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Tips & Tricks

We’re fewer than six weeks from the election. That means, among other things, that optimal fundraising strategies become even more important than usual. Here at ActBlue, we’ve been running tests on a nearly daily basis on all kinds of Express Lane strategies.

Typically, we see the largest (statistically significant) improvements when optimizing factors related to the Express Lane askblock structure like amounts, number of links, and intervals between the links. For our own list, we find that, statistically speaking, the flashier aspects you see in some fundraising emails — emojis in subject lines, e.g. — do not do much (if anything) to improve donation outcomes. Here’s a tactic we recently tested, though, that’s a bit more on the fun side of things and definitely brought in a lot more money.

A little while ago, we started using our weekly recurring feature to great success. (By the way, if you haven’t tried this feature yet, shoot us an email at info [at] actblue [dot] com and we’ll turn it on for you.) After testing which amounts brought in the most money, we landed on this1:

We wanted to see if we could raise more money by asking for “$7 because there are 7 weeks until the election!” Gimmicky? Sure, but we had a hunch that it would perform well.2 Here’s what it looked like:

So what happened? The segment with the ‘7 for 7′ ask performed much better than the control; it brought in 87.6% more money, a statistically and practically significant improvement.3 Cool!

What’ll be interesting to me is to see when this tactic will lose its optimality. The key factor is that $7 (with gimmick) performed better than $10 (the control and previously optimal ask amount) despite it being a lower dollar amount. Though, at some point, a too-low number-of-weeks-to-election-dollar-ask-amount combination will negate the positive c.p. effect of the gimmick. Based on other testing we’ve done, my guess is that that will be at 4-weeks-$4. We’re doing follow-up testing on this “n weeks until the election!” tactic, so we’ll see!

If you decide to test something similar, send me an email and we can chat! Emails to info [at] actblue [dot] com with my name in the subject line will be directed to me.

P.S. Doing a lot of testing in the election run-up? Want a tool to help you manage your test groups? I wrote something in R for you! I’ll post something on the blog about it soon, but if you want it in the meantime, shoot me a note (emails to info [at] actblue [dot] com with my name in the subject line will be directed to me).

FOOTNOTES:

1 Actually, we built a model that predicts how a given Express user will respond to different types of donation requests based on previous donation information. Using those predicted values, we decide what type of donation ask they receive (of one-time, weekly recurring, monthly recurring) and for how much money they are asked. Math! The point: this is what we landed on for a certain subset of our list.

2 Of course, all else equal, it’s tough to distinguish whether any difference was due to the gimmick or because $7 is lower than $10. The theory would be that with a lower amount, more people would give, and even though the mean donation amount would likely be lower, the increase in number of donors would outweigh the decrease in mean donation size. This is definitely possible, but so is the opposite; it’s all about finding the optimal point.

In fact, we included a segment in the test which received an askblock starting with a lower amount and saw this dynamic in action, though the overall treatment effect was not statistically significantly different from the control. This lends support for interpreting the effect from the gimmick segment as the gimmick per se, but a detailed discussion is excluded from the body of the post for the sake of brevity. More rigorous follow-up testing on this “n weeks until the election!” tactic is already in the field— shoot us an email to chat!

3Pr(>|t|) < .01, controlling for other significant factors, including previous donation history.

This post is the fourth in our blog series on testing for digital organizers. Today I’ll be talking about implementing your A/B test. This post will be full of helpful, quick tips.

So, we’ve discussed some things you might want to test, and some other things you might not want to test. Then, we walked through a simple way to figure out the number of people you’ll need in each of your test groups, which number depends on the smallest difference you’d like to reliably detect.1 Now what?

Well, the short answer is “run the test”, but of course it’s never that simple. Your next specific steps depend on what you’re testing, as well as which platform you’re using to run the test. There are too many possibilities for me to go through each one, but I can provide a few quick tips that should apply to you regardless of your specific situation.

First, make sure you have a reliable method of tracking your variations’ performance (like reference codes or an A/B testing tool (here are instructions for using ours)), and make sure you actually implement that method. This may sound like a no-brainer, but we’ve seen plenty of people start what would otherwise be an excellently set-up test with nothing to measure the variations’ relative performance! Is there a joke here about the “results” of the test?

Groaners aside, pointing out that error isn’t at all to make fun of the people who have committed it. Rather, we’re all busy, and things can get hectic. Having this on your pre-send checklist2 will save you from the realization that a lot of time spent thinking up a test, creating the content, and so on ad nauseam was all for naught.

What’s an example? Well, say you’re testing email content for donations. And of course, you want to use the best online fundraising software in the whole wide world, so you’re using ActBlue. Well, we have a handy feature that allows you to generate reference codes to track donations. We have a full instruction guide for using reference codes on our tutorial, found here. If you’re testing two different versions of your email, you could attach the URL param3 refcode=variation_a to the links in your first email and refcode=variation_b to those in your second email. Then, when you go to https://actblue.com/pages/[YOUR_PAGE_NAME]/statistics, you can measure the performance of each email. The information will also appear in a .csv download of your contribution form donations.

We also allow a handy refcode2 URL param if you want to conveniently subdivide your tracking. Conceptually, it’s the exact same thing as refcode; it’s value lies in the fact that it’s an extra place to store information. Think of a backpack with an extra divider on the inside for sorting your stuff. This is the internet version of that. For example, we use this for tracking link placement in the email. The need for refcode2, however, indicates that your test might be a bit complicated (i.e., there are more than just two variations, so setup and evaluation of the test is a bit outside the scope of the tips in this testing series.) That’s no problem, but you might want to shoot us an email at digital [at] actblue [dot] com to have a chat about test setup and design.

My second tip is related to groups. Taking your list—or some subset of your list—and dividing it up into smaller, randomized groups is a step that you’ll likely do in your CRM or email tool. Unfortunately, I can’t provide detailed instructions for each one. Chances are, though, that your CRM has an instruction page on how to do this within their software.4 In any case, this step is critical: without at least randomizing before conducting your trial, you’re setting yourself up for failure.

Here’s an example of how to do it wrong: let’s say you’re testing two emails, and even though you’re not sure which one is better, you have a hunch that email B is better than email A. So, not wanting to lose out on money, you decide to assign 20,000 people with the highest previous donations to group B and 20,000 people with the lowest previous donations to group A. That way, you can conduct the test to find out which email is definitely better, but not have to lose too much money along the way, right? Well, that’d be great, but unfortunately it’s all wrong. Assigning your groups that way would all but ensure you draw false conclusions about your test–email B is all but certain to bring in more donations, but it’s because it was assigned high-propensity donors, not necessarily because it’s the better email. Make sure you’re at least randomizing (with a proper algorithm, q.v. footnote 4) before splitting your groups and implementing your test.

My third tip is short and sweet. After you do all of this legwork, how do you know that the right variations were sent to the right number of people? What if you’re working with eight groups instead of just two? Well, the answer is that you don’t really. But, that can (and should!) be remedied. Place your own email address in each of the test groups. This won’t significantly affect the results of the test, but it will allow you to be sure that the right variations were sent. “But, I only have one email address, how can I put myself in multiple test groups without the hassle of creating new emails?”, you ask. Use the old email-campaigner’s trick of adding a “+” to your email address if you have a Gmail-based address. For example, if your email address is janesmith@actblue.com, you can add janesmith+test_email_a@actblue.com to group A and janesmith+test_email_b@actblue.com to group B; they’ll both be delivered to your inbox, and you’ll be able to perfectly spot whether the variations were sent correctly.

My fourth and last tip of the day is the most important one of all. Remember going through the process of determining your required sample size? Well, we did that for a (lengthily explained) reason. Don’t deviate from that now. What the hell am I talking about? I’m talking about peaking at the results too early (viz., before you reach your necessary sample size.)

I get it. You spent a lot of time setting up a test for these awesome variations of, say, a contribution form, and even though you know you need to wait until 15,000 people land on the form to see results, you want to check what’s happening? Has either taken an early lead? etc., etc., etc.

You can check what’s happening along the way, but you should definitely not stop the test early because it looks like one variation is performing better.5 This is a really common mistake, but a deadly one. I can’t stress this enough. The more times you test two variations for significance (which we’ll talk about in a future post) before the required sample size is hit, the more likely you are to detect a false positive. In fact, you can pretty quickly render your test effectively useless. So, if you just have to see what’s going on, fine, but promise yourself and statisticians everywhere that you won’t act on what you see!

Ok, that’s it for today! Next we’ll talk about evaluating your results and even more importantly, learning from them!

FOOTNOTES:
1 as well as your tolerance for the probability of getting a false positive and false negative, though using standard values can take some of the difficulty of this decision making away

2 which, if you don’t have a pre-send checklist (we prefer old-fashioned paper, big check boxes, and sharpies!), you should make one ASAP

3 A way of passing messages from the URL back to the website which it can use to customize the display or data recorded.

4 Now, this is generally the most basic possible insurance for proper group setup, as most tools will do nothing more than randomize and divide. There are other steps that should be taken for running anything more complex than a simple A/B test, which steps tend to best be done with a statistical tool such as R. If you think something more complicated is in-line for your program, don’t hesitate to shoot us an email (digital [at] actblue [dot] com)– we’d love to work with you to see if something more complicated is in order, and if so, we’d be glad to help.

5 Saying “definitely” in a conversation about statistics is— if also delightfully ironic— a bit misleading. This is actually a really complicated topic with plenty of proffered solutions, which range from minor adjustments in your calculations to an entirely different philosophical approach to statistics (I mean, who knew, right?). Those are all great discussions to have, but for now, it’s probably best to just assume you shouldn’t repeatedly evaluate your test variations before you hit your required sample size. Ok? Cool.

If you looked really closely to the Express Lane emails of a number of groups and campaigns recently, you might have noticed a tiny but significant change. Rather than saying: “Because you’ve saved your payment information with ActBlue Express…” the emails now read: “If you’ve saved your payment information with ActBlue Express…”



Why? Well, it turns out that you can raise slightly more money by sending an Express Lane-structured email to your entire membership. Traditionally, list admins send two distinct emails; Express users see Express Lane links, while everyone else gets an email with “regular” links. With Express Lane to all, you can send the same Express Lane email to all of your users, saving you time and opening up the possibilities for groups with smaller lists.

There’s been a lot of testing done both by us and other committees on sending Express Lane emails to everyone. The general consensus is that Express Lane structure to non-Express users does perform slightly better than normal links. We’ve tested sending Express Lane links to non-Express users 4 different times. Consistently, we see more money (the net bump is around 6-7%), but these results aren’t statistically significant. Others are seeing similar gains.

While we’d love to see statistical significance, we think it’s still a great idea because there is a tremendous upside potential for both groups and campaigns that are already using Express Lane and those who have yet to try it out. It’s a time saver for smaller groups and also encourages your members to save their information with ActBlue and become an Express user.

Our recommendation is that groups and campaigns test this with their membership and confirm that they are getting similar results before making this a best practice. There is some reason to believe that we’re seeing a novelty effect, since the new link structure is unusual. We’ll test this again in the future to make sure that the results are still holding, and we urge others do the same.

This tactic works particularly well for groups with smaller lists. We’re confident enough in the testing to tell you that you’re likely to raise more money from sending Express Lane to your entire list, especially with the strong growth in the Express universe (994k users and counting!). However, pay attention to future posts, in case we do find that there is a novelty effect.

This post is the third in our blog series on testing for digital organizers. Today I’ll be talking a bit about what an A/B test is and explain how to determine the sample size (definition below) you’ll need to conduct one.

Hey, pop quiz! Is 15% greater than 14%?

My answer is “well, kind of.” To see what I mean, let’s look at an example.

Let’s say you have two elevators, and one person at a time enters each elevator for a ride. After 100 people ride each elevator, you find that 15 people sneezed in elevator 1, and 14 people sneezed in elevator 2.

Clearly, a higher percentage of people sneezed in elevator 1 than elevator 2, but can you conclude with any certainty that elevator 1 is more likely to induce sneezing in its passengers? Or, perhaps, was the difference simply due to random chance?

In this contrived example, you could make a pretty good case for random chance just with common sense, but the real world is ambiguous so decisions can be trickier. Fortunately, some basic statistical methods can help us make these judgments.

One specific type of test for determining differences in proportions1 is commonly called an A/B test. I’ll give a simple overview of the concepts involved and include a technical appendix for instructions on how to perform the procedures I discuss.

Let’s recall what we already said: we can perform a statistical test to help us detect a difference (or lack thereof) between the action rate in two samples. So, what’s involved?

I’ll skip over the nitty-gritty statistics of this, but it’s generally true that as the number of trials2 increases, it becomes easier to tell whether the difference (if there’s any difference at all) between the two variations’ proportions is likely to be real, or just due to random chance. Or, slightly more accurately, as the number of trials increases, smaller differences between the variations can be more reliably detected.

What I’m describing is actually something you’ve probably already heard about: sample size. For example, if we have two versions of language on our contribution form, how many people do we need to have land on each variation of the contribution form to reliably detect a difference (and, consequently, decide which version is statistically “better” to use going forward)? That number is the sample size.

To determine the number of people you’ll need, there are a few closely related concepts (which I explain in the appendix), but for now, we’ll keep it simple. The basic idea is that as the percent difference between variations you wish to reliably detect decreases, the sample size you’ll need increases. So, if you want to detect a relatively small (say, 5%) difference between two variations, you’ll need a larger sample size than if you wanted to be able to detect a 10% difference.

How do you know the percent difference you’d like to be able to detect? Well, a good rule of thumb to start with is that if it’s a really important change (like, say, changing the signup flow on your website), you’d want to be able to detect really small changes, whereas for something less important, you’d be satisfied with a somewhat larger change (and therefore less costly test).

Here’s what that looks like:

Sample Size Graph

Required sample size varies by the base action rate and percent difference you want to be able to reliably detect. Notice the trends: as either of those factors increases, holding all else equal, the sample size decreases.

For example, if you’re testing two versions of your contribution form language to see which has a higher conversion rate, your typical conversion rate is 20%, and you want to be able to detect a difference of around 5%, you’d need about 26k people in each group .

For instructions on how to find that number, see the appendix below. Once you have determined your required sample size, you’ll be ready to set up your groups and variations, run the test, and evaluate the results of your test. Each of those will be upcoming posts in this series. For now, feel free to email info [at] actblue [dot] com with any questions!

Footnotes:
1 Note that this should be taken strictly as “proportions”. Of course, there are many things to be interested in other than the percentage of people who did an action vs. didn’t (e.g., donated vs. didn’t donate), like values of actions (e.g., contribution amounts), but for now, we’ll stick to the former.
2I.e., the number of times something happens. For example, this could be the number of times someone reaches a contribution form.

Appendix:

Statistics is a big and sometimes complicated world, so I won’t explain this in too much detail. There are many classes and books that will dive into the specifics, but I want you to have a working knowledge of a few important concepts you’ll need to complete an accurate A/B test. I’m going to outline four closely related concepts necessary for determining your sample size, and walk through how to find this number. Even though I’m sticking to the basics, this section will be a bit on the technical side of things. Feel free to shoot an email our way with any questions; I’m more than happy to answer any and all.

Like I said, there are four closely related concepts when it comes to this type of statistical test: significance level, power, effect size, and sample size. I’ll talk about each of these in turn, and while I do, remember that our goal is to determine whether we can reject the assumption that the two versions are equal (or, in layman’s terms, figure out that there is a real statistical difference between the two versions).

Significance level can be thought of as the (hopefully small) likelihood of a false positive. Specifically, the probability that you falsely reject the assumption that the two versions are equal (i.e., claim that one version is actually better than the other, even if it’s not.) When you hear someone talk about a p-value, they’re referencing this concept. The most commonly used significance level is 0.05, which is akin to saying “there’s a 5% chance that I claim a real difference, but there’s actually not”.

Power is the the probability that you’ll avoid a false negative. Or said another way, the probability that if there’s a real difference there, you’ll detect it. The standard value to use for this is 0.8, meaning there is an 80% chance you’ll detect it; though there are really good reasons for adjusting this value. 0.8 is by no means always the best value to choose for power; it’s generally a good idea to change it if you know exactly why you’re doing what you’re doing. .08 will work for our purposes, though. Why not just pick a value of .9999, which is similar to saying “if there’s a real difference, there’s a 99.99% chance that I’ll detect it”? Well, that would be nice, but as you increase this value, the sample size required increases. And sample size is likely to be the limiting factor for an organization with a small (say, fewer -than-100k-member) list.

Effect Size. Of the two versions you’re testing against each other, typically you’d call one the ‘control’ and the other the ‘treatment’, so we’ll use those terms. Effect size is saying, what do you expect the proportion of actions (e.g., contributions) to be for the control, and what do you expect it to be for the treatment? The percent difference is the effect size. How this affects sample size is demonstrated in the above graph. But the whole point of running this test is that you don’t know what the two proportions will be in advance, so how can you pick those values? Well, actually, you estimate what your base action rate will be. For example, if your donation rate from an email is typically 5%, then you can use that as your base action rate. Then, for the second proportion, pick the smallest difference you’d like to be able to detect. Similarly to power, you might find yourself asking “well why wouldn’t I just pick the smallest possible difference?”. Again, the answer is that as you decrease the magnitude of the difference, the sample size you need will increase.

Finally, we have sample size, or the number of people we need to run the test on. If we have values for the above three things, we can figure out how big of a sample we need!

So how do we do that? Well, there are many ways to do it, but one of the easiest, best, and most accessible is R. It’s free, open-source, and has an excellent community for support (which really helps as you’re learning). Some might ask, “well that has a relatively high learning curve, doesn’t it? And, isn’t there some easier way to do this?” The answer to both of those questions is “maybe,” but I’ll give you everything you need in this blog post. There are also online calculators of varying quality that you can use, but R is really your best bet, no matter your tech level.

Doing this in R is actually pretty simple (and you’ll pick up another new skill!). After you download, install, and open R, enter the following command:

power.prop.test(p1 = 0.1, p2 = 0.105, sig.level = 0.05, power = 0.8)

and press enter. You’ll see a printout with a bunch of information, but you’re concerned with n. In this example, it’s about 58k. That number is the sample size for each group you’d need to detect, in this case, a 5% difference at a significance level of 0.05, a power of 0.8, and a base action rate of 10%. So, just to be certain we’re on the same page, a quick explanation:
p1: Your ‘base action rate’, or the value you’d expect for the rate you’re testing. If you’re donation rate is usually 8%, then p1 = 0.08
p2: Your base action rate plus the smallest percent difference you’d like to be able to detect. If you only care about noticing a 10% difference, and your ‘base action rate’ is 8%, then p2 = 0.088 (0.08 + (0.08 * 0.10))

Of course, your base action rate will likely be different, as will be the percent difference you’d like to be able to detect. So, substitute those values in, and you’re all set! Playing around with different values for these can help you gain a more intuitive sense of what happens to the required sample size as you alter certain factors.

In this post, part of our testing blog series, I’ll talk a bit about some things you might consider testing, and—probably even more importantly—some things you might not want to test. This is all the more relevant if you’re managing a smaller list (say, fewer than 100k active members). As we’ll discuss in future posts, it takes huge sample sizes to reliably detect relatively small differences in two testing segments, so you’ll want to reserve your testing for factors that are likely to have larger effects on your goals, like subject lines, for example.

But to begin, we should be on the same page regarding why we test. It’s pretty simple. We tend to be pretty bad at guessing what will happen, so it’s often better to let data inform our decision making. For instance, when sending an email, should you go with a negative subject line like “This Republican is the worst!”, or a positive one like “Sally Jane is a great Democrat!”?

This trivial example allows us to demonstrate an important testing concept. Testing is only a tool; it’s not the final judge, nor does it say anything about the appropriateness of your content. If “This Republican is the worst!” isn’t in congruence with your campaign/organization’s messaging and mission, then you shouldn’t test that subject line, let alone use it for an email to your entire list.

So, then, assuming the subject matter is in-line with your messaging and mission, what’s something you should test, even with a small list? Subject lines could be one, but there are other things that could have a big impact on your action rates. What comes to mind first and foremost is email content.

By this I mean writing two completely different emails, whether they’re about the same thing or completely different concepts. The varied factor could be anything from your topic and theory of change to your tone and word choice. Even ostensibly similar emails—let alone drastically different ones—can yield very large differences in results. We at ActBlue, for example, regularly test at least three different fundraising emails for every one that we end up sending to our full list.

For one of our most recent email blasts, we sent four different email drafts, a couple of which were quite similar. The results? The best-performing draft brought in over triple the number of donations as the worst-performing drafts! So, here’s a clear case in which performing a simple test can lead to much higher action rates, whether you’re looking for signatures on a petition or donations to your cause.

content_testing_11

It might seem that writing three or more email drafts for every send is a bit much for a resource-constrained organizer. If that’s the case, you should still be message testing periodically, say, once a month or so. The goal here is to ascertain the biggest button-pushers for your list members. A standard example is testing the performance of an email highlighting the negative characteristics of your opponent against the performance of an email highlighting the endorsement of your candidate by a local community leader. This is a less resource-intensive way to gauge the temperature of your list and see what resonates with your list members.

So if the content of an email is something that is definitely worth testing, what are some things that small campaigns shouldn’t test? Well, anything that you expect won’t result in a large percentage difference between your test segments. For example, you certainly could test four differently colored donate buttons, but you shouldn’t.

Chances are, you won’t see a significant advantage in one of them over the others. How do I know this? Well, I can’t claim 100% certainty (nor can any honest analyst), but whenever we at ActBlue or some of our larger partners have tested something very small like this, we’ve seen that result.

For example, we wanted to run an A/B test1 on our contribution form to find out whether we could increase the conversion rate by removing the header, “Employment Information” above two of the FEC-required fields. To see what that looked like (and for some more A/B test examples), check out this blog post. We knew that it would take us close to 150,000 page views to reliably detect the small percentage difference in the two segments of the test we required to make a permanent change to our contribution form. I’ll talk more about determining required sample size in a later post, but for now, the point is that it took a lot to get a little.2 If you manage a smaller list, that means sending dozens of emails for a relatively minor gain, and that’s not worth your time.

Of course, context matters a lot, and in this case, context is your email program and your members. So, the final word is that if you really, really want to know, you should indeed test something for yourself instead of taking someone else’s word for it. But you’re much better off focusing on testing more meaningful factors (like your messaging) that are likely to result in clear and large differences. For the small things, you can learn from the organizations that have the resources to test small nuances. If you subscribe to numerous email lists, you’ll get a good gauge of what community best practices are at a given time.

Testing one email draft against another tells you exactly one thing: which (if either) is better. It doesn’t, however, tell you some things that can be quite valuable: Do members of your list tend to prefer positive emails or scare-to-action emails? Do they tend to respond well to fun, edgy language or slightly more formal language?

One A/B test won’t provide much of an answer to questions like that, but repeatedly testing two different email styles—like short, punchy emails against longer, more descriptive emails—over time can help you understand the style of communication your list members prefer, and therefore help you write emails with better action rates.

As you go on and develop your testing program, examining other questions like how much money to ask for in a fundraising email, how to best segment your list, and so on becomes more important and makes more sense from a cost-benefit perspective, too.

But to start, remember: make sure what you’re testing fits in with your organization’s messaging, plan a test that has a plausible chance of realizing big gains, and, more than anything else, work on honing your messaging. You’ll need to start out with bigger questions—and, therefore, more general tests—about your list members and eventually narrow down to the specifics.

The next post in our series about testing will talk about some essential factors involved in setting up a test, like setting up your groups and determining your required sample size. Expect that one to be published next week, after Netroots Nation.

Footnotes:

1 “A/B test” is an informal term for statistically testing two variations of some singular factor against each other in order to determine which, if either, is better for your desired outcome.

2We have millions of people land on our contribution forms each month, so for us, there’s a huge payoff to testing minor details that result in small percentage-point gains. It’s thousands of tests like this one over the years that make our contribution forms so successful. But this is our context— running a testing program with a small list is a totally different game.

Recurring pledges are like gold. There’s a reason why they’re often called sustaining contributions. Building a base of recurring donors can have a huge impact on the sustainability of any organization, including campaigns.

And now we’re making it easier for you to raise more long-term recurring contributions. Introducing: infinite recurring!

You’ve got a choice: ask people for a recurring contribution for a defined number or months (old standard), or ask them for one with no expiration date (new!). You can also choose not to have a recurring option, but we don’t recommend it (I’ll explain later.)

Here’s how you do it: Go to the edit page of any contribution form. Scroll down till you see this:

recurring toggle

Click on it to expand. It’ll look like this:

recurring options expanded

Select your radio button and then scroll down and hit submit. Yep, that’s it.

ActBlue got it’s start helping candidates raise money for their campaigns, which are built in two year cycles, so we allowed folks to set up recurring contributions for up to 48 months. The assumption was that donors would feel more comfortable signing up for a recurring contribution that would be sure to end at some point. These days, more and more organizations, who are around cycle after cycle, are using ActBlue. Plus, the way people use credit cards has changed and we have a whole system to let you extend/edit/add a new card to your recurring contribution, complete with prompts from us. It doesn’t make a ton of sense to have time-limited recurring contributions anymore.

So we tested it. Would forms with an infinite recurring ask perform the same (or better) as forms with a set number of months? AND would you raise more money if you didn’t have a recurring ask on the form, but asked people with a pop-up recurring box after their contribution was submitted?

We’ve got some answers. Several committees have run tests, confirming that conversion rates on time-limited forms and infinite recurring forms are similar. So if you’re around longer than election day, go ahead and turn on infinite recurring.

Generally speaking, making a form shorter and giving people fewer options leads to higher conversion rates. So theoretically, taking the recurring option off of a form should lead to more donations. We have a pop-up recurring box that campaigns can turn on to try and persuade a one-time donor to make their donation recurring, and there seemed to be a reasonable chance that having no recurring ask on the form would raise more money.

Nope! Turns out that we got a statistical tie on conversion rates between having the recurring option on the form or off. Just having pop-up recurring turned on did not generate as many recurring contributions as having it both on the form and as a post-donation action.

There were slightly more contributions processed on forms without a recurring option, but not enough to generate a statistically significant result. And then add to that the lost revenue from having fewer recurring donations, you end up with a pretty clear take-way: leave the recurring option on the form. Sure, you can turn off the recurring option, but you’ll likely lose money. And nobody wants that.

That’s why recurring contributions have been on every ActBlue contribution form since the beginning. These days we run anywhere from 8-14% recurring, and over $11 million is pledged to thousands of campaigns and organizations.

There is one big question we haven’t answered yet: will you raise more money overall from an infinite recurring contribution than say one with a 48 month expiration date? We’re currently working on a long-term experiment to test exactly that.

The answer might seem self-apparent, but the truth is nobody really knows. Credit cards expire and people cancel their pledges. You never know for sure how much money you’ll raise from a recurring contribution, but if you pay attention to your long-term data, you’ll be able to figure out your pledge completion rate.

If you’re interesting in figuring out a recurring donor strategy, we’re more than happy to give you some (free) advice. Just drop us a line at info@actblue.com.

We on the left have done a great job cultivating a “test, test, test” ethos, and while testing can result in big gains, it takes time and resources that digital organizers often don’t have. And for those working with a smaller list (say, fewer than 100k members or so), the challenges are even greater.

Don’t be discouraged, though; anyone can run an effective testing program, you just need to be aware of your organization’s specific circumstances. For instance, if you have a small list, it’s important to know that there are actually a lot of things that you shouldn’t test (more on this to come in future posts).

To help you get on track toward developing a strong testing program, we’re going to publish a series of blog posts, each focused on a particular aspect of digital testing for small organizations. We’ll be talking about anything from tools and techniques to case studies and getting buy-in from your supervisor.

If there are any specific issues you’d like to see addressed in this series of testing blog posts, please reach out! An email to info@actblue.com with a subject line “ActBlue Blog: Testing” will be directed my way.

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