Steigern sie den AOV durch automatische Empfehlungen relevanter Produlte mit KI

One way to use Bloomreach engagement to increase
the customer's basket size. And ultimately,
their average order value is to automatically
suggest relevant products to those already
in a customer's cart. We want to catch them
while they're still in their session to keep them engaged
with content relevant to their specific interests
before we dive behind the scenes to show how this
type of use case would be set up. I'd like to show
you how this might look from an end user's perspective.
So we're on the website here and
I'm just gonna start browsing some items here.
I do like the shirt. I'll add that one to my
cart. I'm gonna head back to the home
page to browse some more.
I do like this scarf as well. So I'll take
a look at that, but you know, right now, I don't
know if I want to buy. So let me take a look at my cart, see
what I have here. I'll think about it. Ultimately,
I'm like, you know what? I don't know, let me go ahead and leave
the site once I do. So this pop-up
shows up with different recommendations saying,
hey, any second thoughts? Do you want to add any
of these other products to your cart based
on what you've, uh, viewed in the past or
what products you may be interested based on your past
views? So I go, you know what? Sure. Let me
add this scarf to my cart.
I do like it
and now I'm adding an
additional
item to my cart, increasing my basket size
and increasing my average order value, which I would
have not done if I hadn't been prompted by that pop-up.
All right. Now let's take a peek behind
the curtain to walk through how something like this
would be set up.
Now in the platform here,
we have a web tool
where I can create a new one and base it off
of any of these predefined templates, whether
it's a countdown bar or a countdown box.
We have different email and
phone collection options. We have an
N P S box. What we just saw
was the recommendation box. So we'll start
out with that one.
I can select the recommendation model
that I want to use here. We have a bunch
that we can build out from templates that are not
that you don't see right here. But for now, I'll
say more like this. So I wanna show product
recommendations based on products
that are similar to the ones that I've been purchasing
or viewing.
I'll change this to just three products.
I can change the currency if I'd like, I can
change any of the styles, I want
the trigger to be on exit and 10.
So when I go to leave the site, that's when it will pop
up,
I can change the position
of the pop up
and I can change any of these other styles
as well.
Now, I can also A B test this web
layer. If I want to change the styles,
change the wording, maybe change
the recommendation model that I'm using
and then I can come over to the settings tab here
to configure what's the conversion goal?
So what is going to define this
web lare as a success? Is it purchase,
is it a engagement with this
web lay or is it a uh custom
engagement?
I can set up a schedule, whether it's gonna be
stopped manually or at a specific date
and time I can control the page
or pages that it appears on.
I can control the target devices.
So is it just gonna be desktop, just mobile
or both?
I can determine the frequency in which it's displayed?
I can configure the consent and then down
here, I will define the audience.
So instead of all customers,
I only want customers who meet
certain criteria based on actions.
So I'll say all customers who
have not placed a purchase
in their lifetime.
Now, because Bloom Reach operates and collects data in
real time, the platform will be able to learn
about your visitors while they're still present and active
on your site. And that said you'll be
able to action on the most up to date information
on your customers to give them the best and most personalized
experience possible.