Hey guys, it's Dom here from Neighbourhood.
Are you looking at optimising your email strategy but don't know where to start?
Perhaps you're unsure of what changes you
can make that will have an impact on your emails.
Open or Click Rate this is where AB testing your email can come in handy
and today we're going to show you how it's as a heads up for this tool,
you'll need Marketing Hub in HubSpot at the professional or the enterprise level.
If you're keen on more practical support
inside HubSpot Neighbourhood offer a comprehensive, easy to follow course so
you're making the most out of what you've got.
Stay tuned for more info at the end of this video.
If you want to take your learning offline
though, we'll include a downloadable PDF in the description below.
So when it comes to setting up an email
with a B testing, obviously the best place to start is with your emails.
One thing to note with AB testing within
HubSpot is that it's limited to just emails that are sent to a list.
If you have an email that's in an automation, that stops it from being
able to be AB tested, what you can do if that is the case,
however, is build this test into the automation so you can split your
audience using the automation tools into two groups,
send them both a variation of the email and see which one does better.
But today we're going to focus on the tool
that's been built for emails that are sent to lists.
So right here I have an email that's been set up as an example.
If I had in here, let's assume that you
set up the email with all of the information that you want.
You've got all of the images and stuff,
and you've got yourself your default email.
If I come up here to the top left, you'll see the Create AB test button.
If I select that button, I can go through and set up two different
versions of that email to test a specific metric.
So let's say, for example,
I want to test something like the headline or the main image I'm using.
Perhaps it's the subject line any number of things.
What it's effectively going to do is clone
that email, give you two versions of it, and let you make a change to one of them.
Now it can be very tempting to make
a bunch of changes to your newly cloned test email.
I don't recommend that,
mainly because it makes it very difficult to know what works.
If you get two emails and you make a bunch of different changes to one,
and then that email does better than the first one, you don't know what of all
of your changes to lean into and what had that impact.
So better to make just one change, test it, run it through its thing,
and then on the next email you can test something else let's go through.
We'll create a name for this one.
So we're going to call this one now.
Actually, no, let's not call it put in the name.
The thing you're testing,
it's going to help down the track when you get the results.
It'll just help you keep in mind what the thing was that this test was about.
So instead let's say alt image down here, I can choose the distribution.
Now the way this works, it's a little bit confusing.
Is that what it does is it breaks up your send into two groups, the testing group,
which is the group that gets one of the two versions and then the winning
group, and that's the group that gets whatever won the test.
So it means that you're not devoting the whole list to the test.
It means you're devoting part of the list
to the test and the rest of them get the optimised version of the email.
If you want to use this email as a full test, then you can change that by dragging
this slide all the way up, removing the winning version and saying
this email is going to be nothing but test.
But let's assume, for example, you want to optimise and then test.
So let's put it at 50 50.
Now what it's saying is that of our full
audience, 25% get email a, 25% get email B and then the remainder get
whichever of those first two emails did better.
If I keep scrolling down the winning metric is how we know which ₩1.
So in this case it would be open rate.
You can also change it to be click rate or click through rate.
Keep in mind the thing you're testing.
If you're testing open rate,
then all you should really be changing is the subject line and the preview,
like the preview text, maybe the sender as well because anything
within the email they'll only see if they open it.
So you don't want to change something
in the email for an open rate test because there's no real point to it.
So in this case, because I said the image
will change the image in this example, I'll set it as click rate.
How long do you want the test duration to be?
The reason this is important is because we're effectively saying how long should
we wait between sending the test emails and the winning email?
4 hours is what it's sort of recommending
here as the amount of time to get some data back from the system.
So it gives basically everyone
who receives the email in that test period 4 hours to interact with it and then after
that the rest of them get sent no matter what.
So the longer you leave there, the more data you'll get.
But the longer the winning team will have to wait to receive the email.
So maybe let's bump it up a little bit.
Let's say 12 hours.
That sort of gives you a day gives you.
Let's make it 24 a full day between
the people who got the test email and the people who get the winning version
of the email fallback version if there's an inconclusive result.
So if the results are too close to call and it's negligible, which one do you want
the system to just pick as the default winner?
In this case, I'll just say A is the one
that will put the image we feel best about on.
And then B will be the one that we're not sure about.
And that will be our test.
And then we create our test.
Now you can see up here in the top left
what was our create test button has changed.
Now it's telling me that we're in a B test example email.
And then in brackets A here I can switch between the two of them.
Let me switch across to Altimage and you
can see that now we're on a different version of the email.
Down here we have a picture of these bags.
Let's replace that.
And this is going to be the test that we're running.
So we're going to replace this picture
of bags with let's just pick we have a different picture.
This picture here of a jar.
So now we have our two versions of our emails.
We have the version that has the bags.
We have the version that has the jar.
I can switch between them again if I want
to keep sort of going back and forth and working out how I want to lay it out.
So you can see here A, again with the bags, B with the jar.
If you wanted to change something like the headline or the preview text or
anything like that, you can do so if I go across into the settings,
you can see that I still have access to those testing options here.
So if you were testing open rates and you
did want to change the subject line, this is where you would go to do so.
Let's just put in an example subject line.
Example subject.
Let's do that on both, because I'm guessing if it wasn't in B,
it's probably not in A, and then you can hit Review and Sense.
Now, I haven't set up this to send to any sort of list.
This is in our test portal, so we don't have any real data in there.
But once you're done, then you're effectively good to go.
And what's going to happen is you're going to send it.
It's going to go out to that test group, see how they interact with it,
see how many people click it, wait 24 hours, and then at this time
tomorrow, send the winning version to the remainder of that list.
One thing to note when it does come
to sending this out is that there is a minimum number of people that a test
requires in order to be considered significant.
It will warn you if it doesn't think you're picking enough people.
So let's say, for example,
if I was to pick a list here of 99 people, it's going to warn you that for a 50 50
split, if it's going to send half as a test and half to everyone else that it
needs more people to be able to be significant.
So it's warning me here that tests
that aren't split 50 50 require at least 1000 recipients.
Otherwise it's just going to send one version of it.
You can change that if you don't split it 50 50.
If you commit fully to the test, you get a little bit more leeway.
But you do need to have enough people in your list to kind of justify it.
Otherwise the data you get back isn't enough in order to be confident that what
you're going to do is going to have a big enough impact.
So just keep that in mind.
Now that we've sent the email, I can come in here to our test results
and this is where the outcomes of our test will live.
Now, our email didn't get sent to a real
list so I don't have real data to run you through.
But what you can see here is a breakdown
and this is sort of the information that I will be giving you.
You'll be able to see the two versions of your email.
It'll tell you which got better results.
It will tell you the winning metric,
how long the test was for the distribution and breakdown and then below that you'll
be able to see some specific metrics for each thing.
So where people clicked,
how many people clicked, how many people opened, all of that sort of stuff.
You'll be able to make some informed decisions about future emails that you
want to send out based on the data you've received from this test.
And there you have it. Now you know how to AV test an email.
If you're wanting to make the most out
of HubSpot, Neighbourhood offers a step by step course covering Marketing,
Sales Service and CMS Hub in depth, ensuring you and your team are best
serving your customers while developing efficient internal processes.
We'll include a link to this below as well.
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