B2B-Suche nach technischen Produkten

Die Vorteile

Überwinden Sie fachsprachliche Hürden mithilfe der umfassenden B2B-Suchanfragen von Bloomreach Discovery und entziffern Sie nicht eindeutige Fachterminologie in den Suchanfragen Ihrer Kund:innen.

Beispiel
  • Unterscheiden Sie mithilfe von Bloomreach Discovery zwischen Sachanfragen wie „Maschine Schrauben“ und „Schrauben Maschine“, indem Sie das Kundenverhalten mithilfe von maschinellem Lernen abbilden.

Der Mehrwert

Conversions und Kundenzufriedenheit mit relevanten Suchanfragen steigern.

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Video Transcript

Today, I will showcase how Bloomer Discovery
excels in understanding complex queries,
matching customer intent with product attributes
and merchandiser intent.
Let's start with semantic search. Boomer discovery
utilizes advanced techniques to comprehend
the intricacies of technical queries
for commerce, shoppers
by employing semantic search, we diagnose
and understand customer queries,
leveraging actually extraction and query understanding
to align customer intent with product
owner and merchandiser intent
to see this in action. I'm gonna perform
a search um somewhat technical
search for 15 amp 120
volt single pulse switch.
Uh Blumer discovery is able
to accurately retrieve the exact product. I'm
looking for a 15,
100 20 volt single pole switch.
This is achieved by matching the extracted
attributes from the customer's provided
query
uh
with the product information
extracted from the product data,
right? To showcase
this attribute extraction happening
on both sides of the equation here.
What I can do is I can show how that information
is extracted from the search term using
this tool or attribute attacker.
So I type in the term 15 amp 120
volt single volt switch and I run the tagger
and it shows exactly how we understand that
15 amps is a unit of measure
in ampage. 120 volt is
a unit of measure in voltage,
single pole is a type of switch
and switch is the product,
right? So what we're able to do is
actually collapse multiple terms together
to make sure that we're
showing the appropriate type of product.
And we're able to understand units of measure
to match up technical products
with the requirements of the customer's search
term, which is very important in technical
searching.
On the other side of that equation, like I said is
the product data. So we have the information
right from a
search term perspective. But we
see in the product data that these
uh measurements and things
are described differently,
they don't spell out
a amps, a bridge. It's 15
a 120 V, but we're
able to make those associations and
to do so I can show this tool,
our attribute tagger chrome extension that
shows that the product type is a switch. It's
15 amp in a unit of measure 100
and 20 volts in a unit of measure. And it marries
up those two because of the associations
that we have in our anto
to a conclusion. Bloomer's discovery stands
at the forefront of understanding really complex
queries for technical ecommerce searchers
with our semantic search attrite
extraction
and our ability to learn and leverage
several A I models. We're
not only able to return the right results,
but we're also able to rank them appropriately
to drive consistent,
uh, revenue for your business.

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