TESTING: Baby Steps are for Babies
(aka The Myth of Testing One Variable at a Time)
After our earlier posts on the dangers of overly simplistic testing, you could despair of getting any legitimate test results for your file with noise in the data and large quantities required.
There is, however, good news. If you embrace larger-scale testing, you can break the famous “test only one thing at a time” rule.
Let’s say that instead of trying to get the highest response rate possible, you were trying to reach the highest point on Earth. The trick is that you don’t know where it is, how high it is, or whether you are in a place that’s already pretty high or right at sea level.
Testing one variable at a time is like going out your donor with a good set of binoculars. You climb to the highest point you can see, then look again and go to the highest point again. You repeat until you are at the top point you can possibly see.
It’s a good iterative approach that will get you higher than you were before (or verify you were high already). But if you started in Indiana, your best hope would be to get to the top of Hoosier Hill (seriously), a whopping 1257 feet high. This is a local maximum. You are “optimized,” in that you can’t get higher by doing anything nearby.
What you really want is a global maximum, the best you can possibly do. But unless you are in the Himalayas already, local optimization will not get you there. The odds of you being in the Himalayas, of any possible land location on Earth, are vanishingly small.
So if you want to get to Everest, local optimization – changing one variable at a time – won’t do it.
To optimize globally, you must try some different (sometimes very different) things. It would start with a large-scale hypothesis about your donors (e.g., instead of responding to this part of our mission, they will respond to a different part) and making a wholescale communication test in sufficient quantity to see if it works.
This is the equivalent of spinning the globe and putting your finger on a location. Here’s the trick: two-thirds of the Earth’s surface is covered in water. This type of global testing is risky. Even if you have good knowledge about your donors and could limit your search to just land masses, you could end up in Florida, where the highest point is 345 feet tall and where, according to Carl Hiaasen novels, you are in peril of wacky hijinks ensuing.
I digress. The point is that these large-scale risks are scary. Hoosier Hill isn’t looking too bad, especially with the budget they are looking for you to hit. But if you do end up in the Rockies, Alps, Himalayas, etc. with your big risks, then global optimization can help you go from foothills to serious peaks.
So there are some ways to mitigate your risks while still testing big ideas. One is to have a portfolio approach to your risk. Kevin Schulman recommends allocating 15% of your volume to large-scale testing and 10% to incremental improvements here. This allows you to progress up the hill you are on while you scout the terrain for other worlds to conquer.
Another significant route to the peaks is to have a clear idea of why your donors give. In earlier posts we’ve stressed the importance of learning about your donors through the onboarding process and the types of information you’d want to collect.
If you have information like the identities of your donors, their commitment levels, and what makes them committed to your organization, your risk in creating a communication that is significantly different from your usual is significantly decreased. In fact, it’s exactly the type of approach you should be taking.
Finally, there are ways to test multiple variables simultaneously before a donor sees a live communication. The Agitator Toolbox contains one such solution here.
Using a panel of your donors, advanced survey techniques, and some sophisticated statistical analysis, this tool can tell you what images, messages, and themes are worth testing in live media (and which aren’t). No wonder one user described it as “18 months worth of testing in a day.”
So, to the idea of testing one variable at a time: it’s a fine idea as far as it goes. But to get to the best communication you can possibly have, you need to take broader, higher leaps.
How is your experience with broader testing?
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