Raise More, Ask Less — Part 2
Can you really raise more money by asking fewer times?
Absolutely. Or so argues Kevin Schulman in his paper aimed at stirring the pot for our Agitator discussion. Download the paper here.
There are at least two ways to do this. One fairly simple. The other more effective, but requiring a bit more work and thought.
Today we’ll start with the simple one. Tomorrow the one requiring more work and thought.
Method #1. Better Use of Transactional Analysis
In yesterday’s post, using Kevin’s test panels as examples, we saw that average revenue and net per appeal go down significantly — from $3,507 to $1,169 — as the number of appeals increases.
Figure 1 below from Kevin’s paper depicts the concave curve of flatness.
But, you say, isn’t it better to get more money for our cause even if returns are diminishing? Sure.
However, there’s a way to make even ‘mail more, make more’ work better.
And that’s to avoid treating the file as though everyone lives in the same blue curve and should get the same number of appeals no matter what.
Instead, Kevin argues we should be looking at the ‘tolerance’ different donors have for different numbers of ‘asks’. “Is it so hard to think or believe people have different ask tolerances? We readily accept they have different gift frequencies and recency patterns. How do we think they got this way? They are responding differently based on receiving the same 12 or 24 months’ worth of stuff. The reality? There are different curves for different segments of donors.”
Take a look at Figure 2 below and see what happens when ‘ask tolerance’ comes into play.
Kevin notes that when donors are selected by ‘ask tolerance’, there’s a quite different result. If you add up the net revenue of tests for Donor Z (low ask tolerance), Y (medium tolerance) and X (high tolerance), where everyone gets the same 18 appeals, there’s a striking increase in net revenue (by 20% to 30%) from the more ‘ask tolerant’ segments.
At this point it’s abundantly clear that simple ‘ask more, make more’ is a broken or at least badly dented concept. In reality, if we pay attention to ‘ask tolerance’ we can ask less, make same for some donors and spend less and net more for many other donors on the file.
BUT … that’s not the end of it. Kevin believes that the ‘ask tolerance’ improvement is still selling true potential short. “All we’ve really proven thus far, using transactional data and promotion history and statistical modeling, is that you can increase the efficiency of what is an incredibly inefficient process. Hardly earth shattering.”
What if you can ask less, make more, spend less and net even more?
We’ll tackle the answer to that question tomorrow as we explore what Kevin calls ‘a more comprehensive theory and way to do business’.
Anyone out there segmenting files using ‘ask tolerance’?
Roger
P.S. Many readers have weighed in with their thoughts on all this in the ‘Comments’ section and via private emails. Some readers, like David Krear of the National Committee to Preserve Social Security and Medicare have actually tested the ‘ask more’ approach. Tomorrow, we report on this.
8 responses to “Raise More, Ask Less — Part 2”
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OK Roger, so how do you work out ‘ask tolerance’ in the example above? Is it as simple as looking at who responds to what proportion of appeals over that 18 appeal cycle (in which case it’s a circular argument – look, the people who respond to more give more) or are you looking at other data in the file – and if so, what?
Adrian,
First, congrats on the new gig, hope all is well. It is a statistical model built using behavior data. And like every single model built using past behavior for selection (e.g. who is in or out for a campaign) it is circular if viewed from the vantage point of logic. They are all built – this one being no exception – using past outcome data to predict future outcome.
There is zero cause and effect, only effect. But, the point of these models is efficiency, not effectiveness or understanding. But to be sure, there is massive inefficiency in assuming everyone lives on the blue curve, which is precisely what happens in the vast majority of charities when we look at their “active” file and who gets what where, typically, everyone gets everything because 18 beats 15 and 15 beats 12. But a lot of money (in the form of wasted spend) is left on the table in the process.
As Roger says in the post, hardly earth shattering that modeling can make a very inefficient approach less so. The more interesting question is what if we, in addition to getting more efficient, get more effective?
And to do that requires putting teeth in an otherwise toothless donor-centric mantra in a world where we most still operate as if we’re running an assembly line with widgets instead of a human enterprise. The height of irony given how incredibly human the business tends to be on the program side.
Three cheers for modeling and for basic surveys. They both can work wonders when applied properly. At some point in the future most fundraisers will take these for granted like nearly every commercial marketing professional does now.
Just a thank you for this series and conversation. I love it! (And Adrian, thanks for asking before I beat you to it.)
I’m really looking forward to tomorrow. Such important stuff! Most organizations can’t afford churn and burn. Playing the numbers doesn’t help us much. We HAVE to work smarter!
I still don’t understand what this “ask tolerance” model is? I’ve asked several people and no one has ever heard of it. Can you please give more detail as to what this means?
Thanks.
I couldn’t agree more with the idea that we should use basic math to optimize our organization’s net revenue.
While the math is simple, the concept is not. The gray area is where the argument gets won or lost. Much like the ‘global warming’ argument gets killed in the nuance of a cold winter. I hope that the nuance of optimizing your contact cadence does not kill this argument.
At DonorTrends we have developed a Contact Cadence / Audience Audit that scientifically answers the age-old question, ‘Are we mailing too often?’ This is accomplished by mapping the point of diminishing return of your donors.
To address Adrian and Mary’s question, it is not as simple as just looking at those that respond more give more. The premise that you should spend more on those that give more is correct. We should all follow this common sense approach – if 20% of your donors are contributing 70% of the revenue then why would you spend same amount on donors who will not yield significant revenue.
The idea of a contact cadence goes beyond this. This analysis require us to first identify the revenue generated per touch looking at everybody that received all those campaigns, then isolating similar types of donors to further determine the difference in getting the Campaign 1 versus the Campaign 2 (example: the ‘match appeal’ vs. the ‘newsletter’).
Once this analysis is complete, linear programming is applied to optimize a series of linear equations within a given set of constraints. In other words we calculate the optimal number of contacts for each group of donors to generate the best return on investment. Once the cost of an additional contact exceeds the expected increase in revenue you have hit the point of diminishing return.
The bottom line is there is a scientific way to optimize the number of contacts each of your donors are receiving to ensure that your organization meets a specific return on investment goal. This requires an analysis that looks at both promotional history (what campaigns each donor has received) and response data (how those donors have responded).
Ben, is this detailed down to unique donors? Or do you have a way to characterize segments and look at those? (Forgive me if this is a silly question. Math was NOT my best subject.)
Mary, I don’t think it is a silly question at all. Yes in the analysis we prepare we assign a score on the individual level. This score can then be used in an equation to determine the right number of asks. That being said typically a group of donors based on their individual score will be optimized at the same number of contacts. For example tier 1 might warrant 15, and tier 2 warrants 14. Feel free to contact me at ben.miller@donortrends.com and I can explain our process in greater detail.