Verarbeiten Sie Kundensegmente auch in Spitzenzeiten ohne Verzögerung (z.B. an Black Friday)

Der Vorteil

Erstellen Sie auch in Spitzenzeiten rasch und gezielt Segmente auf Basis von Echtzeitdaten.

Beispiel
  • Filtern Sie für Black Friday oder Cyber Monday ohne Verzögerung ein Segment von Kund:innen mit einem bestimmten Lifetime Value, die in den vergangenen 30 Tagen etwas gekauft haben.

Der Mehrwert

Rasche Einblicke im großem Stil.

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Video Transcript

In this video, I'll be walking through how you can use Bloomreach to process custom segments in real time
to power your personalized and up to date marketing
efforts.
Data and Bloomreach engagement is stored in
a no sequel schema list database structure.
So it's not a relational database. And
that means that all data that's ingested into
the platform from all of these different data sources
is accessible in real real time.
The platform is designed with the goal of speed
of access and use in mind along with
the end user experience as a priority.
So let's dive into the segment builder here
and see what this looks like. Now, I want
to build a segment of customers based on
customer lifetime value and purchase
recency. Let's start with customers
with high lifetime value and recent
purchases.
So I'll start by pulling in lifetime
value. I'll
set this to greater than
100.
I'll add one more filter condition here based
on purchase
and I want this to be from the last
six months.
And you can see I have that and logic here.
Now I can simply clone this
to build out the medium and the low categories.
But I'm just gonna go ahead and skip that for
now for the sake of time. And you can see
I already have been built out here. So I have
medium customer lifetime value is anything
greater than 50 and they've purchased
in the last year, but not the last six months.
Similarly, similarly, here
with low is anything,
you know, above one lower than 50
and then they purchased in the last two years, but
not the last 12 months.
You'll notice when I hit preview,
it populates instantly with no rendering time
right down here and our segmentation
update in real time as customers meet
or no longer meet the criteria. And this is
crucial especially during peak periods like
Black Friday, when we want to communicate with
our customers in a timely manner with the most up to
date information.
Let's say I want to send some of these customers to Facebook
for retargeting. I can simply click
into the chart
and click send campaign and
it will open up our scenario builder here.
Now I'll go ahead and drag in the
add audience module.
Now, I wanna send the customers with low lifetime
value and no recent purchases to Facebook
to try and reengage them from that channel.
So I'll just go ahead and link this here.
You can see the condition module is already set
up with that criteria.
So I'll say low
and no recent purchase.
On the other hand, I want to suppress my highly engaged
customers from all ad audiences since
they'll require, you know, less engaging.
And this is how that would look.
So I'll go ahead and clone this.
I'll change this from low,
too high.
You can do this down the no branch
and I'll go ahead and clone this here.
Now, within the modules themselves
here, I'll just come in click Facebook add integration,
have them added to an audience.
I'll just say value based customer
lifetime value audience and I'll base
this off of lifetime value.
And then for this one, I want to remove them from all audiences.
So again, I'll connect this integration here,
but I'm just gonna say remove from all
audiences.
So this automation
of audience cleansing will optimize
my marketing team's ad spend overall and then just
give the customers a more tailored experience.

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