- How to define if your loyalty program works properly and
check your current performance with a industry benchmark and
define if it's working correctly or not.
(soft intro music)
Hi, is this Andrew.
The founder of flowium email marketing agency,
as well as this YouTube channel in today's video,
I would like to walk you through three benchmarks
you need to check and define if your loyalty program works
correctly.
We receive a lot of questions after we wrote this article,
showing the case study for one of our client,
defining the value defining their retention,
how much it costs, having a loyalty member versus
not a loyalty member and so forth.
I will show you step-by-step how to do it and define that if
it's working for you or not your current system.
Before we continue with the video,
I have a short announcement to make.
We are growing and we have a few spots available for new
clients, and I would like to tell you about our
partner program, give thousand dollars,
get thousand dollars.
How does this work? If you know anybody in your network
who are e-commerce brand online retailers,
please refer them to us.
And if they sign up with us,
you will get thousand dollars and they will get thousand
dollar discount or their credit,
or if you're an e-commerce brand and you would like to work
with us, we will give you $2,000 doors towards
your first month of the retainer,
just visit flowium.com/partner and see that details a
partner program.
And we have a template there, email templates,
which you can send it to your friend or somebody, you know,
in e-commerce space.
So here's the list.
So first thing we'll do is participation rate, participation
rate is a number of program members,
loyalty members, divided by your number of total customers.
And so let's see what percentage it is for
this particular client.
Okay, so you'll look into your Klayiyo account,
then you need to go to lists and segments,
and now we need to build what segments.
So we click on create list and segment,
number of customers.
So let's do placed order at least once over all time.
And yeah, so let's do that.
So we'll get the number,
actually we'll put it in here,
the number is number of current smile users.
So you look into your smile IO account,
click on performance, click overview,
and total program members in this case,
it's 26,000; 26 and a half thousand.
So let's do 26,500. Okay.
And let's wait, because it will take time,
especially if you have a big database,
it might take you awhile to update, so.
But after we have this number,
we would be able to calculate, okay,
so we have a number, this one.
Okay. So let me just put it in there.
Okay. So let's do the math now, quickly,
2 2 3 0
the number of total customer user was just
1 6 500 divided
by 2 2 3. I just round it up.
So in this case is 11%. So what does it tell you?
Like, based on this information, you know,
that you're way behind and you need to increase the
participation rate and there's a bunch of things you can do
to do that.
We posted a few videos on YouTube about
email marketing or our loyalty program.
Feel free to check it out.
You can just type in loyalty and flowium and
you'll get a list of all videos that we have. Okay.
Number two, redemption rate.
This is very easy to check you just go to points and
here you will see this redemption rate
in this case is a 83%, which is way, way, way,
way above that benchmark 33%.
So this is very, very good.
If you have lower benchmark,
let's say you had 20% redemption rate.
So there's something wrong with your offer.
Maybe you are asking them too much to do,
or maybe something's not clear.
So the last thing is to do is for us to evaluate value of
your customer.
Like I did here members, non-members, and what is the value.
I'll show you what kind of segments to build and how to pull
the data so you can build this kind of table.
Okay, so I build the table just to simplify your life,
not a loyalty member, loyalty members,
and all those stats we need to get.
It's very important for you to define the timeline.
Let's say you start using loyalty program
back in 2020 in June.
So then theoretically you either you want the full month.
So start calculating from July 1st, for example,
until now is June 1st until June 1st, this year.
So you have a like full few months of data.
So let's go to client and let's create few segments.
So first segment we want to do is one ex-buyer
was due Jan - u -ary
non-loyalty mem - bers.
Okay, so let's do placed, at least,
so, order placed
at least once between no,
between dates and let's do since starting this year.
0 3, sorry.
0 1 0 1 20 21.
And let's do here just January, June, sorry,
June 1st and curve, correct?
Yes.
And second segment we want to build is.
The second segment we want to build non-loyalty members.
Oh, I forgot to add something, loyalty members and
team members placed order at least once between dates.
So, 0 1 0 1 20 21.
And here just peer and property, loyalty member,
so is this one member, smile state equals member.
So this is member.
And here we need to add one more thing.
This and property, smile state equals
doesn't equal member because we want to exclude all members,
correct? Correct.
Okay. So we have the first data, which is loyalty members,
which is loyalty member is 8,000 4, 9, 1, and wait,
still waiting for non loyalty members. Okay.
So we have another data, which is 23,000.
Okay.
So we have that. Now we have to figure out this value.
So that's why klayiyo is so beautiful because you can export
and average out all those numbers.
So let's do it first for loyalty members,
just click manage acts-per-segment to CSV,
and we need few data. So we need value.
So total customer lifetime value,
and we need historic number of orders.
And we need average days between the order
and we need churn risk. Okay, cool.
Start export. You will download the CSV file,
open it and write out like we will average each column in
a second, so we did for loyalty member.
Let's do for a non loyalty member while we're doing it.
So export CSC, same thing, total customer lifetime value,
then historic number of orders,
then average rich orders,
and then churn risk prediction,
start export.
While we waiting, let's open this,
let's do the customer first.
So we select the whole column,
and on the bottom you have a sum average.
So average is hundred.
This is loyalty. I believe $160.61.
I will run the top. Then historic number of orders is what,
15, 16,
16.
Then we do average, days,
91, wow that's a lot.
91 and churn rate is average 50%.
So 52%
Okay, cool.
So we did it for loyalty members.
Let's check, actually, hold on, I'm very curious
to see how does non-loyalty members perform?
Okay, so I just opened another list and
this is something interesting.
So actually the total customer value is higher for
non-loyalty member.
And this is just pure data.
That's why we need to understand. And why is this?
So total customer lifetime value is higher.
However are our historic number of order is two?
So yes, maybe total customer lifetime value is higher,
but historic number of orders is like eight times smaller,
which is crazy.
Okay, churn production is 65%.
So the less likely to buy again from you.
By what, by 14% correct?
And by 13% and this one is 200. Yeah.
So of the four.
So basically, I mean it's a lot.
Yeah. So this is how to calculate this data.
And per article, we, when we posted this article,
a smile IO reached out to us and say like:
Hey, could you please also add this stats?
So on average, if people are fully leveraging their
loyalty program, implementing everything, like adding
the explanation pages, adding a loyalty program
to email marketing and so forth.
Repurchase rate is two and a half times higher purchase
frequency is 32% higher.
Yeah. So he's correct here.
Average order value is time higher when we did not include
average order value here, but you can do just one,
your exports from Klayiyo but just make sure
to check the average order value.
In case you have any additional questions,
please leave them under this video.
And also please check the other video about email marketing
around the loyalty program,
because it will help you improve your stats,
your general stats, improve engagement,
improve your revenue and participation
in your loyalty program.
Thank you.
(exit music)