Fundraising has never been easy. But it’s never been this hard. So, in an effort to fix the acquisition and retention problem, the sector’s looking for new/better ways to profile and segment prospects and supporters. But (with a handful of exceptions that I’ll share in the next post) no one’s been successful.
The mindset is 100 percent correct, but the methodology isn’t. So, it’s worth looking at where so much profiling and segmentation goes wrong.
The first mis-step is usually some kind of supporter survey and/or focus group. Behavioural science has repeatedly shown people can’t reliably answer these questions, yet for some reason we keep asking them.
It’s not that people purposefully lie, but because they fail to predict their future behaviour. Or because the nature of the question is such that it can’t be answered in a reliable way (e.g. “We carry out a range of activities to help [insert cause here], which would you like to hear more about?” “What reasons might you stop supporting a charity?” “Will you vote for Brexit/Trump etc.”).
We want these answers, but we’ll never get them with these questions.
There is always some set of variables used to create segments. Either winging it or a statistical grouping method (the latter leads to a false sense of confidence). The question that really matters is what variables get used. Why?
Whatever variables are used to create segments you will, by definition, see differences on those exact same variables when profiling. This is circular and tautological. But it is important because profiling of the segments, on the very variables used to create them, is what is used to feel confident about the segments being different.
If you have more data on your CRM, and you add it to your segments, you will see even more differences. The more data you use to describe, the more different they’ll look. These differences are as inevitable as they are irrelevant.
When giving behavior variables are used, in whole or part, to create segments you’ve reduced your segmentation into a selection tool (in other words it describes what they did, not why they did/will do it). Why?
Because giving behavior is being used to explain giving behavior! It is all effect, zero cause.
This is no different than regular RFV selection since the behavior part drowns out any other variables. But those other variables (e.g. attitudes about your cause and wanting to help those in need) will make it look like it is something different, something tied to motivation, etc. It isn’t.
Because there was no theoretical basis for the attitudes used to create the segments. It just intuitively feels right to ask a series of global questions on how people feel (e.g. about your cause and supporting charities etc.) and then grouping based on the responses.
When you do this you merely wind up finding a number of segments with varying degrees of feeling on the battery of statements. Which is neither interesting or useful.
When the descriptive profiling is done on these attitude-only segments there is often little behavior difference. So, some of those behaviors are thrown into the set of variables used to create the segments. And there you have it; instant “differences” on behaviors you care about across your segmentation. But in reality, it’s just a weak form of RFV selection.
Many use demographics along with attitudes to ensure differences in the segments (to fit some preconceived, totally false notion that demographics matter). Adding age to the attitude variables will, by definition, create segments that think a bit differently (on random but alluring irrelevant info) and that are different average ages. But so would adding star-sign, hair colour or height!
This approach always yields an enormous number of supposedly “strategic” segments. (I’ve seen them range from seven to 30+ segments!) If they were real (which by virtue of how they were created is impossible) they’d be impossible to deliver on.
All told it’s an enormously costly effort to produce segments that put the “less” in useless.
Again, the mind-set of re-segmenting based on a deeper understanding of supporters is 100% right. But it’s not hard to see this popular methodology is dangerously wrong. In the next post I’ll share exactly how you should segment and how it has led to results like these.