Personnaliser les recommandations en fonction de l’historique des achats et de navigation

Bloomreach can easily show personalized
recommendations based on purchasing
and browsing history,
including real-time events in the current
session.
As you can see here, www.bloomreach.comes
equipped with a handful of out
of the box recommendation templates.
The one we'll be focusing on in the session
is customer recent interactions.
So items currently viewed
or purchased.
And if I go into this recommendation model,
I can select a product catalog,
I can even decide to potentially
filter further in a criteria
of the catalog. So let's say,
for example, we want to create
the recommendation against all
of the jacket products in this catalog.
We can also do that.
But for this purpose, we're gonna keep it overall
to the entire catalog.
Now, the next step is understanding what
you want to map the customer interaction to
in this case,
potentially we want to look
at all the customers viewing
the items instead of purchasing the items
because that still shows customer
intent.
So we're gonna say view items and mapping
it to each individual product ID.
So you'll see the coverage which
is pretty good
over 95%.
And we are gonna set the lifetime learning window
so that it will be the most robust results.
Now we can go ahead and take this
recommendation, save it as customer
recent interactions.
And what this will allow us to do
is run it in any of
the marketing channels that you would want
to showcase.
So in this third tab, I'm gonna show
you a web layer template.
And for this example, we're gonna go into
a recommendation box.
What this allows you to do is select
that template. You just created customer recent
interactions
and we're gonna change the currency really quick.
So when you preview it,
it will pull in
in real time. Those last
five items that have recently
been viewed by each individual customer
and the related items in that catalog.
This can be in an email campaign,
this can be in an M MS text.
The kind of opportunities
are endless and hopefully it'll help
you never leave money on the table again
by personalizing this customer experience
in real time.
This will hopefully help drive an increase
in LTV and boost your sales.