Store-spezifisches Ranking

Die Vorteile

Nutzen Sie Segmentrelevanz, um jeden Store als Segment zu verwalten und Produkt-Rankings automatisch anzupassen. Die Ergebnisse werden automatisch auf Basis des Umfelds Ihrer Kund:innen personalisiert, wobei Faktoren wie Einkommen, Demografie und regionale Vorlieben einfließen. Die Ergebnisse erscheinen rascher als bei der Datenaggregierung für eine 1:1-Personalisierung.

Beispiel
  • Zeigen Sie Bio-Erdbeeren automatisch ganz oben auf Ihrer Seite, wenn ein Kunde nach „Erdbeeren“ sucht, der aus Berlin Zehlendorf kommt, eine Region, in der Bio-Erdbeeren häufig bevorzugt werden.

Der Mehrwert

Personalisierte Shopping Experiences für Segmente mit ähnlichen Eigenschaften.

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Video Transcript

Today, we'll be looking at a very common
search and category page use case.
How do we as a brand or retailer?
Create personalized experiences
based on a user's location.
It's very common that different locations
and different users will have different affinities,
maybe even have different disposable
incomes. And as such will make very different
decisions when they visit the category or search
page. Think
of these examples
in this particular one, we're looking at a grocery
retailer,
some areas may show a greater
affinity towards biodegradable
or bioproducts.
Other areas will just care about the cheapest
product possible.
Take a fashion example,
a jacket
search for someone in Portugal
will not likely be the same jackets as a person
in Denmark is looking for.
So how do we account for these different user behaviors
in different regions?
There are a number of ways in which to
give bloom reach customer information. This
can be done by a tracking pixel
or even done by a segment. If
one is already creating se segment information
in a customer database,
alternatively, Bloom, which also has the possibility
to create segments directly in our own back end,
whichever method is used as
soon as the customer hits the website room
which will understand which segment
or geography they are in.
Based on this information, boomers will start
tracking every single behavior. Customers
within that geography or segment are performing.
Which products are they engaging in a category level,
which products are they engaging at a search level?
And after time,
rumor will have enough information to
re rank every single category and
search page
for that specific geography.
This starts to create differentiated experiences
based on the exact customer behavior
bloom, which is learning from that particular
segment
or geography.
In this context, you can see
the customer is an odd way,
are preferring whole milk.
This is something we have learned from their behavior.
If we switch to a more affluent segment
in an area such as
you will soon notice
organic milk
is the first and most popular
series of products visible.
What we are doing here is not hiding
milk products from certain segments.
We are purely re ranking.
The product is visible at category or search level
to match the affinity
that this segment is showing us.
In this way. Bloomer can continuously
learn against your customer data and
continuously in real time re
rank both search and category pages
in order to match the needs
and desires of your particular
audience
blue, which has a number of ways in which we can personalize
geography being only one of them.
We're more than happy to talk about the other ways in which
bloom, which can personalize both category BP
and search page experiences with you.
Thank you.

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