Name: How to explore a larger integrated picture of consumers
Uploaded: 2022-02-15T16:12:24.039Z
Duration: 172.55
Description: Understanding users and consumers is not a simple task, especially with massive consumer data coming from numerous sources. In this segment, Christian Kurz comments on his experience trying to figure out how to build a holistic understanding of consumers.

How to explore a larger integrated picture of consumers.vtt

English

{{00:00:25}}
There's definitely benefits.

{{00:00:26}}
There's also downsides.

{{00:00:27}}
But the overall benefit is clearly to
get a larger integrated picture of who

{{00:00:33}}
the user, the consumer, is because it
is that combination of the what and the

{{00:00:37}}
why and the how, and bringing all of
that together just gives us a fuller

{{00:00:42}}
picture, a more well-rounded balanced
view of what is going on in between.

{{00:00:48}}
And I actually think claiming either
on either of the sides, claiming

{{00:00:52}}
to know everything, it's just
not realistic because you really

{{00:00:54}}
truly have to put it together.

{{00:00:56}}
Now that's not an easy feat to achieve,
but I think we all strive to do that.

{{00:01:01}}
And I think that over the next few
years we'll hopefully make some more

{{00:01:05}}
progress in managing to get those hooks
that cause those combinations together.

{{00:01:15}}
So there's qualitative work where
you put AI into open questions and

{{00:01:20}}
have some sort of analysis that
doesn't need to be done by a human.

{{00:01:24}}
Same thing, it's just the data volume,
the volume of data that's coming

{{00:01:28}}
out everywhere is just overwhelming.

{{00:01:30}}
And I think that's really where
AI can help us sort this out.

{{00:01:34}}
Now, I personally, today, this
might change in a few years

{{00:01:37}}
too, but today I wouldn't want
AI to just go and do something.

{{00:01:41}}
I would want it to be a tool for
humans to then use and to make

{{00:01:45}}
the life of the people easier.

{{00:01:48}}
So that basically pre-analysis.

{{00:01:51}}
It comes out with here's
what we think this means.

{{00:01:53}}
And then somebody needs to look at
it and say, does this make sense?

{{00:01:56}}
And if not, then tweak
it and figure it out.

{{00:01:58}}
So I think it is that pre-analysis
with that huge data volume

{{00:02:03}}
where it can be really useful.

{{00:02:13}}
The data science unicorn is somewhat
of a mythical creature, whose specialty

{{00:02:18}}
is science, technology and business.

{{00:02:20}}
A person with this unique combination
of skillset is really rare to find,

{{00:02:25}}
which explains why businesses are
now moving away from holding out

{{00:02:28}}
on finding this mythical candidate.

{{00:02:31}}
Businesses are recognising that the
benefits of hiring different people

{{00:02:35}}
who offer specialised complimentary
skill sets actually allows for

{{00:02:39}}
teams to have a more integrated
picture of consumer behavior.

But the overall benefit is clearly to
get a larger integrated picture of who

the user, the consumer, is because it
is that combination of the what and the

why and the how, and bringing all of
that together just gives us a fuller

picture, a more well-rounded balanced
view of what is going on in between.

And I actually think claiming either
on either of the sides, claiming

to know everything, it's just
not realistic because you really

Now that's not an easy feat to achieve,
but I think we all strive to do that.

And I think that over the next few
years we'll hopefully make some more

progress in managing to get those hooks
that cause those combinations together.

So there's qualitative work where
you put AI into open questions and

have some sort of analysis that
doesn't need to be done by a human.

Same thing, it's just the data volume,
the volume of data that's coming

And I think that's really where
AI can help us sort this out.

Now, I personally, today, this
might change in a few years

too, but today I wouldn't want
AI to just go and do something.

I would want it to be a tool for
humans to then use and to make

It comes out with here's
what we think this means.

And then somebody needs to look at
it and say, does this make sense?

And if not, then tweak
it and figure it out.

So I think it is that pre-analysis
with that huge data volume

The data science unicorn is somewhat
of a mythical creature, whose specialty

A person with this unique combination
of skillset is really rare to find,

which explains why businesses are
now moving away from holding out

Businesses are recognising that the
benefits of hiring different people

who offer specialised complimentary
skill sets actually allows for

teams to have a more integrated
picture of consumer behavior.

Best Practice

Understanding users and consumers is not a simple task, especially with massive consumer data coming from numerous sources. In this segment, Christian Kurz comments on his experience trying to figure out how to build a holistic understanding of consumers.

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Chrisitan Kurz.jfif

Christian Kurz

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