130%? Then take a look at the infographic below – The Anatomy of a Committed Donor – prepared by our colleague Kevin Schulman over at DonorVoice.
Kevin’s infographic (click image to enlarge) distills the essence of the landmark National Donor Commitment Study conducted this fall. It identifies the seven key steps an organization (not the donor, but the organization itself) can take to improve donor commitment/loyalty and thus increase its bottom-line dramatically.
In short, it is the actions an organization takes that affects a donor’s attitude; and it is the donor’s attitude that affects – positively or negatively – the donor’s behavior.
On the surface this all seems simple enough. And, in fact it really is, EXCEPT our old, bad habits keep getting in the way. One of those bad habits, of which I’m as guilty as the next fundraiser, is mistaking myth for truth.
Myth: If a donor give us their email address they’re a more loyal/committed donor.
Fact: There’s absolutely no proof of this.
Myth: The more online activity donors participate in, the greater the likelihood that they’ll be better, more committed donors.
Fact: There’s absolutely no proof of this.
Myth: If a donor makes the effort to send in a change of address, they’re a more committed donor than someone who doesn’t.
Fact: There’s absolutely no proof of this.
BUT….REGARDLESS of facts and reality, countless fundraisers continue to segment donor files based solely on donor behavior, hoping to somehow improve the bottom-line. Sadly, it’s not gonna happen, at least not in a reliable consistent manner.
Why? Because of an immutable law of science, statistics and logic: “Correlation does not imply causation.” This ‘law’ emphasizes that correlation between two variables (for example, sending in a change of address and being a more loyal donor) does not automatically imply that one causes the other.
Stick with me please, and I’ll treat you to ice cream – and murder. Only as an example, of course.
Historically the murder rate has always trended positively with an increase in ice cream sales. In Poli-Sci 101 this is known as a “false causation” — i.e., correlation does not imply causation.
It is known that throughout the year murder rates and ice cream sales are highly correlated. That is, as murder rates rise, so does the sale of ice cream. There are three possible explanations for this correlation:
Possibility #1: Murders cause people to purchase ice cream. One could imagine a world where this is true. Perhaps when one is murdered, they are resurrected as zombies who feed on ice cream. (Or go online, or send in their changes of address.)
Possibility #2. Purchasing ice cream causes people to murder or be murdered. Again, one could imagine a world where this is true. Perhaps when one eats ice cream those without ice cream become jealous and murder those with ice cream. (Or frequently fill out petitions and take other online actions.)
Possibility #3. There is a third variable — what statisticians call a “confounding variable” — that causes the increase in BOTH ice cream sales AND murder rates. For instance, the weather.
When it’s cold and wintry, people stay at home rather than go outside and murder people. They also probably don’t eat a lot of ice cream. When it’s hot and summery people spend more time outside interacting with each other, and are more likely to get into the kinds of situations that lead to murder. They are also probably buying ice cream, because nothing beats the sound of an ice cream truck on a blazing summer day.
In this example, the weather is a variable that confounds the relationship between ice cream sales and murder rates. Sometimes this is also called the “third variable problem”, which refers to the fact that anytime we observe the relationship among two variables there’s always the possibility that some third variable which we don’t know about is responsible for ‘confounding’ the relationship.
Setting the ice cream and murder example aside, the confounding variable in behavior-based fundraising correlations — e.g., providing change of address and giving more — is usually donor commitment … how donors feel about your organization that causes them to care enough to alert you to their new address in the first place. And unlike the extremely limiting tactic one might employ in a “correlation only” world — in this case, making it easier for people to tell you about their new address — you should instead focus on the cause and, more specifically, actions your organization can take that directly impact commitment levels, to get the corresponding behavior-based benefit, whether donors move or not!
For example, perhaps you thanked the donor in a timely manner, or told a great story about someone you helped, or otherwise reinforced the notion that your organization is right on top of its mission.
I use ice cream and murder to illustrate why it’s important to always be on the lookout for confounding variables. They can make us reach conclusions that are wrong. Confounding variables can make us miss enormous opportunity. Or, to use slightly more technical language: Confounding variables = BAD.
So, how does this apply to what we fundraisers see and what we do? Sure, we see that we get better performance from people who spend a lot of time online, but that’s probably not what’s spiking their performance. The question we need to be asking and answering is “what did we do (what actions did we take) that created a positive attitude on the part of the donor to participate more?
It is the actions we take, not the donor, that cause or create the confounding variable called commitment.
‘Donor commitment’ is the equivalent of ‘the weather’ in our ice cream/murder example. But unlike the weather, your organization can create and destroy Donor Commitment. Just don’t try to measure or determine true Commitment by behavior, because, like love, it’s an attitudinal construct.
The problem with simply relying on donor behavior is that it points to no causal path. That’s one of the problems with the conventional segmentation that most fundraisers use. Traditional segmentation generally leads to identification of a false causation pattern.
If you take a look at Kevin’s Infographic and go to the section that summarizes online behavior it is easy to draw the conclusion that the more folks are online, the more likely they are to be more financially valuable. But, as Kevin points out, this is a false or confounding variable.
And therein lies our mistake. We look at our behavioral segmentation patterns and draw faulty conclusions. E.g. we should try to get more e-mail addresses or we should encourage people who are good donors to also provide e-mail addresses. Or, since many good donors provide change of addresses we should mount a change of address campaign. NOT!
Don’t get me wrong, it is important to look for patterns and it’s not outrageous to assume that people who are providing e-mail addresses are likely to be better and more committed donors. But unfortunately, one doesn’t follow the other. On its face this pattern may not seem as outrageous as the ice cream/murder scenario; but at the end of the day, both have confounding variables.
What is so wonderful is that in the case of fundraising – unlike murder and ice cream and the weather– you can control this one.
Why? Because you can measure and manage commitment and determine what you do that impacts it. Behavior matters – yours! It causes commitment (or kills it) and the resultant donor commitment causes their behavior.
The fact that you send people 10 or 12 direct mail pieces as opposed to four in a year may lead to more money, but that’s not why the response is better. The response is better for attitudinal reasons––you may be providing more information, you may be informing the donor more frequently, etc. Your actions are affecting their attitude and that, in turn, determines and drives their behavior toward you.
To paraphrase: the question we all need to ask and answer is not what our donors’ behavior can do for us, but what we can do to improve our donors’ attitudes. When we do this, the good behavior of a committed donor will follow.
Roger
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Good points here, but I have serious concerns about some of your conclusions. Very few fundraisers, in fact, go to the trouble of analyzing donor behaviour (in the form of affinity-based activities such as sending in changes of address and engaging with the organization online), and I’m not sure why you think this is an “old, bad habit.” I wish they WOULD make more frequent study of these things! I’m not sure how to interpret your assertion that there’s no proof that “the more online activity donors participate in, the greater the likelihood that they’ll be better, more committed donors.” I would agree with you that “proof” may be lacking, but only because “proof” is contingent on the unique realities of each institution’s own constituency. It’s human behaviour, not physics — there is no universal law we can prove or disprove. But if you claim there’s no connection, I cannot agree with you. There is ample evidence available that affinity-based behaviours are predictive of giving. (I’ve published a number of papers on my blog that support that.) You are quite right in saying that correlation is not causation, but data miners and predictive modelers are less concerned with causation than they are with the predictive power of correlation and association. The “confounding variable” you speak of is engagement and affinity itself, which may not be directly measurable but which manifests itself in a variety of ways that ARE detectable and measurable. Where I totally agree with you is that we are prone to make a lot of mistakes when it comes to thinking about causation, as if we only need to collect someone’s email in order to make them more likely to give — that is, of course, absurd! Where you lose me is when you insist that if a variable isn’t causal, then it must be worthless. Nothing could be further from the truth. Private-sector companies in data-rich industries (especially online) pay very close attention to customer behaviour; the ones that don’t probably aren’t very profitable. We in the nonprofit sector waste an awful lot of time and money attempting to convert prospects who are not engaged and never will be, and failing to focus our energies on the highly engaged. I think we overestimate our ability to sway potential donors via our messages, and underestimate the extent to which affinity (expressed in behaviour) determines support. Choosing to ignore signals of affinity (or the lack of such signals) is irresponsible.
Hi Roger,
Some great points, and I agree wholehearedly with your point about mistaking correlation for causation, but like Kevin I disagree with the implication that we shouldn’t use these indications for analysis because there may be confounding factors.
The reality is that in many situations we don’t have access to these confounding variables – such as a independent measurement of engagement – so we look to other variables to stand in for them. Insteading of confounding, the variables are proxies for what we want to measure. To use your ice cream and murder example, if I want to predict murders, and I don’t have temperature records accessible I may then look to ice cream sales as a proxy variable to stand in for it. I could predict murder rates based on ice cream sales (although I’m not saying you should). An example I often use is the Google fluwatch system, where they use searches on flu symptoms to predict outbreaks. The flu itself is the cause, but all we have to measure is people’s behaviour in response.
Thanks for the post to get us talking!
Every now and then it’s probably good to make assertions that contradict hard evidence. It DOES get people talking.
The Agitator is nothing if not about making bold assertions and shaking things up. However we firmly believe it is done with evidence on our side. Sometimes, it does however require clarification so let me attempt to do that here.
We believe firmly in the need for the fundraising industry to get far more sophisticated in its targeting efforts, using predictive models, external data and yes, affinity markers or proxies. However, we also believe the industry needs to get much more serious about MAKING more good donors to augment more sophisticated targeting.
Kevin, in his comments, is 100% right when he says predictive modelers are not concerned with causation. They deal with the world as it is served up to them and what non-profits need to focus on is changing that world. Good donors are not born, they are created, but behavior based approaches alone will NEVER do a good job at CREATION, hence the dreadful retention rates.
Today’s 0 to 12 month donor is tomorrow’s 13 month plus donor. Using affinity markers or other behavior based proxies (beyond RFM variables) to get better at targeting those in the 13 plus who are “worthy” of being pulled back into 0 to 12 “status” is worthwhile but it is also a losing battle if not seriously bolstered with far more understanding of CAUSE and the confounding variables.
This is being done on a large scale by our commercial brethren who, in addition to spending oodles and oodles of money on data mining (which we support) also dedicate huge sums to brand building, customer service and relationship building.
More specifically, the confounding variable of “engagement” (we prefer to call it Commitment) can not only be defined and measured but managed by the non-profit to MAKE more good donors.
So, where we take issue with the comments to this post is twofold: 1) this is very doable and needs to be done. That it is not yet being done in any meaningful way in this sector only bolsters the argument. 2) we would argue the exact opposite of Kevin’s assertion that as non profits “we overestimate our ability to sway potential donors via our messages”. If the sector truly believes this then it is forever lost and we need to clean house. Seriously.
We know as well as anybody that targeting is the number one “variable” to dictate response on appeals – more so than the message. So yes, better targeting equals better response and more effort should be focused there. However, one CANNOT simply target his/her way to long term fiscal prosperity. The vast majority of donors attrite, they have negative lifetime values and the leaky bucket gets leakier and leakier all the time. This industry has been taking in its own laundry for too long and the tipping point is already upon us.
Let us also be clear, the case for putting resources towards “making more good donors” (i.e. educating, persuading, motivating and relationship building) need not be some soft, fuzzy, just-believe concept. It can be done with financial projections and metrics just as rigorous as the campaign and behavior based ones we are so familiar with in the lead generation business now called fundraising.
Thank you all for your comments and discussion on this critically important subject.
Roger
Excellent clarification. The purview of predictive modeling in fundraising is NOT the whole picture — you’re quite right. So, what’s the big picture in higher education fundraising (which is what I know)? What are the causative agents that bring an alumnus/na to give to alma mater? Now we’re talking big picture! Looming largest, probably, is student experience: having made lifelong friends, having been involved in campus social life via societies and sports and parties, having had excellent professors and mentorship opportunities, having the lasting impression that one’s degree has been a worthwhile achievement in terms of career as well as memories … In Annual Fund, we do well to evoke the emotions associated with the memory of one’s student life (particularly undergraduate student life) rather than toot our horn in more abstract ways that fail to connect on an emotional level. So is messaging important? Yes, and lets do our testing to see what works best. But let’s not flatter ourselves by thinking that our message has somehow “caused” people to give. We’re just tapping into a vein of feeling that was already there.
Should we explore causation? Well, the few fundraising shops with the resources to properly conduct that kind of experimental research should go for it. But I think they’ll find the underlying causes (such as student experience, and institutional reputation) are well outside their purview. Better that every part of the university — from admissions to the registrar’s office to student services to the faculty — understand that they are all partly responsible for the future financial health of the institution, to the extent that it relies on the support of alumni who found their time in university worthwhile and rewarding.
In the meantime, I see fundraisers obsessing over whether to use postage on return envelopes, obsessing over comma placement in letters, and on and on. Sure, that’s what they control (institutional reputation? not so much), but I think that time would be better spent figuring out who among our alumni have indicators of affinity but may not have expressed that affinity through giving yet.
That’s higher ed. The non-higher ed world, which I cannot speak to, may be very different.