Versenden Sie relevante, auf frühere Bestellungen angepasste E-Mails zur optimalen Zeit

Die Vorteile

Sorgen Sie dafür, dass Ihre Kund:innen Ihre E-Mails dann erhalten, wenn die Wahrscheinlichkeit, dass sie die Nachricht öffnen oder anklicken am höchsten ist, und zeigen Sie ihnen Artikel, die zu den Produkten aus ihren früheren Bestellungen passen.

Beispiel
  • „Diese gemütliche Decke passt farblich perfekt zu der Couch, die du gekauft hast!“

Der Mehrwert

Conversions und Customer Lifetime Value (CLV) steigern.

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Video Transcript

A common goal for marketers is to automate the customer
journey while also keeping it hyper personalized
for each individual. So in this video, we'll
be walking through how to make sure each individual
customer receives your email campaign at
a time that they're most likely to click or open it
and they're shown product recommendations that
are likely to suit their needs. So if you
want to make sure that your subscribers receive emails at appropriate
times, bloom Rich Engagement offers an out
of the box
uh prediction template to calculate that optimal
send time
based on your customers past behavior. This feature
will predict the right hour for receiving an
email campaign for each of your subscribers
individually. And this can be optimized
based on either open or click, right? So
let's dive into what this looks like here.
I'll click into that template. It's gonna ask a
few quick questions.
So number one, how much data? So how far
back do we wanna look for email engagement?
I always like to go with three months since it's
fairly recent and up to date.
So number two, select the default if there's
not enough data to determine an optimal
sun time prediction. We will go with this default
value.
And then number three is just a quick
mapping question. So I'll say this
is gonna map to our campaign event
where the status
equals
clicked and
open.
All right. Now, once that is all set, I'll go
ahead and build out the recommendation
that we'll use in the email.
You'll also notice we have a set of recommendation
templates as well out of the box to give you a jumping
off point, I'm going to use
this one for customers who bought this
item also bought.
Now, this is gonna take a look at the entire
database of customers to figure out
what are common products that are purchased
amongst common sub segments of customers.
So I'll first select the product catalog here.
Number two. Again, it's just gonna be a quick mapping
question. So I'm gonna map that to purchase
item for specific product I DS
just defining what a purchase event is for
number three, the learning window.
Um I'm just gonna go back in all time for right
now for the blacklist option
here. This is saying I
don't wanna show products to a customer
if they've already purchased it or if they've already viewed
it or some other engagement criteria.
So for example, I would say purchase
item
product ready
in the last all time if the product
has a shelf life and it needs to be replaced
after a certain amount of time then I can set this
to be a relative time frame of, let's
say six
months. So once that six months is
up, the product will be reintroduced into
the recommendation algorithm.
Now, this last step here for customer preferences,
this is gonna take the power of the customer
data platform and the insights pulled from
the raw customer data and it's gonna
pair it with the product catalog data. So
when I enable this, I can say I
want a catalog
attribute here. So we'll say category level
three,
the category of the product must equal
the customer's category preference.
Right now, once I have all of this set up, I have the optimal
sun time prediction
configured as well as these recommendations.
The last step is to plug it into our scenario
or customer journey.
So I'll start this out here.
I'm gonna have this set up on a repeating cadence
here. I'll set it up to be
weekly on Mondays
that's good to go. And then I can
pull in the condition node here to
build out the audience from here.
I can actually set up my audience based
on customer actions or customer attributes.
This does include that prediction. So I
could pull that in here.
Or once I define the audience,
I can drag in this weight
module from down here
and open this up to say I want this to be based
on optimal send time prediction, whether
that's optimized for click the rate or open
rate. All right. And the
last step here is to pull in that email
and personalize it.
So I'm just gonna pick a template to
go off of.
I'll drag in dynamic content
configure this block to be
dark recommendations.
Select my recommendation model.
The one I just built out,
apply that
and head over to the test
preview to get a feel for
how this is gonna render differently for each
recipient.
All right. So by giving the customers
this hyper personalized experience, not only
does it decrease the amount of work and manual template
creation, the marketing team has to do, but
it will also increase the email engagement as
well as that customer lifetime value.

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