11 Sep On Metrics, Data, Information, Science, and How We Use Them – Part 1
By David Allen, Development for Conservation
For several months now, I’ve been talking about the fact that I will be doing a workshop on Metrics this year at Rally. Fundraising Metrics, that is. I am, and have been, particularly interested in learning more about what we measure and how we use that information to make decisions.
What I am learning is mostly disappointing.
Two conclusions will serve as leads for the Rally workshop. The first is that we collect a lot of data and not a lot of information. The second is, that as much as we pride ourselves for using science in our mission work, we do not use science in our operating environment, and particularly not in Fundraising.
I am still interested in your thoughts about these. Please help by using the comments section below or emailing me directly (David AT Development For Conservation DOT com). My intent is to use this post, next week’s post, and your comments as handouts for the workshop.
We collect a lot of data, but not a lot of information. For example, we count how many new members join every year, but we don’t track where they came from. We know how many people “like” our Facebook page, and we know the open rate for our eNews, but we don’t know whether or how either one is related to giving. We know what our average gift is, but we don’t know whether it’s trending up, or down or why.
Many of us have goals related to increasing membership to a specific number or by a specific percentage, but they are stuck out there with no strategies attached to the goals. The number of members we have is related to the relationship of our attrition (renewal rate) and our recruitment. Are we gaining more than we are losing? We can increase our membership by improving either or both of those numbers. But beyond that, we don’t have a clue about what to do, or what will actually move, either of those needles. Or, even if what we’re doing now will scale. Or what scaling will cost.
Will we have enough money every year to operationalize our Strategic Plan? Who knows?
The bottom line from my research is this: We don’t use data to make decisions about fundraising. Now admittedly, my sample size is limited, and admittedly, there are exceptions and exceptional organizations out there. But very few compared to the size of the land trust community.
Most of us are far more likely to add some arbitrary riser (like plus 5%) to last year’s budget numbers and limit our ambition to that projection, than we are to actually consider what might be needed. Or, proactively employ proven strategies designed to get us to bigger numbers, much less make the necessary investment.
Worse, in some ways, is that we fudge data to make us look better. For example, we aren’t honest about how much fundraising events cost. We don’t include the value of in-kind contributions on the expense side. We under-count staff time or don’t include that cost at all. And, we don’t include the fulfillment costs of auction items. (Don’t even get me started on the opportunity cost of not spending that same time on activities with more significant ROI.)
Here’s another example: we count members (and calculate renewal rates) as anyone who has given in the last 15 months – sometimes 18, and sometimes 24 – instead of 12. The reason to count members and calculate renewal rates is to project income. Because we don’t budget on a 15-month or 18-month basis, these data points are useless. Or, we somehow consider members differently than memberships or households. The number of voting adults in a household is only important if each one of them is writing checks.
My second finding is that we are reluctant to use science in our fundraising operating environment. This seems ironic for science-based organizations. Science reminds us that the systems at play in our universe are dynamic – always changing. Science reminds us that we are at our most vulnerable when we charge ahead with conviction born of certainty – in the wrong direction. Science reminds us that often what we accept as fact is only theory – and theories need to be constantly challenged and tested.
For example, we embed color photographs in our fundraising letters, but we’ve never actually questioned whether this will help us raise more money. (It doesn’t.)
We prioritize asking Board members to write notes on letters, but we don’t actually track whether or not this works. (It does, but signing the outside of the envelopes and making phone calls are both more effective.) Plus, most of the time it’s random – we don’t track the notes or ask each Board member to write to the same people each year.
We reach for matching gifts under the assumption that having a matching gift available will help drive increased giving, but we actually don’t know that. (It does.)
And we don’t know whether the amount of the match matters – 1:1, 2:1, 5:1, 1:2. (It doesn’t matter.)
And it never occurs to us to ask whether there is a downside. (There is some evidence that training donors to expect their gifts to be matched actually depresses long-term giving.)
But don’t take my word for all this. Be a Scientist! Test it!
A Scientist would start with one of these theories, review the published data that is available related to the theory, and set up an experiment to test whether it’s accurate. A Scientist would make a living by being curious about whether there’s a more effective way.
What questions are you asking of your data? Here are a few of mine:
- If I didn’t change any of my fundraising strategies, but instead kept doing exactly what I am doing now, how much money would I make next year? How much would I make five years from now?
- Are my current strategies sustainable? Are they vulnerable? Are they scalable if I need to make more money?
- If I took one of my strategies and doubled it, would I double my net revenue from that activity? What about ten times? What about for each of my strategies?
- Am I doing enough to meet the goals of my Strategic Plan? If not, what else could I be doing? Or better yet, what should I NOT do, moving forward, to make room for something that might have better promise?
- Does out-sourcing make sense?
- Am I spending enough money on Fundraising?
As we head into the Fall fundraising season, I hope you challenge your own assumptions and test whether there might be a better way. Work at getting data that will help you tell whether you’re on the right track or not. Count things that matter. And prepare yourself for making changes for next year based on that information.
Next week, I’ll give you five metrics I think you can use and detail what I think they can tell you.
Cheers, and have a great week.
Photo by James Frid courtesy of Stocksnap.io.