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Transcript

The Biggest and Boldest NRF Must-See Tech Preview | Ask An Expert · Omni Talk Retail

0:00
Foreign.
0:08
Hello
0:08
everyone.
0:09
Welcome
0:10
to
0:10
what
0:10
is
0:10
now
0:11
our
0:11
second
0:12
annual
0:12
NRF
0:13
Must
0:13
See
0:14
Tech
0:14
Preview.
0:15
Oh
0:15
my
0:15
God,
0:15
it's
0:16
2026.
0:17
Ann
0:18
and
0:18
I
0:18
have
0:18
selected
0:19
our
0:19
top
0:19
must
0:20
see
0:20
technology
0:21
companies
0:21
that
0:22
you
0:22
have
0:22
to
0:22
have
0:22
on
0:22
your
0:23
list.
0:23
Heading
0:23
into
0:24
NRF
0:24
in
0:25
just
0:25
a
0:25
few
0:25
days,
0:26
we've
0:26
got
0:26
companies
0:26
bringing
0:27
you
0:27
everything
0:27
from
0:28
AI
0:28
agents
0:28
to,
0:29
to
0:29
cross
0:29
border
0:30
commerce
0:30
to
0:30
in
0:31
store
0:31
technology
0:32
which
0:33
you'll
0:33
see
0:33
across
0:33
this
0:34
stellar
0:34
lineup.
0:35
That's
0:35
right,
0:36
Chris.
0:36
I
0:36
cannot
0:37
wait
0:37
to
0:37
get
0:37
started.
0:38
And
0:38
just
0:38
a
0:38
quick
0:39
reminder
0:39
for
0:39
all
0:39
of
0:40
those
0:40
of
0:41
you
0:41
who
0:41
are
0:41
at
0:42
home
0:42
listening
0:43
in,
0:43
watching
0:44
live
0:44
right
0:44
now,
0:45
you
0:46
can
0:46
put
0:46
your
0:47
questions
0:47
for
0:47
each
0:48
of
0:48
our
0:48
tech
0:48
companies
0:49
right
0:49
in
0:49
the
0:50
text
0:50
chat
0:51
area
0:51
to
0:51
the
0:51
right
0:52
of
0:52
your
0:52
screen
0:52
if
0:52
you're
0:53
on
0:53
desktop.
0:54
And
0:54
we
0:54
will
0:55
make
0:55
sure
0:55
to
0:56
get
0:56
to
0:56
as
0:57
many
0:57
of
0:57
those
0:57
as
0:57
we
0:58
can
0:58
can
0:58
during
0:59
this
0:59
session.
1:00
All
1:01
right
1:01
Chris,
1:01
who's
1:02
first
1:02
up
1:03
in
1:03
the
1:03
lineup?
1:03
Well,
1:04
and
1:04
he's
1:05
no
1:05
stranger
1:05
to
1:06
omnitok,
1:06
I
1:06
can
1:06
tell
1:07
you
1:07
that
1:07
much.
1:08
From
1:08
his
1:08
early
1:08
days
1:09
creating
1:09
AI
1:09
and
1:10
computer
1:10
vision
1:10
store
1:11
solutions
1:11
at
1:11
Trigo
1:12
to
1:12
now
1:13
where
1:13
his
1:13
agent
1:14
employees,
1:15
agent
1:15
employees
1:16
are
1:16
helping
1:16
retailers
1:17
with
1:17
their
1:17
negotiations.
1:18
Please
1:18
welcome
1:19
GAIN
1:19
CEO
1:20
Michael
1:20
Govai.
1:21
All
1:22
right
1:22
Michael,
1:22
let's
1:23
get
1:23
right
1:23
into
1:23
it
1:24
for
1:24
those
1:24
meeting
1:24
you
1:25
for
1:25
the
1:25
first
1:25
time,
1:25
tell
1:26
us
1:26
about
1:26
your
1:26
background
1:27
and,
1:27
and
1:27
Gain
1:28
and
1:28
how
1:28
it
1:28
all
1:29
came
1:29
to
1:29
be.
1:29
Yes,
1:30
I
1:30
was
1:30
the,
1:30
as
1:30
you
1:31
know,
1:31
was
1:31
the
1:31
CEO
1:31
of
1:32
Trigo,
1:32
the
1:33
company
1:33
that
1:33
developed
1:33
Autonomy
1:34
stores
1:34
and
1:34
test
1:35
prevention
1:35
tool
1:36
for
1:36
very
1:37
large
1:37
grocery
1:37
retailers.
1:38
And
1:38
after
1:39
finishing
1:39
my
1:39
role
1:40
at
1:40
Trigo,
1:41
I
1:41
started
1:42
to
1:42
talk
1:42
with
1:42
many
1:43
of
1:43
my,
1:43
you
1:44
know,
1:44
past
1:44
customers
1:45
and
1:45
some
1:45
current
1:46
customers
1:47
from
1:48
the
1:48
retail
1:48
space
1:48
and
1:49
CPG
1:50
companies
1:50
as
1:51
well.
1:51
And
1:51
to
1:51
hear
1:51
the
1:52
need
1:52
and,
1:52
and
1:53
I
1:53
will
1:53
pretty
1:54
amazed
1:54
of
1:54
the
1:54
fact
1:55
that
1:56
all
1:56
of
1:56
them
1:57
are
1:57
not
1:57
negotiating
1:58
with
1:58
most
1:58
of
1:58
their
1:58
suppliers.
1:59
So
1:59
they
1:59
have
2:00
tools
2:00
for
2:00
to
2:01
say
2:01
what
2:01
should
2:02
be
2:02
the
2:02
right
2:02
price
2:03
for
2:03
their
2:03
suppliers.
2:04
If
2:04
you're
2:04
a
2:04
retailer,
2:05
you're
2:05
a
2:05
CPG
2:06
companies
2:06
or
2:06
if
2:06
you're
2:06
a
2:06
cpg,
2:07
your
2:07
suppliers.
2:09
But
2:09
they
2:09
didn't
2:09
have
2:10
the
2:10
ability
2:10
to
2:10
actually
2:10
negotiate
2:11
with
2:11
most
2:12
of
2:12
them.
2:13
And
2:13
that
2:13
was
2:14
actually
2:14
in
2:14
part
2:15
it
2:15
was
2:15
a
2:15
bit
2:16
around
2:16
year
2:17
and
2:17
a
2:17
half
2:17
ago
2:17
in
2:18
par
2:18
two
2:18
where
2:19
the
2:19
agent
2:19
capability
2:20
just
2:20
arrived
2:21
to
2:21
the
2:21
world
2:22
so
2:22
that
2:22
AI
2:23
can
2:23
make
2:23
decisions
2:24
and
2:24
also
2:24
act.
2:26
So
2:26
then
2:26
we
2:26
thought
2:27
of
2:27
the
2:27
ability
2:27
of
2:28
AI
2:28
employees
2:28
to
2:29
actually
2:29
execute
2:30
for
2:30
them
2:31
and
2:31
to
2:31
start
2:31
to
2:31
negotiate
2:33
for
2:33
the
2:33
company.
2:33
So
2:34
that's
2:34
how
2:34
it
2:34
all
2:35
started.
2:36
And
2:36
yeah,
2:37
and
2:37
here
2:37
we
2:37
are.
2:38
Yeah,
2:38
I
2:38
mean
2:38
I
2:39
remember
2:39
Michael,
2:39
last
2:40
time
2:40
we
2:40
spoke
2:40
to
2:40
you,
2:41
you
2:41
really
2:42
introduced
2:42
us
2:43
to
2:43
the
2:44
first
2:44
kind
2:44
of
2:44
practical
2:45
use
2:45
case
2:45
that
2:46
I
2:46
had
2:46
heard
2:46
at
2:46
least
2:47
of
2:47
these
2:47
agentiq
2:48
employees
2:49
Especially
2:50
the
2:50
one
2:50
that
2:51
is
2:51
helping
2:51
with
2:51
the
2:51
negotiations
2:52
that
2:52
you
2:52
just
2:52
mentioned.
2:53
Natalie,
2:54
explain
2:55
for
2:55
the
2:55
audience
2:56
what
2:56
exactly
2:57
Natalie
2:57
does
2:58
and
2:58
how
2:59
the
2:59
capabilities
2:59
of
3:00
what
3:00
Natalie's
3:01
doing
3:01
or
3:01
started
3:02
out
3:02
doing
3:02
and
3:03
what
3:03
Natalie's
3:03
doing
3:04
now.
3:04
So
3:05
we
3:05
call
3:05
it
3:05
AI
3:06
employee,
3:06
not
3:06
just
3:06
agent,
3:07
because
3:08
any
3:09
AI
3:09
employee
3:09
could
3:09
be,
3:10
you
3:10
know,
3:10
hundreds
3:10
of
3:10
agents
3:11
inside.
3:12
Sure.
3:12
And
3:12
each
3:13
employee
3:13
can
3:14
talk.
3:14
I
3:14
mean,
3:14
each,
3:15
you
3:15
can
3:15
talk
3:15
to
3:15
them
3:16
freely
3:17
over
3:17
chat
3:17
and
3:18
they
3:18
can
3:18
actually
3:19
execute
3:19
for
3:20
you
3:20
actually
3:20
to
3:20
do
3:21
the
3:21
work
3:21
end
3:21
to
3:21
end,
3:22
not
3:22
just
3:22
something
3:23
specific.
3:25
And
3:26
also,
3:26
I
3:26
mean,
3:26
it's
3:26
done
3:27
very
3:27
fast.
3:27
I
3:27
mean,
3:27
things
3:28
that
3:28
for
3:28
us
3:28
can
3:28
take,
3:29
for
3:29
humans
3:29
could
3:30
take
3:30
a
3:30
week
3:30
or
3:30
two.
3:30
For
3:31
them,
3:31
it
3:31
will
3:31
take
3:31
one
3:31
minute.
3:32
So
3:33
Natalie,
3:34
our
3:34
first
3:35
employee,
3:35
she's
3:36
negotiating
3:37
for
3:38
our
3:38
customers
3:39
with
3:39
their
3:39
supplier.
3:40
So
3:41
the
3:41
thing
3:41
is
3:41
that
3:42
all
3:43
any
3:43
large
3:43
company
3:44
has
3:45
around
3:45
80%
3:46
or
3:46
even
3:46
more
3:47
of
3:47
their
3:47
suppliers
3:48
that
3:48
they
3:48
might
3:50
negotiate
3:50
with
3:50
them
3:51
once
3:51
a
3:51
year.
3:52
And
3:52
with
3:53
Natalie,
3:53
they
3:53
can
3:53
negotiate
3:54
all
3:54
year
3:54
long
3:54
and
3:55
enjoy
3:55
the
3:55
value
3:55
and
3:56
the
3:56
changes
3:57
in
3:57
the
3:57
market,
3:59
not
3:59
just
3:59
leave
4:00
the
4:00
money
4:01
on
4:01
the
4:01
table.
4:02
So
4:03
that
4:03
Natalie,
4:04
she's
4:04
actually
4:04
negotiating
4:05
like
4:05
human,
4:06
like
4:06
over
4:07
email.
4:08
She
4:09
negotiate
4:09
with
4:09
the
4:10
suppliers
4:10
and
4:10
bring
4:11
the
4:11
best
4:11
possible
4:12
deal
4:12
for
4:12
our
4:13
customers.
4:13
Michael,
4:14
explain.
4:14
Even
4:14
maybe
4:15
like,
4:15
take
4:15
me
4:15
through
4:15
a
4:16
scenario
4:16
with
4:16
Natalie
4:17
too.
4:17
Like,
4:18
I
4:18
mean,
4:18
how
4:18
once
4:19
this
4:19
is
4:19
set
4:19
up,
4:20
like
4:20
talk
4:21
me
4:21
through
4:21
like
4:22
a
4:22
negotiation
4:23
or
4:23
how
4:23
this
4:23
would
4:24
all
4:24
go
4:24
down.
4:25
So
4:25
Natalie
4:25
connected
4:26
to
4:26
external
4:27
data
4:27
sources
4:28
of
4:28
commodities
4:29
and
4:29
raw
4:30
materials
4:30
and
4:31
imagine
4:31
like
4:32
reduction
4:33
of
4:33
prices
4:34
in
4:34
the
4:34
market,
4:35
not
4:35
that
4:35
we
4:36
come
4:36
to
4:36
the
4:36
buyer
4:36
and
4:36
say,
4:37
hey,
4:37
I
4:37
see
4:37
reduction
4:38
of
4:38
prices
4:39
at
4:39
those
4:40
raw
4:41
materials.
4:41
And
4:42
it's,
4:42
it
4:43
could
4:43
be
4:43
now
4:43
an
4:43
opportunity
4:44
to
4:44
start
4:44
to
4:44
negotiate
4:45
with
4:45
our
4:45
supplier
4:46
and
4:46
enjoy
4:47
it
4:47
as
4:47
well.
4:48
So
4:48
she
4:49
will
4:49
start
4:49
to
4:49
talk
4:49
with
4:50
the
4:50
buyer
4:50
and
4:50
we'll
4:51
offer
4:51
a
4:51
strategy
4:52
for
4:52
negotiation
4:53
and
4:53
we'll
4:53
ask
4:54
about
4:54
can
4:54
I
4:54
start
4:54
the
4:55
negotiation?
4:56
And
4:56
if
4:56
the
4:56
buyer
4:57
will
4:57
say
4:57
yes,
4:57
and
4:58
it's
4:58
an
4:58
open
4:58
conversation
4:59
between
4:59
them,
5:00
she
5:00
will
5:00
actually
5:01
start
5:01
to
5:01
negotiate
5:02
with
5:02
the,
5:02
with
5:03
the
5:03
supplier
5:04
for,
5:04
for
5:04
them,
5:05
for
5:05
the
5:05
company.
5:05
Wow.
5:06
And
5:06
that's
5:07
so
5:07
crazy,
5:07
isn't
5:08
it,
5:08
Chris?
5:08
Like,
5:08
I
5:09
mean,
5:09
there's
5:09
no
5:10
employee
5:11
that's
5:11
going
5:11
to
5:11
be
5:12
able
5:12
to
5:12
be
5:12
like
5:12
keeping
5:13
track
5:13
of
5:13
all
5:13
this
5:14
data
5:14
and
5:14
then
5:14
shoot.
5:15
I
5:15
mean,
5:15
it's,
5:15
it's
5:16
just
5:16
unbelievable
5:17
to
5:17
me.
5:17
Yeah.
5:18
And
5:18
the
5:18
thing
5:18
I
5:18
like
5:18
about
5:19
it
5:19
too,
5:19
it
5:19
takes
5:19
the
5:19
partiality
5:20
out
5:20
of
5:20
the
5:20
negotiation
5:21
process
5:22
too,
5:22
which,
5:22
you
5:22
know,
5:23
as
5:23
you
5:23
know,
5:23
being
5:23
a
5:24
former
5:24
buyer,
5:24
there's
5:25
a
5:25
lot
5:25
of
5:25
like
5:26
glad
5:26
handing
5:26
that
5:26
goes
5:27
into
5:27
some
5:27
of
5:27
those
5:27
negotiations.
5:28
Like
5:28
who's
5:28
taking
5:29
me
5:29
out
5:29
to
5:29
dinner,
5:29
who's.
5:30
Who's
5:30
taking
5:31
me
5:31
golfing,
5:31
who's
5:32
taking
5:32
me
5:32
to
5:32
the
5:32
ball
5:32
game.
5:33
And
5:33
so
5:33
if
5:33
I'm
5:33
on
5:34
the
5:34
CPG
5:34
side
5:35
like
5:35
I
5:35
kind
5:36
of
5:36
like
5:36
this
5:36
because
5:36
it
5:37
gives
5:37
me
5:37
a
5:37
chance
5:37
to
5:38
particularly
5:38
negotiate
5:39
more
5:39
of
5:39
my
5:40
items
5:40
but
5:40
to
5:40
do
5:40
it
5:40
in
5:40
a
5:41
very
5:41
impartial
5:41
way.
5:42
So.
5:42
So
5:42
Michael,
5:43
how
5:43
does
5:43
it
5:43
work
5:43
from
5:43
the
5:43
CPG
5:44
side
5:44
too?
5:45
Like
5:45
how
5:45
would
5:45
a
5:45
CPG
5:46
deploy?
5:46
Natalie?
5:47
For
5:47
example,
5:47
one
5:48
side
5:48
of
5:48
it,
5:48
the
5:48
CPG
5:49
is
5:49
the
5:49
supplier
5:49
of
5:49
the
5:50
retailer
5:51
and
5:51
small
5:51
cpg,
5:52
I
5:52
mean
5:53
they
5:53
love
5:53
it
5:53
because
5:53
now
5:54
they
5:54
will
5:54
have.
5:55
It's
5:55
actually
5:55
a
5:55
communication
5:56
tool
5:56
between
5:57
them
5:57
to
5:57
the
5:57
large
5:58
company,
5:58
the
5:58
large
5:59
retailer.
6:00
But
6:00
suddenly
6:00
start
6:01
to
6:01
speak
6:01
with
6:01
them.
6:02
They
6:02
can
6:02
also
6:02
enjoy
6:03
from
6:03
it.
6:03
I
6:03
mean
6:03
reduction
6:04
of
6:04
prices
6:04
means
6:05
that
6:05
they
6:05
might
6:05
start
6:06
to
6:06
buy
6:06
much
6:06
more
6:07
for
6:07
them.
6:07
So
6:08
it's.
6:08
And
6:08
today
6:09
nobody
6:09
will
6:09
actually
6:09
pick
6:10
up
6:10
the
6:10
phone
6:10
for
6:10
most
6:11
of
6:11
the
6:11
small
6:12
suppliers.
6:13
And
6:13
when
6:14
it
6:14
comes
6:14
to
6:14
the
6:14
large
6:14
cpg
6:15
actually
6:15
the
6:16
same
6:16
thing,
6:16
the
6:16
same
6:17
kind
6:17
of
6:17
negotiation
6:18
between
6:18
the
6:18
retailer
6:19
to
6:19
the
6:19
CPG
6:20
company
6:20
is
6:20
the
6:21
negotiation
6:21
between
6:21
this
6:22
large
6:22
CPG
6:23
company
6:24
to
6:24
their
6:24
suppliers.
6:25
So
6:25
it's
6:26
the
6:26
same
6:26
process
6:27
pretty
6:28
the
6:28
same
6:29
process
6:29
between
6:30
the
6:30
large
6:30
TPG
6:31
to
6:31
their
6:31
suppliers.
6:32
And
6:32
they
6:32
are
6:32
starting
6:32
to
6:32
pick
6:33
up.
6:33
And
6:33
we're
6:34
already
6:34
working
6:34
with
6:34
some
6:35
of
6:35
the
6:35
largest
6:35
CPG
6:35
company
6:36
in
6:36
the
6:36
world
6:36
that
6:37
started
6:37
to
6:37
enjoy
6:38
Natalie
6:39
and
6:39
also
6:39
by
6:39
the
6:40
way
6:40
our
6:40
secondary
6:40
employee
6:41
Bob
6:42
that
6:42
rely
6:42
on
6:43
replenishments
6:44
for
6:44
their
6:44
warehouses
6:45
for
6:45
the
6:46
factory
6:46
and
6:47
for
6:47
the
6:47
retailers.
6:48
It
6:48
will
6:48
rely
6:49
on
6:49
the
6:50
replenishment
6:50
for
6:50
the
6:51
inventory
6:51
for
6:51
the
6:51
store.
6:52
So
6:53
not
6:53
to
6:54
negotiate
6:54
for
6:54
the
6:54
deals.
6:55
And
6:55
Bob,
6:55
actually
6:56
send
6:56
the
6:56
PO
6:57
and
6:57
complete
6:58
the,
6:59
the,
6:59
you
7:00
know,
7:00
the
7:00
complete
7:01
the
7:01
entire
7:02
action
7:02
for
7:02
the,
7:04
for
7:04
the
7:04
company.
7:05
Yeah.
7:05
Okay,
7:06
so
7:06
I
7:06
love
7:07
that
7:07
we're
7:07
leading
7:07
with
7:08
this
7:08
because
7:09
I
7:09
mean
7:09
recently
7:09
I've,
7:10
you
7:10
know,
7:10
I've
7:10
been
7:10
putting
7:10
a
7:11
lot
7:11
out
7:11
particularly
7:12
around
7:12
where
7:13
agentic
7:13
AI
7:14
needs
7:14
to
7:14
be
7:15
the
7:15
focus
7:16
of
7:16
retailers
7:16
first
7:17
and
7:17
foremost.
7:17
Like
7:17
should
7:17
it
7:18
be
7:18
on
7:18
the
7:18
consumer
7:18
facing
7:19
side
7:19
or
7:19
should
7:19
it
7:19
be
7:20
on
7:20
the
7:20
internal
7:20
operations?
7:21
And
7:21
what
7:21
you're
7:21
saying
7:21
here
7:22
is
7:22
that
7:22
Natalie
7:22
and
7:22
Bob
7:23
can
7:23
both
7:24
help
7:24
improve
7:24
the
7:24
operations
7:25
of
7:25
a
7:25
company
7:26
and
7:26
that's
7:27
one
7:27
of
7:27
the
7:27
values
7:27
of
7:28
agentix.
7:28
So
7:28
I'm
7:29
curious,
7:29
like
7:30
what
7:30
is
7:30
the
7:30
current,
7:30
what
7:31
is
7:31
your
7:31
current
7:31
read
7:32
of
7:32
the
7:32
sentiment
7:33
of
7:33
retailers
7:33
and
7:34
CPG
7:35
towards
7:35
agents
7:36
like
7:36
Gaines?
7:37
Has
7:37
it
7:38
changed?
7:38
Have
7:38
you
7:38
seen
7:39
it
7:39
evolving?
7:40
How
7:40
would
7:40
you
7:40
sum
7:41
up
7:41
the
7:41
current
7:41
sentiment
7:42
across
7:42
the
7:42
industry
7:43
around
7:43
deploying
7:43
agents?
7:44
What
7:45
we
7:45
see
7:45
that
7:45
the
7:46
executives,
7:47
I
7:47
mean
7:47
the
7:47
management,
7:48
the
7:48
CEOs,
7:49
I
7:49
mean
7:49
pushing
7:49
very
7:50
hard
7:50
now
7:50
to,
7:51
to
7:51
add
7:51
more
7:52
and
7:52
more
7:52
AI.
7:53
We
7:53
do
7:53
see
7:53
some
7:54
fear
7:54
if
7:55
I
7:55
would
7:55
say
7:55
employees
7:56
level.
7:56
And
7:57
I
7:57
think
7:57
this
7:57
is
7:58
by
7:58
the
7:58
way
7:58
why
7:58
we
7:59
are,
7:59
why
7:59
we
7:59
Called
8:00
ourselves
8:00
gain
8:01
to
8:01
tell
8:02
people
8:02
I
8:02
mean
8:02
we
8:02
are
8:02
coming
8:02
to
8:03
gain,
8:03
we're
8:03
coming
8:03
to
8:03
help,
8:04
we're
8:04
not
8:04
coming
8:05
to
8:05
replace,
8:06
we
8:06
are
8:06
coming
8:07
to
8:07
help
8:07
you
8:07
execute
8:08
and
8:08
then
8:08
you
8:09
can,
8:10
I
8:10
mean
8:11
provide
8:11
more
8:11
impact
8:12
to
8:12
the,
8:13
to
8:13
the
8:13
companies.
8:14
But
8:14
we
8:14
do
8:14
see
8:14
a
8:14
lot
8:15
of
8:15
pressure,
8:15
a
8:15
lot
8:16
of
8:16
pressure
8:16
now
8:16
you
8:17
know,
8:17
at
8:17
the
8:17
beginning
8:18
of
8:18
the
8:18
year
8:18
to
8:18
start
8:18
with
8:19
a
8:19
lot
8:19
of
8:19
projects
8:20
and
8:20
I'm
8:21
happy
8:21
to
8:21
be,
8:21
to
8:22
be
8:22
at
8:22
that
8:22
side
8:22
of
8:23
you
8:23
know,
8:23
for
8:23
companies
8:24
that
8:24
now
8:24
need
8:24
to
8:25
deliver.
8:25
Chris
8:25
and
8:26
I
8:26
think
8:26
are
8:26
just
8:26
surprised
8:27
by
8:27
how
8:28
far
8:28
this
8:28
has
8:28
come
8:28
and
8:29
all
8:29
of
8:29
the
8:29
capabilities
8:29
that
8:30
it's
8:30
able
8:30
to
8:31
offer.
8:32
These,
8:32
these
8:32
buyers
8:33
who
8:34
from
8:34
our
8:34
experience
8:35
talking
8:35
to
8:35
retailers
8:36
buyers
8:36
are
8:37
some
8:37
of
8:37
the
8:37
most
8:37
time
8:38
constrained
8:39
under
8:40
pressure
8:40
teams
8:40
that
8:41
exist
8:41
inside
8:42
retailers
8:42
and
8:42
CPGs
8:43
right
8:43
now
8:43
too
8:45
are
8:45
what
8:46
are
8:46
the
8:46
areas
8:46
that
8:46
your,
8:47
your
8:47
CPG
8:48
and
8:48
retailer
8:48
clients
8:49
are
8:49
most
8:49
surprised
8:50
when
8:51
they
8:51
deploy
8:51
gain
8:52
like
8:52
what
8:52
are
8:52
some
8:52
of
8:53
the
8:53
things
8:53
that
8:53
they
8:53
hadn't
8:54
expected
8:55
and
8:55
that
8:55
some
8:55
of
8:55
the
8:56
benefits
8:56
that
8:56
they're
8:56
getting
8:56
once
8:57
this
8:57
is
8:57
up
8:57
and
8:57
running
8:58
for
8:58
them.
8:58
I
8:59
mean
8:59
people
8:59
always
9:00
amazed
9:00
your
9:00
first
9:01
time
9:01
that
9:01
it's
9:01
really
9:01
working.
9:02
I
9:02
mean
9:03
something
9:03
completely
9:04
new.
9:06
That's
9:06
actually
9:06
a
9:06
big
9:06
achievement.
9:07
Yeah,
9:07
I
9:08
mean
9:08
and
9:09
the
9:09
fact
9:09
that
9:09
Natalie
9:10
can
9:10
you
9:11
know
9:11
answer
9:12
very
9:12
sophisticated
9:13
answer
9:14
for
9:14
the
9:14
supplier
9:14
for
9:14
example
9:15
if
9:15
you're
9:15
on
9:16
the
9:16
retail
9:16
side
9:17
and
9:18
how
9:19
flexible
9:19
the
9:19
solution
9:20
is,
9:21
that's
9:21
I
9:22
think
9:22
the
9:22
first
9:22
thing
9:23
second
9:23
thing
9:23
is
9:24
that
9:24
sometimes
9:24
even
9:25
I
9:25
mean
9:25
simple
9:26
things
9:26
that
9:27
we
9:27
do
9:27
can
9:27
provide
9:28
huge
9:28
impact.
9:29
So
9:30
for
9:30
example,
9:30
you
9:30
know,
9:31
as
9:31
I
9:31
said
9:31
we
9:31
are
9:31
already
9:32
working
9:32
with
9:32
very
9:32
large
9:33
CPG
9:33
company
9:34
and
9:34
and
9:34
we
9:34
started
9:35
with
9:36
negotiate
9:37
for
9:37
the
9:38
repair
9:38
parts
9:38
for
9:38
the
9:39
factory
9:40
for
9:40
example
9:41
pipes.
9:42
And
9:42
it's
9:43
not
9:43
very
9:43
sophisticated,
9:44
it's
9:44
not
9:44
very
9:45
complex.
9:46
And
9:46
even
9:46
just
9:47
to
9:47
take
9:47
a
9:47
look
9:47
at
9:48
the
9:48
history
9:48
of
9:49
how
9:49
much
9:49
these
9:49
suppliers
9:50
told
9:50
you
9:50
two
9:51
years
9:51
ago
9:52
similar
9:52
pipe
9:53
it
9:53
was
9:54
much
9:54
more
9:56
cheaper.
9:56
We
9:56
can
9:57
just
9:57
take
9:57
the
9:57
data
9:58
from
9:58
the
9:58
past
9:58
and
9:59
come
9:59
back
9:59
to
9:59
the
9:59
supplier.
10:00
Hey,
10:00
three
10:00
years
10:00
ago
10:01
it
10:01
was
10:01
20%.
10:02
I
10:03
mean
10:03
lower
10:03
price.
10:04
Let's
10:04
talk
10:04
about
10:05
it.
10:05
And
10:06
a
10:06
minute
10:07
after
10:07
we
10:08
started
10:08
this
10:08
kind
10:08
of
10:08
negotiation
10:09
we
10:09
got
10:10
an
10:10
answer
10:10
for
10:10
the
10:10
supplier.
10:11
Hey,
10:11
we
10:11
give
10:12
you
10:12
now
10:12
the
10:12
20%
10:13
discount
10:14
of
10:14
the
10:14
pipes.
10:15
And
10:16
this
10:16
is
10:17
a
10:17
kind
10:17
of
10:17
thing
10:18
that
10:18
the
10:18
buyers
10:18
will
10:18
not
10:19
do
10:19
today
10:19
because
10:19
it's
10:20
just
10:20
a
10:21
small
10:21
deal,
10:21
it's
10:22
not
10:22
something
10:22
huge.
10:23
Nobody
10:23
will
10:23
start
10:23
to
10:24
negotiate
10:25
this
10:25
small
10:25
deal.
10:26
But
10:26
in
10:26
total
10:27
the
10:27
amount
10:27
of
10:28
deals
10:28
they
10:29
have
10:29
is
10:29
huge.
10:29
So
10:30
in
10:30
total
10:30
the
10:31
potential
10:31
is
10:32
endless.
10:32
But
10:33
sometimes
10:33
do
10:34
you
10:34
know
10:34
simple
10:34
tasks,
10:35
not
10:35
just
10:35
the
10:35
most
10:35
complicated.
10:36
And
10:36
we
10:36
also
10:37
of
10:37
course
10:37
providing
10:38
very
10:38
complicated
10:40
negotiations.
10:41
We
10:42
are
10:42
now
10:42
already
10:42
negotiating
10:43
for
10:43
retailers
10:45
for
10:45
deals
10:45
that
10:46
are
10:46
on
10:46
2
10:47
to
10:47
3
10:47
million
10:48
dollars
10:48
each
10:48
deal
10:49
as
10:49
well.
10:51
So
10:51
it's
10:51
also
10:52
relatively
10:52
large
10:53
deals,
10:53
not
10:53
just
10:53
small
10:54
things.
10:54
That
10:54
brings
10:54
up
10:55
another
10:55
interesting
10:55
point
10:55
to
10:56
me
10:56
Michael,
10:56
is
10:56
like,
10:57
you
10:57
know,
10:57
a
10:57
lot
10:57
of
10:57
these
10:57
people
10:58
change
10:58
their
10:58
jobs
10:59
very
10:59
frequently
10:59
too.
11:00
Like
11:00
the
11:00
typical
11:00
life
11:01
cycle
11:01
of
11:01
a
11:01
buyer
11:01
in
11:02
my
11:02
experience
11:02
is
11:02
two
11:02
to
11:02
three
11:03
years
11:03
in
11:03
position
11:03
at
11:04
most.
11:04
And
11:04
so
11:04
a
11:04
lot
11:04
of
11:05
that
11:05
knowledge
11:06
that
11:06
they
11:06
gain
11:06
gets
11:07
lost,
11:07
you
11:07
know,
11:08
throughout
11:08
the
11:08
years.
11:08
And
11:09
you
11:09
know,
11:09
an
11:09
agent
11:10
would
11:10
retain
11:11
that
11:11
knowledge
11:12
throughout
11:12
its
11:13
use,
11:13
usage
11:13
and
11:14
understand
11:14
the
11:14
history
11:15
of
11:15
the
11:15
products
11:15
they're
11:16
negotiating.
11:16
So.
11:16
So
11:17
that's
11:17
really
11:17
interesting
11:18
too.
11:18
All
11:18
right,
11:18
so
11:19
what
11:19
before
11:19
we
11:19
let
11:20
you
11:20
go,
11:21
what's
11:21
ahead
11:21
for
11:22
Gain
11:22
and
11:22
your
11:22
clients
11:23
in
11:23
2026?
11:24
Like
11:24
are
11:24
Natalie
11:24
and
11:25
Bob
11:25
going
11:25
to
11:25
get
11:25
a
11:25
new
11:26
brother
11:26
or
11:26
sister?
11:26
Like
11:27
what's
11:27
going.
11:29
So
11:29
they
11:29
will
11:29
get
11:30
a
11:30
sister
11:30
in
11:30
2027
11:31
selfie
11:33
that
11:33
will
11:33
help
11:33
them
11:34
to
11:34
onboard
11:34
new
11:35
suppliers.
11:35
Also
11:36
huge
11:36
issue
11:37
for
11:37
retailers.
11:38
Usually
11:38
it
11:39
can
11:39
take
11:39
three
11:39
to
11:39
four
11:39
months
11:40
to
11:40
add
11:41
new
11:41
suppliers
11:42
for
11:42
their.
11:42
You
11:43
know,
11:43
for
11:43
the
11:44
direct
11:44
procurement
11:45
for.
11:45
For
11:45
cpg,
11:46
it
11:46
can
11:46
take
11:46
even
11:47
a
11:47
year.
11:47
Oh
11:47
my
11:48
God,
11:48
that'd
11:48
be
11:48
so
11:48
helpful.
11:49
Oh
11:49
my
11:49
God.
11:50
Yeah,
11:50
yeah,
11:50
that,
11:50
that
11:51
would
11:51
be
11:51
the.
11:51
The
11:52
next
11:52
in
11:52
2027,
11:53
26
11:54
would
11:54
be
11:54
more
11:55
entire
11:55
onboarding.
11:56
Wow.
11:56
Yeah.
11:57
But
11:57
26
11:58
will
11:58
be,
11:58
I
11:59
mean
11:59
to
11:59
execute
12:00
for
12:00
retailers,
12:01
to
12:01
negotiate
12:02
for
12:02
them,
12:02
to
12:03
help
12:03
them
12:03
on
12:03
the
12:04
also
12:04
tactical
12:05
stuff
12:05
that
12:05
Bob
12:06
is
12:06
doing
12:07
to
12:07
help
12:07
them
12:07
to
12:08
be
12:08
much
12:08
more
12:08
productive.
12:09
And
12:09
regarding
12:10
your
12:10
point
12:10
about
12:10
the
12:11
buyers,
12:11
I
12:12
would
12:12
say
12:12
that
12:12
I
12:12
think
12:12
in
12:13
the
12:13
long
12:13
term
12:13
the
12:14
buyer
12:14
profession
12:15
will
12:15
change
12:15
a
12:15
bit.
12:15
People
12:16
will
12:16
be
12:16
more
12:16
managers
12:18
will
12:18
manage
12:19
the
12:19
AI
12:20
and
12:20
the
12:20
AI
12:20
will
12:21
execute.
12:22
So
12:22
it
12:22
will
12:22
be
12:23
more
12:23
an
12:23
expert
12:24
of
12:24
how
12:24
to.
12:25
To
12:26
manage
12:26
those
12:27
AI
12:27
employees
12:28
that
12:28
are.
12:28
Providing
12:29
the
12:29
manage
12:30
the
12:30
outcomes.
12:31
Yeah,
12:31
exactly.
12:32
Michael,
12:33
I'm
12:34
so
12:34
glad
12:34
that
12:35
you
12:35
are
12:35
kicking
12:36
us
12:36
off
12:36
here
12:36
for
12:37
our
12:37
NRF
12:38
must
12:38
see
12:38
tech
12:38
event.
12:40
If
12:40
people
12:40
listening
12:41
want
12:41
to
12:42
connect
12:42
with
12:42
you,
12:42
they
12:43
want
12:43
to
12:43
learn
12:43
more
12:43
about
12:44
Natalie,
12:44
about
12:45
Bob
12:46
or
12:46
about
12:46
anyone
12:47
else
12:47
down
12:47
the
12:48
line,
12:49
where
12:49
is
12:49
the
12:49
best
12:50
way
12:50
for
12:50
them
12:50
to
12:50
find
12:51
you
12:51
or
12:52
talk
12:52
to
12:52
you
12:52
at
12:52
nrf?
12:53
Yeah,
12:53
so
12:53
actually,
12:54
you
12:54
know,
12:54
I
12:54
was
12:54
many
12:55
times
12:55
in
12:56
dnr
12:56
but
12:56
actually
12:57
first
12:57
time
12:57
for
12:57
gain
12:58
to
12:58
be
12:58
in
12:58
dnrf.
12:59
Oh
13:00
yeah.
13:01
And
13:01
we
13:02
are
13:02
going
13:02
to
13:02
be
13:03
at
13:03
the
13:03
innovation
13:04
showcase.
13:06
Lucky
13:06
you.
13:07
Yeah,
13:07
yeah,
13:08
yeah.
13:08
It's
13:09
Boost
13:09
8041.
13:11
All
13:11
right,
13:12
so
13:12
8041.
13:14
Got
13:14
it.
13:15
And
13:15
the
13:15
innovation
13:16
area
13:16
always
13:16
pop.
13:17
Always
13:17
a
13:17
popular
13:18
spot
13:18
traffic.
13:19
Yes.
13:19
We
13:19
always
13:20
tell
13:20
people
13:20
to
13:20
make
13:20
sure
13:20
to
13:21
stop
13:21
by
13:21
there.
13:22
Well
13:22
Michael,
13:23
we'll
13:23
see
13:23
you
13:24
out
13:24
in
13:24
New
13:25
York
13:25
for
13:25
GAIN's
13:25
first
13:26
NRF
13:26
in
13:26
just
13:26
a
13:27
couple
13:27
weeks.
13:27
Thanks,
13:28
Michael.
13:28
Thank
13:29
you
13:29
guys.
13:29
Bye
13:29
Michael.
13:30
Now
13:30
that
13:30
the
13:31
contracts
13:31
and
13:31
pricing
13:32
for
13:32
our
13:32
products
13:32
have
13:33
been
13:33
negotiated
13:33
thanks
13:34
to
13:34
the
13:34
help
13:34
of
13:34
our
13:34
agentic
13:35
employees,
13:36
let's
13:36
move
13:37
now
13:37
to
13:37
how
13:37
we
13:38
successfully
13:38
sell
13:39
those
13:39
goods
13:39
at
13:39
home
13:40
and
13:40
in
13:40
this
13:41
case,
13:41
more
13:41
importantly
13:42
abroad.
13:43
Joining
13:43
US
13:43
Next
13:44
is
13:44
ESW's
13:45
Chief
13:45
Revenue
13:46
Officer,
13:47
Tonya
13:48
Likes.
13:48
Tonya,
13:49
welcome
13:49
to
13:50
Omnitalk.
13:50
We're
13:51
excited
13:51
to
13:51
have
13:51
you.
13:52
Thank
13:52
you
13:53
very
13:53
much
13:53
for
13:53
having
13:53
me,
13:53
Anne.
13:54
I'm
13:54
delighted
13:54
to
13:54
be
13:54
here.
13:55
Now
13:55
Tonya,
13:56
ESW
13:56
is
13:57
relatively
13:57
new
13:57
to
13:58
our
13:58
audience,
13:58
but
13:59
when
13:59
we
13:59
heard
13:59
about
13:59
you
14:00
and
14:00
the
14:00
work
14:00
that
14:00
your
14:00
team
14:00
is
14:01
doing,
14:01
we
14:01
had
14:01
to
14:02
share
14:02
with
14:02
our
14:02
listeners
14:03
and
14:03
had
14:04
to
14:04
set
14:04
you
14:04
up
14:04
for
14:05
our
14:06
NRF
14:06
Must
14:06
See
14:07
Tech
14:07
Preview.
14:08
Can
14:08
you
14:08
share
14:08
a
14:09
little
14:09
bit
14:09
about
14:09
your
14:10
role
14:10
and
14:11
specifically
14:12
how
14:12
ESW
14:13
helps
14:13
retailers
14:14
and
14:14
brands?
14:15
ESW
14:16
in
14:16
a
14:16
nutshell,
14:16
right?
14:17
It
14:17
makes
14:18
worldwide
14:19
E
14:19
commerce
14:20
powerful
14:20
and
14:20
simple
14:21
right
14:21
now.
14:22
Why,
14:22
why
14:22
is
14:23
the
14:23
importance
14:23
on
14:23
powerful
14:24
and
14:24
simple?
14:24
So
14:25
you
14:25
know,
14:25
global
14:26
shoppers
14:26
have
14:27
really,
14:27
really
14:27
high
14:28
expectations
14:29
and
14:29
they
14:29
want
14:30
a
14:30
seamless
14:30
checkout,
14:31
they
14:31
want
14:31
a
14:31
localized
14:32
experience,
14:33
right?
14:33
But
14:34
going
14:34
global,
14:35
right,
14:35
and
14:35
going
14:36
international
14:36
brings
14:37
like
14:37
a
14:37
ton
14:37
of
14:38
complexities
14:39
and
14:39
the
14:40
complexities
14:40
with
14:40
cross
14:41
border
14:41
trade
14:42
are
14:42
continuously,
14:43
they're
14:43
not
14:43
just
14:44
hard
14:44
now,
14:44
but
14:44
they've
14:45
become
14:45
even
14:45
harder,
14:46
right?
14:46
Especially
14:46
if
14:47
you
14:47
just
14:47
look
14:47
at
14:47
2025
14:48
as
14:48
a,
14:49
as
14:49
one,
14:49
one
14:50
period
14:50
of
14:50
time.
14:52
It's
14:52
around
14:53
trade
14:54
complexity
14:54
like
14:55
the
14:55
trade
14:55
compliance.
14:56
So
14:56
a
14:56
lot
14:57
of
14:57
changes
14:57
in
14:57
the
14:58
macroeconomics
14:58
this
14:59
year
14:59
which
14:59
we
14:59
can
14:59
talk
14:59
a
15:00
little
15:00
bit
15:00
about.
15:00
It's
15:01
around
15:02
the,
15:03
I
15:03
suppose
15:03
the
15:04
increasing
15:04
customer
15:05
experiences
15:06
around
15:06
delivery,
15:07
around
15:07
localization,
15:08
around
15:08
translation.
15:09
And
15:10
so
15:10
what
15:10
ESW
15:11
do
15:11
is
15:11
that
15:11
we
15:12
have,
15:12
we've
15:13
been
15:13
doing
15:13
this
15:13
for
15:13
a
15:13
long
15:14
time
15:15
and
15:15
we're
15:15
experts
15:16
in
15:17
bringing
15:18
brands
15:18
to
15:19
over
15:19
200
15:20
countries
15:21
in
15:21
a
15:21
very,
15:21
very
15:22
short,
15:22
condensed
15:23
but
15:23
highly
15:23
successful
15:24
time
15:24
frame.
15:25
So
15:25
that's
15:25
ESW
15:26
in
15:26
a
15:26
nutshell.
15:27
Me,
15:28
I
15:28
joined
15:29
ESW
15:30
earlier
15:30
this
15:30
year,
15:31
in
15:31
January,
15:32
actually
15:33
last
15:33
year,
15:33
actually
15:34
it's
15:34
nearly
15:34
a
15:35
year.
15:35
And,
15:36
and
15:36
so
15:37
I
15:37
am
15:38
here
15:38
to
15:38
accelerate
15:39
our
15:40
growth.
15:40
Right?
15:40
That's,
15:41
that's,
15:41
that,
15:41
that,
15:41
that's,
15:42
that's
15:42
my
15:43
sole
15:43
purpose
15:44
is
15:44
to
15:44
look
15:44
at
15:44
how
15:45
do
15:45
we,
15:45
how
15:45
do
15:46
we
15:46
grow
15:46
even
15:46
faster,
15:47
right
15:47
over
15:47
the
15:47
course
15:48
of
15:48
the
15:48
next
15:48
three
15:48
to
15:48
five
15:49
years.
15:49
Time
15:49
flies
15:50
when
15:50
you're
15:50
having
15:50
fun,
15:50
no
15:51
doubt.
15:51
So
15:52
you,
15:52
you
15:52
mentioned
15:52
something,
15:53
you
15:53
mentioned
15:53
something
15:54
too.
15:54
Like,
15:54
you
15:55
know,
15:55
to
15:55
say
15:55
that,
15:56
to
15:56
say
15:56
that
15:56
there
15:56
weren't
15:57
cross
15:57
border
15:57
challenges
15:58
in
15:59
2025
15:59
is
16:00
probably
16:00
the
16:00
biggest
16:00
understatement
16:01
of
16:01
the
16:01
year.
16:01
I
16:01
mean
16:01
those
16:02
challenges
16:02
were
16:02
immense
16:03
for
16:03
many,
16:03
many
16:03
retailers
16:04
and
16:04
brands.
16:04
So
16:05
like,
16:05
I'm
16:06
curious,
16:06
as
16:07
you
16:07
look
16:07
across
16:07
the
16:08
Portfolio
16:08
of
16:08
companies
16:09
you
16:09
work
16:09
with.
16:10
What
16:10
have
16:10
the
16:10
best
16:11
brands
16:11
done
16:12
to
16:12
balance
16:13
international,
16:14
to
16:14
balance
16:14
their
16:15
international
16:15
growth
16:15
ambitions,
16:17
you
16:17
know,
16:17
amid
16:18
all
16:18
these
16:18
like,
16:18
what
16:18
are
16:19
very
16:19
difficult
16:19
conditions.
16:20
The
16:21
key
16:21
word,
16:22
I
16:22
would
16:22
say,
16:22
because
16:23
it's
16:23
not
16:23
just
16:23
one
16:24
thing
16:24
that
16:24
a
16:24
great
16:24
brand
16:25
has
16:25
done
16:25
or
16:25
a
16:25
successful
16:26
brand,
16:27
it's
16:28
many
16:28
things,
16:28
right?
16:29
It's
16:29
the
16:29
sum
16:29
of
16:29
all
16:30
parts.
16:31
So
16:31
it's
16:31
about
16:31
being
16:32
able
16:32
to
16:32
be
16:33
a
16:33
brand
16:33
that
16:34
has
16:34
agility,
16:35
right?
16:35
Agility
16:36
in
16:36
how
16:36
you
16:36
operate
16:37
from
16:37
a
16:38
trade
16:39
perspective,
16:41
like
16:41
a
16:41
trade
16:41
compliance,
16:42
right?
16:42
So
16:42
duties
16:43
and
16:43
taxes
16:43
and
16:43
how
16:44
do
16:44
you
16:44
manage
16:45
that?
16:45
That,
16:46
you
16:46
know,
16:47
whole
16:47
conundrum
16:48
of
16:48
challenges.
16:49
Logistics,
16:50
right?
16:50
How
16:50
can
16:51
you
16:51
optimize
16:52
your
16:52
logistics
16:52
and
16:52
your
16:53
supply
16:53
chain
16:54
to
16:54
get
16:54
goods
16:55
in
16:55
the
16:55
buyer's
16:56
hands
16:56
quicker
16:57
but
16:57
also
16:57
to
16:57
provide
16:58
a
16:58
really,
16:58
really
16:59
seamless
16:59
return
17:00
experience
17:00
because
17:01
returns
17:01
is
17:01
really
17:01
important.
17:02
We've
17:02
all
17:02
had
17:02
that,
17:03
we've
17:04
always,
17:04
we've
17:04
all
17:05
received
17:05
that
17:05
good.
17:05
And
17:06
you
17:06
try
17:06
and
17:06
find
17:07
the
17:07
way
17:07
to
17:07
return
17:08
it.
17:08
And
17:08
if
17:08
you
17:08
can't,
17:09
it
17:09
leaves
17:09
a
17:09
lasting
17:10
impression
17:10
on
17:11
whether
17:11
I
17:12
would
17:12
buy
17:12
from
17:12
that
17:12
brand
17:13
in
17:13
the
17:13
future.
17:14
Right.
17:15
And
17:15
then
17:15
I
17:15
think
17:16
the
17:16
final
17:16
thing
17:16
I
17:16
would
17:17
say
17:18
is
17:18
around,
17:18
you
17:19
know,
17:19
you've
17:19
got
17:19
to
17:20
speak
17:20
the
17:20
consumer's
17:20
language.
17:21
So
17:21
a
17:21
consumer
17:22
in
17:22
the
17:22
US
17:24
has
17:24
different
17:25
requirements
17:26
than
17:26
a
17:26
consumer
17:27
in
17:27
Brazil
17:28
or
17:28
in
17:29
Malaysia
17:30
or
17:30
in,
17:30
or
17:31
in
17:31
Ireland,
17:31
right.
17:32
They
17:32
have
17:32
a
17:32
different
17:33
experience
17:33
in
17:33
terms
17:34
of
17:34
what
17:35
is
17:35
the
17:35
payment
17:35
method
17:35
I
17:36
want
17:36
to
17:36
use,
17:37
how
17:37
do
17:38
I
17:38
want
17:38
to
17:38
be
17:38
communicated
17:39
to,
17:39
what's
17:39
the
17:39
tone
17:39
of
17:40
communication,
17:41
what's
17:41
the
17:41
merchandise
17:42
selection
17:42
that
17:42
I
17:42
want
17:43
to
17:43
see
17:43
is
17:43
very,
17:43
very
17:43
different.
17:44
So
17:44
it's.
17:45
So
17:45
the
17:45
brands
17:45
that
17:45
have
17:46
been
17:46
most
17:46
successful
17:47
are
17:47
ones
17:48
that
17:48
have
17:48
the
17:48
ability
17:49
to
17:49
operate
17:50
in
17:50
a
17:50
very
17:51
agile,
17:51
fast
17:52
paced
17:52
environment.
17:53
Now
17:54
I'll
17:54
give
17:54
you
17:54
an
17:54
example
17:55
of
17:56
some
17:56
of
17:56
the
17:56
things.
17:57
One
17:57
thing,
17:58
right,
17:58
that's
17:58
that
17:59
kind
17:59
of
17:59
comes
17:59
to
18:00
mind
18:00
in
18:00
terms
18:00
of
18:01
2025
18:03
specifically,
18:05
you
18:05
know,
18:05
with
18:05
the
18:06
trade
18:06
complexities
18:07
that
18:07
were
18:07
placed
18:08
upon
18:08
organizations
18:10
and
18:10
the
18:10
removal
18:11
of
18:11
the
18:11
800
18:12
de
18:12
minimis
18:12
in
18:13
the
18:13
U.S.
18:13
there
18:14
was
18:14
an
18:14
awful
18:14
lot
18:14
of
18:15
European
18:15
brands
18:16
that
18:17
have
18:18
a,
18:19
a
18:19
strong
18:20
presence
18:20
in
18:20
the
18:20
US
18:21
market,
18:22
but
18:22
suddenly
18:23
they
18:23
were
18:23
impacted
18:24
by
18:24
these
18:26
huge
18:26
duty
18:27
bills,
18:28
right?
18:29
And
18:29
so
18:29
the
18:29
cost
18:30
of
18:30
doing
18:30
business
18:30
in
18:31
the
18:31
US
18:31
which
18:31
represents
18:32
a
18:32
big
18:32
significant
18:33
chunk
18:33
of
18:34
their
18:34
existing
18:34
revenue,
18:35
suddenly
18:35
becomes
18:36
unprofitable.
18:36
So
18:37
we
18:37
worked
18:37
with
18:37
all
18:38
of
18:38
our
18:38
customers
18:38
that
18:38
had
18:39
these
18:39
flows
18:39
into
18:39
the
18:40
US
18:41
and
18:42
we
18:42
changed
18:42
their
18:42
business
18:43
model.
18:43
So
18:44
we
18:44
used
18:44
our
18:45
US
18:45
entity
18:47
to
18:47
be
18:47
able
18:47
to
18:47
become
18:48
the
18:48
importer
18:48
of
18:49
record
18:50
and
18:51
to
18:52
so
18:52
this
18:52
like
18:53
we
18:53
imported
18:54
the
18:54
goods
18:54
into
18:54
the
18:55
US
18:55
Under
18:55
ESW
18:56
US
18:58
we
18:58
declared
18:59
and
18:59
calculated
19:00
the
19:00
duties
19:00
and
19:00
taxes
19:01
on
19:01
the
19:01
wholesale
19:01
value
19:02
of
19:02
the
19:02
product
19:02
rather
19:03
than
19:03
the
19:03
retail
19:03
value.
19:04
And
19:04
it
19:05
actually
19:05
reduced
19:05
landed
19:06
costs
19:06
for
19:07
retailers
19:07
for
19:07
up
19:07
to
19:08
60%.
19:09
So
19:09
this
19:09
type
19:10
of
19:10
agility
19:11
in
19:11
your
19:11
business
19:12
models
19:12
is
19:12
essential
19:13
to
19:14
be
19:14
able
19:14
to
19:14
react
19:14
accordingly
19:15
depending
19:15
on,
19:16
you
19:16
know,
19:16
what's
19:17
the,
19:17
what's
19:17
the
19:17
problem
19:18
of
19:18
the
19:18
day?
19:18
And
19:19
Tonia,
19:19
explain
19:20
a
19:20
little
19:20
bit
19:20
like
19:21
who,
19:21
what
19:21
types
19:22
of
19:22
retailers?
19:22
Like
19:23
where
19:23
are
19:23
they?
19:23
How
19:24
many
19:24
SKUs
19:24
are
19:24
we
19:25
talking?
19:25
Like
19:25
what,
19:26
what's
19:26
the
19:26
breadth
19:27
of
19:27
types
19:27
of
19:28
retailers
19:28
that
19:28
you
19:28
can
19:28
work
19:29
with?
19:29
Yeah,
19:30
obviously
19:30
they
19:30
need
19:30
to
19:31
be
19:31
agile,
19:31
but
19:31
like
19:32
who's
19:32
coming
19:32
to
19:33
you
19:33
and
19:33
what
19:33
kind
19:33
of
19:34
states
19:34
were
19:34
they
19:34
in?
19:34
How
19:35
are
19:35
they
19:35
handling
19:35
this
19:36
prior
19:36
to
19:36
working
19:36
with
19:36
you?
19:37
So
19:37
ESW
19:38
focuses
19:38
on
19:39
large
19:39
strategic
19:40
brands.
19:41
So
19:41
we
19:41
play
19:41
very
19:42
much
19:42
in
19:42
the
19:42
enterprise
19:42
space.
19:43
The
19:44
industries
19:44
that
19:45
we
19:45
focus
19:45
on
19:46
is
19:46
apparel,
19:47
luxury
19:47
apparel
19:48
or
19:48
fashion,
19:49
beauty,
19:50
so
19:50
health
19:50
and
19:50
beauty
19:51
and
19:51
then
19:52
consumer
19:52
electronics,
19:53
to
19:53
give
19:53
you
19:54
some
19:54
examples.
19:55
On
19:55
the
19:56
fashion
19:56
side
19:57
we
19:57
work
19:57
with
19:57
Calvin
19:58
Klein,
19:58
we
19:59
work
19:59
with
19:59
Levi's,
20:00
Golden
20:00
Goose
20:00
and
20:01
J.
20:01
Crew.
20:01
So
20:02
you
20:02
can
20:02
see
20:02
quite
20:02
high
20:03
end
20:04
household
20:04
names
20:05
as
20:05
well.
20:05
In
20:06
beauty,
20:06
l'
20:06
Oreal
20:07
is
20:07
a
20:07
customer
20:08
and
20:09
on
20:09
the
20:09
consumer
20:10
electronics
20:10
space,
20:11
Logitech
20:11
is
20:11
a
20:12
customer.
20:12
You
20:12
know,
20:12
there's,
20:13
there's
20:13
one
20:13
retailer
20:14
in
20:14
question
20:15
who
20:15
had
20:15
a
20:15
very,
20:16
very
20:16
strong
20:16
presence
20:17
in
20:17
the
20:17
US
20:17
but
20:18
they'd
20:18
never
20:18
actually
20:19
done
20:19
business.
20:19
And
20:20
they
20:20
came
20:20
to
20:20
us
20:20
two
20:21
and
20:21
a
20:21
half
20:21
years
20:21
ago
20:22
and
20:22
said
20:22
we
20:22
need
20:23
to
20:23
go
20:23
international
20:24
but
20:24
we
20:24
don't
20:24
know
20:24
how
20:24
to
20:25
do
20:25
it.
20:25
And
20:25
so
20:26
they
20:26
partnered
20:26
with
20:26
ESW
20:27
and
20:28
they
20:28
now
20:28
exist
20:29
in
20:29
not
20:29
only
20:29
in
20:30
the
20:30
US
20:30
their,
20:30
their
20:31
domestic
20:31
market,
20:32
but
20:32
across
20:33
50
20:33
other
20:33
regions
20:35
and
20:35
countries.
20:36
And
20:36
it's
20:36
an
20:37
area
20:37
that
20:37
is
20:37
significantly
20:38
growing.
20:38
And,
20:39
and
20:39
to
20:40
answer
20:40
your
20:40
question,
20:40
what
20:40
did
20:41
they
20:41
do
20:41
before
20:41
this?
20:44
There's,
20:45
you
20:45
know,
20:45
there's
20:46
some,
20:46
I
20:47
think
20:47
opening
20:47
in
20:47
one
20:48
country
20:49
is
20:50
very
20:50
different
20:50
to
20:51
opening
20:52
in
20:52
50
20:52
to
20:53
80
20:53
countries.
20:53
Right.
20:54
And
20:55
so
20:55
brands
20:57
typically
20:57
will
20:58
have
20:58
a
20:58
presence,
20:59
an
20:59
in
21:00
house
21:00
presence
21:00
on
21:00
their
21:01
own.
21:01
Right.
21:02
And
21:02
they're
21:02
able
21:02
to
21:03
manage
21:03
that
21:03
very,
21:04
very
21:04
effectively
21:04
and
21:05
efficiently.
21:06
The
21:06
complexities
21:07
come
21:07
with
21:08
scale.
21:09
Right.
21:10
And
21:10
so
21:11
we
21:11
would
21:11
typically
21:11
partner
21:12
with
21:12
brands
21:13
when
21:13
they're
21:13
on
21:13
a
21:13
kind
21:14
of
21:14
a,
21:14
on
21:15
that
21:15
growth,
21:16
I
21:16
suppose,
21:17
starting
21:17
to
21:17
see
21:17
that
21:18
growth
21:18
and
21:19
want
21:19
to
21:19
have
21:20
a
21:20
strong
21:21
international
21:21
presence,
21:22
be
21:22
that
21:22
from
21:22
Europe
21:23
into
21:23
the
21:23
US
21:24
and
21:24
wider
21:25
or
21:25
from
21:25
the
21:25
US
21:26
into
21:26
international
21:27
markets
21:27
outside
21:27
of
21:27
the
21:28
us.
21:29
Got
21:29
it.
21:29
And
21:30
so
21:30
I
21:30
think
21:31
what
21:31
I'm
21:31
hearing
21:31
and
21:32
what
21:32
I
21:32
understand,
21:32
Tonia,
21:33
is
21:33
that
21:34
not
21:34
only
21:34
are
21:34
you
21:34
helping
21:35
them
21:35
figure
21:36
out
21:36
how
21:36
to
21:36
do
21:36
incredibly
21:37
difficult
21:38
things
21:38
like
21:38
reduce
21:39
the
21:39
duties
21:40
and
21:40
taxes
21:41
on
21:41
importing
21:42
products,
21:43
but
21:43
also
21:43
how
21:44
to
21:44
work
21:45
to
21:45
set
21:45
up
21:46
their
21:46
site
21:46
so
21:46
that
21:46
they're
21:47
ready
21:47
to
21:47
go
21:48
into
21:48
and
21:49
Market
21:49
their
21:49
products
21:50
to
21:50
a
21:51
variety
21:51
of
21:52
countries
21:53
and
21:53
locations
21:55
all
21:55
in
21:55
one
21:56
business.
21:56
Really
21:57
like
21:57
you
21:57
kind
21:58
of
21:58
do
21:58
it
21:58
from
21:58
soup
21:59
to
21:59
nuts.
21:59
You're
21:59
helping
21:59
them
22:00
kind
22:00
of
22:00
figure
22:00
out
22:00
how
22:01
to
22:01
go
22:02
international.
22:03
I'd
22:03
also
22:03
add,
22:04
as
22:04
well
22:04
as
22:05
redesigning,
22:06
typically
22:06
redesigning
22:07
their
22:07
supply
22:07
chain
22:08
to
22:08
ensure
22:08
the
22:08
products
22:09
are
22:09
in
22:09
the
22:09
right
22:09
places
22:10
at
22:10
the
22:10
right
22:11
times
22:11
to
22:12
meet
22:12
the
22:12
needs
22:13
and
22:13
enabling
22:14
the
22:14
shopper
22:14
to
22:14
have
22:14
that
22:15
seamless
22:15
return
22:15
experience.
22:16
So,
22:17
you
22:17
know,
22:17
ESW
22:19
operate
22:19
as
22:20
an
22:20
extension
22:20
of
22:20
your
22:21
business,
22:21
right?
22:21
So
22:22
we're
22:22
your
22:22
partner
22:23
for
22:23
international
22:24
and
22:24
whatever
22:24
international
22:25
means,
22:25
depending
22:25
on
22:26
where
22:26
your
22:27
HQ
22:27
is,
22:27
right.
22:27
So
22:28
international
22:28
could
22:28
go
22:29
to
22:29
the
22:29
US
22:29
or
22:29
go
22:30
outside
22:30
of
22:30
the
22:30
us.
22:31
I
22:31
want
22:31
to
22:31
dive
22:32
into
22:32
the
22:33
season
22:33
that
22:33
we
22:33
just
22:34
got
22:34
out
22:34
of
22:34
the
22:34
holidays.
22:36
I
22:36
have
22:36
to
22:36
imagine
22:37
this
22:37
adds
22:37
even
22:37
more
22:38
complexity
22:39
as,
22:39
you
22:39
know,
22:39
gifting
22:40
comes
22:40
to
22:40
play,
22:41
comes
22:41
into
22:41
play.
22:42
Were
22:42
there
22:42
any
22:43
things
22:43
that
22:44
you
22:44
saw
22:45
from
22:45
your
22:46
stable
22:46
of
22:46
clients
22:47
just
22:47
changing
22:48
patterns
22:48
in
22:49
how
22:49
international
22:50
shoppers
22:50
behaved,
22:51
how
22:51
things
22:52
like
22:52
agentic
22:53
AI
22:53
shopping
22:53
change
22:54
consumer
22:54
behavior?
22:55
You
22:55
have
22:56
this
22:56
wealth
22:56
of
22:56
knowledge.
22:57
What
22:57
were
22:57
some
22:58
of
22:58
the
22:58
trends
22:59
maybe
22:59
that
22:59
you
22:59
saw
23:00
coming
23:00
out
23:00
of
23:01
the
23:01
holiday
23:01
shopping
23:02
season
23:02
that
23:02
you
23:02
could
23:02
share
23:03
with
23:03
our
23:03
audience?
23:04
So
23:04
it's
23:04
interesting,
23:05
as
23:05
you
23:05
say,
23:06
and
23:06
coming
23:06
out
23:07
of
23:07
the
23:07
holiday
23:07
shopping
23:08
season,
23:08
so
23:08
that,
23:09
that's,
23:10
you
23:10
know,
23:10
we,
23:11
what
23:11
we
23:11
saw
23:11
was
23:12
in
23:12
prior
23:13
years,
23:13
holiday
23:14
shopping
23:15
ended
23:15
sort
23:16
of
23:16
mid
23:17
December,
23:18
but
23:18
actually
23:19
this
23:19
year,
23:19
right,
23:20
it
23:20
started
23:20
much
23:21
earlier
23:21
and
23:21
it
23:22
ended
23:22
really
23:23
only
23:23
up
23:23
until
23:23
the
23:24
end
23:24
of
23:24
December.
23:24
And
23:24
we've
23:25
even
23:25
seen
23:25
that,
23:26
that,
23:26
that,
23:26
that
23:28
the
23:28
volumes
23:29
maintain
23:30
into
23:30
January,
23:31
right?
23:31
So
23:31
the
23:31
season
23:31
is
23:32
stretching,
23:33
right?
23:33
What
23:33
used
23:34
to
23:34
be
23:34
a
23:34
day
23:34
or
23:34
two,
23:35
two
23:35
or
23:35
three
23:36
years
23:36
ago
23:36
has
23:37
become
23:37
of
23:37
it
23:38
like
23:38
a,
23:38
a
23:39
certain
23:39
festival
23:39
or
23:39
a
23:40
certain,
23:41
you
23:41
know,
23:41
in
23:42
like
23:42
elbow
23:43
du
23:43
fin
23:43
in
23:43
Mexico
23:44
used
23:45
to
23:45
be
23:45
a
23:45
week,
23:45
it's
23:46
now
23:46
three
23:46
weeks,
23:46
right?
23:47
Singles
23:48
day.
23:48
Used
23:48
to
23:48
be
23:49
a
23:49
day,
23:49
it's
23:49
now
23:50
two
23:50
weeks,
23:50
right?
23:51
So
23:51
everywhere
23:52
globally
23:53
we've
23:53
seen
23:53
the
23:53
stretching,
23:54
global
23:54
stretching
23:55
of
23:55
the
23:55
season,
23:55
right?
23:56
And
23:56
9%
23:57
of
23:58
annual
23:59
sales,
23:59
like
24:00
holiday
24:00
sales,
24:01
right?
24:01
Landed
24:01
in
24:01
that
24:02
period
24:02
and
24:02
it's
24:02
the
24:02
highest
24:03
we've
24:03
probably
24:04
ever
24:04
seen,
24:04
right?
24:05
So
24:06
big
24:06
stretching
24:06
of
24:06
the
24:07
season.
24:07
One
24:08
second
24:09
thing
24:09
is
24:09
the
24:10
omnichannel
24:10
experience.
24:11
So
24:12
we're
24:12
all
24:13
really
24:13
adapted
24:14
going
24:14
online
24:14
and
24:14
buying
24:15
our
24:15
stuff.
24:16
But
24:16
the,
24:17
the,
24:17
the
24:17
ability
24:17
to
24:18
seamlessly
24:18
buy
24:18
online
24:19
or
24:20
in
24:20
store,
24:21
return
24:21
online
24:22
or
24:22
return
24:22
in
24:23
store,
24:23
irrelevant
24:23
of
24:24
where
24:24
I
24:24
bought
24:24
it
24:24
from,
24:25
right?
24:26
And
24:27
having
24:27
a
24:27
really
24:28
strong
24:28
mobile
24:28
experience
24:29
is
24:30
non
24:30
negotiable,
24:31
right?
24:31
Is
24:31
non
24:31
negotiable
24:32
for
24:32
brands.
24:33
And
24:34
also
24:36
your
24:36
logistics
24:37
partner
24:38
is
24:39
so
24:39
essential.
24:40
So
24:40
a
24:41
massive
24:41
uptake
24:42
in,
24:42
in
24:43
expedited
24:43
delivery
24:44
options,
24:44
right?
24:45
People
24:45
want,
24:45
when
24:46
they
24:46
buy
24:46
they
24:47
want
24:47
the
24:47
product
24:47
now
24:48
I'm
24:48
one
24:48
of
24:48
them.
24:48
Right.
24:49
I
24:49
might
24:49
have
24:49
the
24:50
time,
24:50
but
24:50
when
24:50
I
24:50
bought
24:51
something,
24:51
I
24:51
want
24:51
to
24:51
get
24:51
it,
24:52
like,
24:52
you
24:52
know.
24:52
Yeah.
24:53
And
24:54
so
24:54
that
24:54
was
24:54
a
24:55
huge.
24:55
Those,
24:56
those
24:56
three
24:56
areas,
24:57
I
24:57
would
24:57
say
24:57
were
24:58
the
24:58
biggest,
24:59
biggest
24:59
changes.
25:00
Wow.
25:00
Your
25:00
point
25:01
about,
25:01
your
25:01
point
25:01
about
25:01
Singles
25:02
days
25:02
is
25:02
right
25:02
on
25:03
too.
25:03
Like,
25:03
Singles
25:03
day
25:04
came
25:04
in
25:04
with
25:04
went
25:04
this
25:05
year.
25:05
Like,
25:05
it
25:05
was
25:05
like,
25:05
I
25:06
didn't.
25:06
It
25:06
didn't
25:06
even
25:06
make
25:06
a
25:06
noise
25:07
to
25:07
me
25:07
over
25:07
here
25:07
in
25:08
the
25:08
US
25:08
and
25:08
did
25:08
you
25:09
even
25:09
notice
25:09
it?
25:09
Like,
25:09
usually
25:10
you
25:10
get
25:10
all
25:10
this
25:10
PR
25:11
about
25:11
it
25:11
and
25:11
I
25:11
didn't
25:12
even
25:12
hear
25:12
about
25:12
it
25:12
this
25:12
year.
25:12
And
25:12
it's
25:13
probably
25:13
to
25:13
your
25:13
point
25:13
that
25:13
it's
25:14
just
25:14
become
25:14
a
25:14
different
25:14
event
25:15
in
25:15
and
25:15
of
25:15
itself.
25:16
So
25:16
that's
25:16
really
25:16
fascinating,
25:16
Tanya.
25:17
The
25:17
other
25:18
point
25:18
that
25:18
this
25:18
brings
25:18
up
25:18
for
25:18
me
25:19
is
25:19
I
25:19
can
25:19
remember
25:19
back
25:19
when
25:20
I
25:20
was
25:20
an
25:20
executive
25:20
at
25:20
Target
25:21
and
25:21
I
25:21
had
25:21
a
25:21
buddy
25:21
who's,
25:22
whose
25:22
job
25:23
was
25:23
this.
25:23
This
25:23
was
25:23
like
25:23
2017.
25:24
His
25:24
job
25:25
was
25:25
to
25:25
figure
25:25
out
25:26
international
25:26
expansion.
25:27
And
25:27
I
25:27
can
25:27
imagine
25:28
that
25:29
had
25:29
he
25:29
had
25:29
you
25:29
at
25:30
the
25:30
time,
25:30
he
25:30
would
25:30
have
25:30
definitely
25:31
reached
25:31
out
25:31
to
25:31
you.
25:31
So
25:32
with
25:32
NRF
25:32
just
25:33
days
25:33
away,
25:34
what
25:34
are
25:35
some
25:35
of
25:35
the
25:35
considerations
25:36
that
25:36
people
25:36
that
25:37
are
25:37
exploring
25:38
this
25:38
type
25:38
of,
25:39
you
25:39
know,
25:39
relationship
25:40
with
25:40
you
25:40
or
25:41
an
25:41
external
25:41
partner
25:42
like
25:42
esw,
25:43
what
25:43
are
25:43
some
25:43
of
25:43
the
25:44
things
25:44
they
25:44
should
25:44
consider
25:45
in
25:45
trying
25:46
to
25:46
approach
25:46
that
25:46
conversation
25:47
with
25:47
someone
25:48
like
25:48
yourself?
25:49
We
25:49
work
25:49
with
25:49
a
25:49
lot
25:49
of
25:50
brands.
25:50
So,
25:50
you
25:51
know,
25:51
your
25:51
ex
25:51
Target,
25:52
and
25:53
coming
25:53
from
25:53
your
25:53
days,
25:54
there
25:54
was
25:54
one
25:54
person
25:54
that's
25:55
tasked
25:55
with
25:55
international,
25:56
right?
25:57
Yep.
25:58
International
25:58
is
25:59
a
25:59
beast,
25:59
Right.
26:00
It's
26:00
not
26:00
only
26:00
a
26:00
beast
26:01
in
26:01
terms
26:01
of
26:01
complexity,
26:02
but
26:02
it's
26:02
a
26:02
beast
26:03
in
26:03
terms
26:03
of
26:03
potential.
26:04
Right.
26:05
And
26:05
so
26:06
the
26:06
way
26:06
to
26:06
look
26:06
at
26:07
it
26:07
is,
26:07
like,
26:07
we
26:07
would
26:07
bring
26:08
a
26:08
lot
26:08
of
26:08
brands
26:09
on
26:09
a
26:09
journey.
26:09
So
26:09
the
26:10
typical,
26:10
the
26:10
first
26:10
thing
26:10
a
26:11
brand
26:11
will
26:11
think
26:11
about
26:11
is,
26:12
do
26:12
I
26:12
buy
26:13
it,
26:13
do
26:13
I
26:13
build
26:13
in
26:14
house?
26:14
I've
26:15
done
26:15
one
26:15
country.
26:16
Isn't
26:16
that
26:16
hard,
26:17
is
26:17
it?
26:17
Right?
26:18
Now,
26:20
when
26:20
you
26:21
think
26:21
about
26:21
do
26:21
I
26:21
build
26:22
in
26:22
house?
26:23
You
26:24
have
26:24
to
26:24
think
26:24
about,
26:24
well,
26:25
what's
26:25
the
26:25
cost?
26:26
That's
26:26
going
26:26
to.
26:26
That's
26:27
going
26:27
to
26:27
how
26:27
much
26:27
cost,
26:28
how
26:28
much
26:28
speed?
26:29
And
26:29
do
26:30
I
26:30
have
26:30
the
26:30
right
26:31
team?
26:31
Right.
26:32
So
26:32
cost
26:32
it
26:33
can
26:33
take.
26:33
It
26:33
can
26:34
cost
26:34
between
26:35
2.5
26:36
to
26:36
4.5
26:37
million
26:37
just
26:38
to
26:38
get
26:38
up
26:38
and
26:38
running.
26:39
Right.
26:40
And
26:40
that
26:40
would
26:40
mean
26:40
probably
26:41
a
26:41
launch
26:42
within
26:42
one
26:42
to
26:42
two
26:43
years
26:43
per
26:44
country.
26:45
Per
26:45
country.
26:46
Right.
26:46
And
26:47
then
26:47
you
26:47
think
26:47
about,
26:47
what
26:48
do
26:48
I
26:48
need
26:48
to
26:48
do
26:48
to
26:48
maintain
26:49
that.
26:49
Right.
26:51
And
26:51
how
26:52
am
26:52
I
26:52
going
26:52
to
26:52
understand
26:53
those
26:53
localized
26:53
customer
26:54
experiences?
26:55
So
26:55
the
26:55
first
26:56
thing
26:56
to
26:57
think
26:57
about
26:57
it,
26:57
that's
26:58
one
26:58
thing.
26:58
And
26:58
then
26:58
the
26:58
second
26:59
part,
26:59
actually,
26:59
Chris
26:59
Is,
27:00
is
27:01
this
27:01
the
27:01
best
27:02
place
27:02
to
27:03
spend
27:03
my
27:03
effort?
27:04
We're
27:04
a
27:04
retailer,
27:05
right?
27:06
Do
27:06
I.
27:06
Does
27:07
the
27:07
retailer
27:07
not
27:07
focus
27:08
on
27:08
building
27:08
phenomenal
27:09
product?
27:10
Right.
27:11
And
27:11
then
27:11
do
27:11
you
27:12
partner
27:12
with
27:12
someone
27:13
who
27:13
has
27:14
the
27:14
technical
27:16
and
27:16
technical,
27:17
I
27:17
suppose,
27:17
expertise,
27:19
the
27:19
investment
27:20
and
27:20
the
27:20
innovation
27:21
in
27:21
the
27:21
platform
27:22
to
27:22
not
27:23
only
27:23
do
27:23
what's
27:23
required
27:24
today,
27:25
but
27:25
tomorrow
27:27
is
27:27
another
27:27
beast.
27:28
Right.
27:28
And
27:28
let's
27:28
talk
27:28
about
27:29
Agentic
27:29
in
27:29
a
27:29
moment
27:30
because
27:30
we
27:30
have
27:30
to
27:30
talk
27:30
about
27:31
that.
27:31
Okay,
27:32
so
27:32
what
27:32
does
27:32
tomorrow
27:33
bring?
27:34
And
27:35
so
27:35
you've
27:35
got
27:35
to
27:35
just
27:35
think
27:35
what's
27:36
best
27:36
for
27:36
your
27:36
business
27:38
and
27:38
then
27:38
when
27:38
you
27:38
think
27:38
about
27:38
what's
27:39
best
27:39
for
27:39
your
27:39
business
27:39
and
27:39
you
27:39
decide,
27:40
actually,
27:40
let's
27:40
look
27:41
at
27:41
partners,
27:41
you've
27:42
got
27:42
to
27:42
find
27:42
someone,
27:43
a
27:43
partner
27:43
that
27:44
gives
27:44
you
27:45
your
27:45
brand
27:46
the
27:46
control
27:48
to
27:48
determine
27:48
where
27:49
you
27:49
go,
27:49
what
27:50
you
27:50
do
27:50
and
27:50
how
27:50
you
27:51
do
27:51
it.
27:51
Right.
27:52
So
27:52
what
27:52
we're
27:53
not
27:54
is
27:54
an
27:54
organization
27:55
that
27:55
will
27:55
come
27:55
in
27:55
and
27:56
dictate
27:56
to
27:56
a
27:56
brand
27:56
what
27:57
they
27:57
need
27:57
to
27:57
do.
27:57
What
27:58
we
27:58
are
27:58
is
27:58
an
27:58
organization
27:59
that
27:59
will
27:59
work
28:00
with
28:00
a
28:00
brand
28:00
to
28:01
understand
28:01
their
28:02
strategy
28:02
and
28:02
operate
28:03
within
28:03
that
28:04
strategy
28:04
to
28:04
meet
28:04
the
28:05
brand's
28:05
growth
28:05
expectations.
28:07
So
28:07
that
28:07
would
28:07
be
28:08
how
28:08
I
28:08
would
28:08
frame
28:08
it
28:09
for
28:09
brands.
28:10
I'd
28:10
love
28:10
to
28:11
talk
28:11
to
28:11
anyone
28:12
at
28:12
NRF
28:13
who
28:13
wants
28:13
to
28:13
talk
28:13
to
28:13
us.
28:14
And
28:15
so
28:15
I'm
28:15
sure
28:16
I
28:16
can
28:16
share
28:16
some
28:16
contact
28:17
details
28:18
at
28:18
the
28:18
end
28:18
so
28:19
that
28:19
people
28:19
can
28:19
reach
28:19
out,
28:19
you
28:20
know?
28:20
Yeah,
28:20
for
28:21
sure.
28:21
I,
28:21
I
28:21
love
28:21
what
28:22
you're
28:22
throwing
28:22
down
28:22
here
28:22
too,
28:23
because,
28:23
like,
28:23
what
28:24
I
28:24
took
28:24
from
28:24
that
28:24
conversation
28:25
that
28:25
I
28:25
can
28:25
remember
28:25
vividly
28:26
back
28:26
in
28:26
the
28:26
day
28:26
was
28:27
like,
28:27
it
28:27
seems
28:28
like
28:28
it's
28:28
easy
28:28
fruit
28:29
on
28:29
the
28:29
ground
28:29
just
28:29
waiting
28:29
to
28:30
be
28:30
picked
28:30
up,
28:30
but
28:31
yet
28:31
it's
28:31
so
28:32
complicated
28:32
for
28:32
so
28:32
many
28:33
people.
28:33
And
28:33
I
28:33
think
28:33
this
28:34
is
28:34
the
28:34
right
28:34
approach
28:34
that
28:35
one
28:35
has
28:35
to
28:36
take
28:36
if
28:36
they
28:36
want
28:36
to
28:36
capitalize
28:37
it
28:37
on
28:38
to
28:38
the
28:38
extent
28:38
that
28:39
one
28:39
could.
28:39
So,
28:40
all
28:40
right,
28:40
you
28:40
mentioned
28:40
agentic
28:41
AI.
28:41
So
28:42
we'll
28:42
give
28:42
you
28:42
the
28:42
floor
28:42
here
28:42
to
28:42
close
28:43
this
28:43
out.
28:44
You
28:44
know,
28:44
before
28:44
we
28:44
let
28:45
you
28:45
go,
28:45
what
28:45
are
28:45
your
28:45
thoughts
28:45
on
28:45
agent?
28:46
Ok,
28:46
so
28:47
agentic,
28:48
it's
28:48
a
28:48
real
28:48
buzzword,
28:49
right?
28:50
And
28:50
it
28:50
is.
28:50
Everyone
28:51
is
28:51
trying
28:51
to
28:52
kind
28:52
of
28:52
wrap
28:52
their
28:52
head
28:53
around
28:53
what
28:53
is
28:54
agentic.
28:55
I
28:55
would
28:55
say
28:56
it's
28:56
a
28:56
buzzword,
28:56
yes,
28:57
but
28:57
it's
28:57
a
28:57
very
28:58
important
28:58
one
28:58
because
28:58
it's
28:59
going
28:59
to
28:59
be
28:59
the
28:59
operating
29:00
model
29:00
for
29:00
the
29:00
next
29:01
decade
29:02
of
29:02
retail.
29:02
Right.
29:03
And
29:04
what
29:05
Agentic?
29:06
I
29:07
suppose
29:07
if
29:07
you
29:07
think
29:07
about
29:08
global
29:08
scale
29:08
in
29:08
26,
29:09
27,
29:09
28
29:10
and
29:10
beyond,
29:11
it's
29:11
not
29:11
going
29:11
to
29:11
come
29:12
from
29:12
just
29:12
working
29:12
harder.
29:13
Like
29:13
we've
29:14
all
29:14
seen
29:14
that
29:14
in
29:14
our
29:14
daily
29:15
lives,
29:15
right?
29:15
Do
29:15
we
29:16
work
29:16
harder
29:16
or
29:16
do
29:16
we
29:17
work
29:17
smarter?
29:18
We
29:18
work
29:18
smarter,
29:19
right?
29:19
And
29:19
we
29:20
use
29:20
AI
29:20
in
29:21
our
29:21
daily
29:21
lives
29:21
to
29:21
make
29:22
us
29:22
more
29:22
efficient.
29:22
It's
29:23
the
29:23
exact
29:23
same
29:23
with
29:23
E
29:24
commerce.
29:24
So
29:24
I
29:25
actually
29:25
think
29:25
Agentic
29:26
is
29:26
the
29:26
future
29:27
of
29:27
smart
29:27
commerce.
29:28
Right.
29:28
It's
29:29
smart
29:29
commerce
29:30
that
29:30
we're
29:30
looking
29:30
at.
29:30
What
29:31
does
29:31
that
29:31
mean
29:31
though?
29:33
And
29:33
like,
29:34
yeah,
29:35
like
29:35
what
29:35
does
29:35
that
29:36
give
29:36
a
29:36
retailer
29:37
and
29:37
what
29:37
does
29:37
it
29:37
give
29:38
a
29:39
buyer?
29:39
Right.
29:40
So
29:40
you
29:41
have
29:41
to
29:41
think
29:41
about
29:41
it
29:41
in
29:42
the
29:42
way
29:42
of
29:43
how
29:43
can
29:43
you
29:44
localize
29:44
experiences
29:46
for
29:46
consumers
29:47
automatically
29:48
without
29:48
having
29:49
to
29:49
think
29:49
about
29:49
it?
29:50
How
29:50
can
29:50
you
29:51
optimize
29:52
your
29:52
payment
29:52
flows
29:53
that
29:53
boost
29:54
your
29:54
conversion
29:55
and
29:55
minimize
29:56
your
29:56
fraud?
29:57
Right.
29:57
So
29:57
getting
29:58
more
29:58
shoppers
29:58
through
29:59
in
29:59
a
29:59
safe
30:00
and
30:00
high
30:01
converting
30:01
environment,
30:02
how
30:03
can
30:03
you
30:03
have
30:03
a
30:04
dynamic
30:04
logistics
30:05
decisioning
30:06
engine,
30:06
right,
30:07
that
30:07
protects
30:07
your
30:08
margins
30:08
but
30:09
meets
30:09
customer
30:09
expectations
30:11
and
30:11
then
30:11
how
30:11
can
30:12
you
30:12
onboard
30:13
and
30:13
launch
30:14
in
30:14
new
30:14
countries
30:15
and
30:16
new
30:16
markets
30:17
in
30:17
a
30:17
faster
30:17
time
30:17
than
30:18
has
30:18
ever
30:18
done
30:18
before?
30:19
And
30:19
that's
30:19
what
30:20
we're
30:20
doing.
30:20
We're
30:20
building
30:20
with
30:21
our
30:21
Gentic
30:21
hub.
30:22
So
30:22
we're
30:22
not
30:23
layering
30:24
agentic
30:24
on
30:25
top
30:25
or
30:25
layering
30:26
AI
30:26
on
30:26
it.
30:27
We're,
30:28
we're
30:28
optimizing
30:29
every
30:29
single
30:29
part
30:30
of,
30:30
of
30:31
how
30:31
we
30:31
bring
30:31
brands
30:32
to
30:32
life
30:33
in
30:33
international
30:33
markets
30:34
with
30:35
Agentic.
30:36
So
30:36
it's
30:36
a,
30:37
it's
30:37
a
30:37
complete
30:37
soup
30:38
to
30:38
nuts,
30:38
right.
30:38
Transformation
30:39
about
30:39
using
30:40
Agentic
30:40
throughout
30:40
the
30:41
flow.
30:41
And
30:41
the
30:41
benefit
30:42
for
30:42
the
30:42
retailers,
30:44
we
30:44
strongly
30:45
believe,
30:46
right.
30:46
The
30:46
benefit
30:47
for
30:47
the
30:47
retailers
30:48
is
30:48
going
30:48
to
30:48
be
30:48
significant
30:49
because
30:50
retail
30:51
has
30:51
outgrown
30:51
manual
30:52
decision
30:52
making
30:53
and
30:55
the
30:55
complexity
30:56
now
30:56
requires
30:57
that
30:57
AI
30:58
can
30:58
adapt
30:58
faster.
30:59
Faster
31:00
than
31:00
how
31:00
the
31:00
conditions
31:01
are
31:01
changing.
31:01
And
31:02
the
31:02
conditions
31:02
are
31:02
changing
31:03
very
31:03
quickly.
31:04
So
31:04
we're
31:05
really
31:05
excited
31:05
by
31:05
it.
31:06
We're
31:06
delighted
31:07
to
31:07
talk
31:07
to
31:07
brands
31:08
about
31:08
what
31:09
it
31:09
could
31:09
do
31:09
for
31:09
them.
31:10
But
31:10
yeah,
31:11
we're
31:11
launching
31:12
our
31:12
AgentIQ
31:12
hub
31:13
very
31:13
soon.
31:14
And
31:14
actually
31:14
Frank
31:15
Coertas,
31:16
our
31:16
Chief
31:16
Product
31:16
officer
31:17
is
31:17
at
31:18
NRF
31:18
with
31:18
us
31:19
talking
31:19
about
31:19
Agentix.
31:20
So
31:20
eager
31:21
to
31:21
have
31:21
as
31:21
many
31:22
conversations
31:22
with
31:22
brands
31:23
that
31:23
have
31:23
an
31:23
interest.
31:24
Tony,
31:24
I
31:24
think
31:25
what
31:25
I'm
31:25
taking
31:25
away
31:26
from
31:26
this
31:26
and
31:26
what
31:26
I
31:27
think
31:27
is
31:27
going
31:27
to
31:27
be
31:27
a
31:28
really
31:28
important
31:29
theme
31:29
out
31:29
at
31:30
NRF
31:30
and
31:30
for
31:30
the
31:31
rest
31:31
of
31:31
2026
31:32
is
31:32
really
31:33
giving
31:34
retailers
31:34
the
31:34
opportunity
31:35
to
31:35
rely
31:36
on
31:36
partners
31:37
like
31:38
ESW
31:39
so
31:39
that
31:39
they
31:39
can
31:40
focus
31:40
on
31:40
what
31:40
you
31:40
talked
31:41
about
31:41
earlier,
31:42
producing
31:43
the
31:43
best
31:43
quality
31:44
goods,
31:44
getting
31:44
their
31:45
product
31:45
set
31:46
right
31:46
so
31:47
that
31:47
they
31:47
can
31:47
expand
31:48
it
31:48
internationally
31:50
and
31:50
that
31:50
they
31:50
don't
31:50
have
31:50
to
31:51
worry
31:51
about
31:51
the
31:52
constantly
31:52
changing
31:53
things
31:53
like
31:53
de
31:54
minimis
31:54
exceptions
31:55
like
31:55
tariffs
31:55
changing
31:56
at
31:56
a
31:56
moment's
31:57
notice
31:58
or
31:58
about
31:59
the
31:59
intricacies
32:00
of
32:00
how
32:00
customers
32:01
are
32:01
going
32:01
to
32:01
be
32:01
shopping
32:02
cross
32:02
border.
32:03
And
32:03
so
32:04
that's
32:04
one
32:04
thing
32:04
that
32:05
I'm
32:05
really
32:05
hopeful
32:06
that
32:06
our
32:06
listeners
32:07
will
32:07
be
32:07
able
32:07
to
32:08
learn
32:09
and
32:10
to
32:10
find
32:10
the
32:10
right
32:10
partners.
32:11
That's
32:11
one
32:11
of
32:11
the
32:11
reasons
32:11
why
32:12
we
32:12
do
32:12
this
32:12
show,
32:12
is
32:13
to
32:13
help
32:13
you
32:13
find
32:13
those
32:14
partners
32:14
who
32:14
can
32:14
carry
32:15
you
32:15
through
32:15
through
32:16
this
32:16
next
32:17
wave
32:17
of,
32:17
of
32:18
technology
32:18
when
32:19
it
32:19
comes
32:19
to
32:19
shopping.
32:20
Tonia,
32:21
you
32:21
mentioned
32:22
a
32:22
couple
32:22
times
32:22
you'll
32:23
be
32:23
out
32:23
at
32:23
nrf,
32:24
your
32:24
team
32:24
will
32:24
be
32:24
out
32:24
at
32:25
nrf.
32:25
What's
32:26
the
32:26
best
32:26
way
32:26
for
32:26
our
32:27
listeners
32:27
to
32:27
get
32:27
in
32:27
touch
32:28
with
32:28
you
32:29
either
32:29
out
32:29
there
32:29
or
32:30
afterward?
32:31
So
32:31
we
32:31
are
32:31
there
32:32
and
32:32
we've
32:32
got
32:32
a
32:33
big
32:33
presence
32:33
there.
32:34
We'll
32:35
be
32:35
on
32:35
the
32:35
floor.
32:36
We're
32:36
having
32:36
some
32:36
events.
32:37
So
32:37
if
32:38
you
32:38
want
32:38
to
32:38
reach
32:38
out
32:38
to
32:38
me
32:39
on
32:39
LinkedIn,
32:40
my
32:40
email
32:41
as
32:41
well
32:41
as
32:42
is,
32:45
please
32:45
reach
32:45
out.
32:46
We'll
32:46
connect,
32:47
we'll
32:47
follow
32:47
up
32:48
and
32:48
we'll,
32:48
we'll
32:48
get
32:49
some
32:49
time
32:49
time
32:49
in
32:49
the
32:50
calendar
32:50
to,
32:50
to
32:51
understand
32:51
your
32:51
needs.
32:52
Fantastic.
32:52
Thanks,
32:53
Tonia.
32:53
Thank
32:54
you
32:54
very
32:54
much.
32:55
Okay,
32:56
we've
32:56
covered
32:56
products
32:56
moving
32:57
and
32:57
being
32:57
sold
32:57
across
32:57
borders.
32:58
Let's
32:58
talk
32:58
about
32:59
tracking
32:59
the
32:59
movement
32:59
of
32:59
products
33:00
in
33:00
our
33:00
stores.
33:01
Pervase
33:01
idea
33:02
is
33:02
helping
33:02
retailers
33:03
achieve
33:03
this.
33:03
So
33:03
it's
33:04
with
33:04
great
33:04
pleasure
33:05
that
33:05
we
33:05
introduce
33:05
Pervase
33:06
IDs
33:07
Chief
33:07
Executive
33:08
Officer
33:09
Peter
33:09
Oram
33:10
to
33:10
the
33:10
show.
33:10
Welcome
33:11
to
33:11
omnitalk,
33:11
Peter.
33:12
Yeah.
33:12
Hey,
33:13
thanks
33:13
everyone.
33:13
Thanks
33:13
for
33:14
having
33:14
me.
33:14
Yeah,
33:14
it's
33:15
great
33:15
to
33:15
have
33:15
you
33:15
on
33:15
the
33:15
show.
33:15
I
33:15
mean
33:16
we
33:16
had,
33:16
we
33:16
had
33:16
Pervasive
33:17
ID
33:17
on
33:17
our
33:17
Spotlight
33:18
podcast
33:18
series
33:19
probably
33:19
over
33:19
a
33:19
year
33:20
ago
33:20
now
33:20
and
33:20
I
33:20
can't
33:21
remember
33:21
exactly
33:21
when,
33:22
but
33:22
it
33:22
was
33:22
a
33:23
really
33:23
great
33:23
conversation.
33:23
I
33:24
remember
33:24
it
33:24
was
33:24
one
33:24
of
33:24
my
33:24
favorite
33:24
conversations
33:25
that
33:25
we
33:25
did
33:26
in
33:26
that
33:26
year.
33:27
So.
33:27
But
33:28
there's
33:28
probably
33:28
a
33:28
lot
33:28
of
33:28
people
33:29
that
33:29
are
33:29
still
33:29
watching
33:30
or
33:30
listening
33:30
and
33:30
meeting
33:31
you
33:31
for
33:31
the
33:31
first
33:31
time.
33:32
So
33:32
tell
33:33
us
33:33
a
33:33
little
33:33
bit
33:33
more
33:33
about
33:34
Pervase
33:34
id,
33:34
what
33:35
it
33:35
is,
33:35
what
33:35
it
33:35
does
33:36
and
33:36
what
33:36
are
33:36
you
33:36
most
33:37
excited
33:37
about
33:37
in
33:38
leading
33:38
the
33:38
company?
33:39
Wow.
33:40
So
33:40
really
33:41
perverse
33:41
idea
33:41
is
33:42
fundamentally
33:42
about
33:43
turning
33:43
physical
33:44
retail
33:44
into
33:44
a
33:45
fully
33:45
visible
33:46
data
33:46
rich
33:47
environment.
33:47
If
33:47
you
33:47
want
33:47
to
33:48
go
33:48
down
33:48
that
33:48
route.
33:49
I've
33:49
seen
33:49
a,
33:50
a
33:50
podcast
33:51
that
33:51
you
33:51
don't
33:51
agree
33:51
Orange
33:52
just
33:52
with
33:52
Troy
33:52
yesterday
33:53
and
33:53
it
33:54
is
33:54
coming
33:54
the
33:54
whole,
33:55
you
33:55
know,
33:56
data
33:56
rich
33:57
environments,
33:58
the
33:58
digital
33:58
twins,
33:59
all
33:59
that
33:59
sort
33:59
of
33:59
stuff.
34:00
Basically
34:00
our
34:00
hardware
34:02
is
34:03
what
34:03
I
34:03
call
34:03
the
34:03
data
34:04
collection
34:04
devices
34:05
and
34:06
that
34:06
fundamentally
34:07
creates
34:07
the
34:08
data
34:08
that
34:08
all
34:08
these
34:08
guys
34:09
need
34:10
to
34:10
then
34:10
make
34:11
them
34:11
informed
34:11
decisions.
34:12
So
34:12
effectively,
34:13
you
34:13
know,
34:14
we
34:14
manufacture
34:14
fixed
34:15
overhead
34:15
RFID
34:16
solutions.
34:17
Our
34:17
Latest
34:18
is
34:18
Trackmaster
34:19
Trackmaster
34:19
3X.
34:20
It
34:21
offers
34:21
real
34:21
time
34:22
RFID
34:22
tracking
34:23
that
34:23
gives
34:23
the
34:23
retailers
34:24
total
34:24
visibility
34:25
of
34:25
their
34:25
inventory
34:26
across
34:26
the
34:26
store.
34:27
In
34:27
practical
34:28
terms,
34:28
we
34:29
help
34:29
retailers
34:29
reduce
34:30
like
34:30
they're
34:30
out
34:30
of
34:31
stocks,
34:32
improve
34:32
availability,
34:33
we
34:33
cut
34:34
the
34:34
shrink
34:34
in
34:35
and
34:35
run
34:35
more
34:35
efficient
34:36
store
34:36
operations.
34:38
In
34:38
terms
34:38
of
34:39
what
34:39
excites
34:39
me
34:39
about
34:40
leading
34:40
the
34:40
company
34:40
now,
34:40
I've
34:41
been
34:41
here
34:41
about
34:42
a
34:42
year
34:42
and
34:42
a
34:42
half
34:42
now
34:44
and
34:44
we're
34:45
generally
34:45
solving
34:46
real
34:46
world
34:46
problems.
34:47
That's
34:48
where
34:48
we're
34:48
at
34:48
now.
34:48
I
34:49
think
34:49
the
34:49
world's
34:49
moved
34:49
on,
34:50
the
34:50
world
34:50
of
34:50
retail's
34:51
moved
34:51
on
34:51
and
34:52
we're
34:52
sort
34:52
of
34:52
redefining
34:53
what's
34:53
possible
34:53
in
34:54
this
34:54
connected
34:54
retail
34:55
world
34:55
that's
34:55
come
34:56
up.
34:56
We're
34:57
laying
34:57
the
34:57
foundations
34:58
for
34:58
this
34:58
sort
34:58
of
34:58
autonomous
34:59
stores,
35:00
frictionless
35:00
experiences,
35:01
smarter
35:02
decision
35:02
making,
35:03
generally
35:04
power.
35:04
And
35:04
the
35:04
in
35:04
store
35:05
software
35:05
partners
35:06
such
35:06
as
35:06
Gray
35:06
Orange
35:07
need
35:07
that
35:07
clarity.
35:08
These
35:08
guys,
35:08
you
35:08
know,
35:09
we're
35:09
working
35:09
with
35:09
all
35:10
these
35:10
partners
35:10
where
35:10
we
35:11
can
35:11
push
35:11
the
35:11
data
35:11
to
35:12
these
35:12
guys
35:12
so
35:12
they
35:13
can
35:13
make
35:13
them
35:13
effective
35:14
solutions,
35:15
software
35:16
solutions
35:16
for
35:16
the
35:16
retailer.
35:17
Pete,
35:17
let's
35:18
talk
35:18
a
35:18
little
35:18
bit
35:18
about
35:18
the
35:19
technology
35:19
and
35:20
kind
35:20
of
35:20
the,
35:20
the
35:21
evolution
35:21
of
35:21
it,
35:21
the
35:22
timeline.
35:23
Let's,
35:24
let's
35:24
start
35:24
with
35:25
kind
35:25
of
35:25
what
35:26
the
35:26
tech
35:26
looked
35:26
like.
35:27
You
35:27
mentioned
35:27
the
35:27
overhead
35:28
readers.
35:28
What
35:28
were
35:29
you
35:29
collecting,
35:29
you
35:30
know,
35:30
the
35:30
last
35:30
couple
35:31
of
35:31
years
35:31
and
35:32
where,
35:32
where
35:32
is
35:33
it
35:33
now
35:34
and
35:34
then
35:34
what
35:34
do
35:34
you
35:34
think
35:34
the
35:35
future
35:35
looks
35:35
like?
35:35
Like
35:36
kind
35:36
of
35:36
take
35:36
us
35:36
through
35:37
that
35:37
timeline.
35:38
Yeah,
35:38
well
35:39
the
35:39
evolution's
35:40
been
35:40
huge
35:40
really.
35:41
So
35:41
yeah,
35:42
I
35:42
mean
35:42
Pervas
35:43
ID
35:43
has
35:43
been
35:44
around
35:44
for
35:44
a
35:44
few
35:44
years
35:45
and,
35:45
and
35:45
concentrated
35:46
on
35:46
overhead,
35:47
fixed
35:47
overhead
35:47
solutions.
35:49
Generally
35:50
though,
35:50
you
35:51
know
35:51
the
35:52
market
35:52
hadn't
35:52
come
35:53
to
35:53
it
35:53
rfid
35:54
although
35:54
it's
35:55
been
35:55
around
35:55
forever
35:55
it
35:56
seems
35:56
ever,
35:56
you
35:56
know
35:56
the
35:57
last
35:57
20
35:57
years
35:58
everyone
35:58
was
35:58
using
35:59
handheld,
35:59
comfortable
36:00
of
36:00
using
36:00
handheld.
36:01
But
36:01
I
36:02
think
36:02
the
36:02
market's
36:02
come
36:03
to
36:03
us.
36:03
So
36:03
as
36:03
we've
36:04
evolved
36:04
over
36:04
the
36:04
last
36:05
two
36:05
or
36:05
three
36:05
years,
36:05
evolving
36:06
the
36:06
solution,
36:06
working
36:06
with
36:07
them
36:08
sort
36:08
of
36:09
then
36:09
forward
36:09
thinking
36:10
retailers
36:11
with
36:11
the
36:11
solution
36:12
and
36:12
the
36:12
market
36:13
is
36:13
coming
36:13
together.
36:14
So
36:14
I
36:14
think
36:14
over
36:14
the
36:15
next
36:15
sort
36:15
of
36:15
three
36:16
to
36:16
five
36:16
year,
36:17
you
36:17
know,
36:17
overhead
36:18
will
36:18
become
36:18
a
36:18
big
36:19
part
36:19
of
36:19
the,
36:19
the
36:20
in
36:20
store
36:20
solution.
36:22
If
36:22
I,
36:22
if
36:23
I
36:23
look
36:23
at
36:23
what
36:24
we
36:24
focused
36:24
on
36:25
really
36:25
is
36:25
creating
36:25
that
36:26
continuous
36:26
hands
36:27
free
36:28
tracking
36:28
that
36:29
requires
36:29
far
36:29
less
36:30
hardware.
36:30
So
36:30
initially
36:31
on,
36:31
on
36:31
overhead
36:32
hardware
36:33
solutions
36:33
there
36:33
was
36:33
a
36:34
lot
36:34
of
36:34
infrastructure,
36:35
you
36:35
know,
36:35
a
36:35
lot
36:35
of
36:35
hardware
36:36
right
36:36
be
36:37
deployed
36:38
that
36:38
obviously
36:38
increased
36:39
the
36:39
cost
36:39
for
36:39
the
36:40
retailer.
36:41
Therefore
36:41
the
36:41
ROI
36:42
was
36:42
never
36:42
there.
36:43
Now
36:44
with
36:44
Trackmaster
36:45
3X
36:45
we've
36:45
worked
36:46
closely
36:46
with,
36:46
with
36:47
some
36:47
of
36:47
our,
36:48
our
36:48
pilot
36:48
partners,
36:49
retail
36:49
pilot
36:50
partners
36:50
such
36:50
as
36:51
as
36:51
Urban
36:51
Group
36:52
and,
36:53
and
36:53
now
36:53
we've
36:53
came
36:53
up
36:54
with
36:54
our
36:54
Trackmaster
36:55
3X
36:56
and
36:56
we've,
36:56
we've
36:57
just
36:57
about
36:58
doubled
36:58
the
36:59
coverage
36:59
area
37:00
of
37:00
one
37:00
of
37:00
our
37:00
readers.
37:01
So
37:01
we've
37:01
took
37:01
it
37:01
from
37:02
two
37:02
and
37:02
a
37:02
half
37:02
thousand
37:03
square
37:03
feet
37:04
to
37:04
four
37:04
and
37:05
a
37:05
half
37:05
thousand
37:05
square
37:06
feet
37:06
with
37:07
One
37:07
reader
37:07
using
37:08
multiple
37:09
antennas.
37:10
So
37:10
all
37:10
of
37:10
a
37:11
sudden
37:11
that's
37:11
far
37:12
less
37:12
hardware
37:12
that
37:13
you
37:13
would
37:13
need
37:13
for
37:13
a
37:13
typical
37:14
retail
37:14
store.
37:15
Therefore
37:16
that
37:16
ROI
37:16
is
37:17
a
37:17
lot
37:17
quicker.
37:17
Typically
37:18
six
37:18
to
37:18
eight
37:18
months
37:19
is
37:19
what
37:19
we're
37:19
seeing.
37:20
Therefore
37:21
it's
37:21
a
37:21
much
37:21
more
37:22
viable
37:22
solution
37:23
for,
37:23
for
37:23
these
37:23
guys,
37:24
for
37:24
the
37:24
retailers
37:25
when
37:25
they're
37:25
looking
37:25
at
37:25
what
37:25
they're
37:26
going
37:26
to
37:26
do
37:26
next
37:26
and
37:26
how
37:27
are
37:27
they
37:27
going,
37:28
real
37:28
time
37:28
data
37:29
that
37:29
they
37:29
want
37:29
to
37:29
consume,
37:30
that
37:30
they
37:30
want
37:30
to
37:30
make
37:30
these
37:31
decisions
37:31
off,
37:31
you
37:31
know,
37:32
so
37:32
it's
37:33
sort
37:33
of
37:33
been
37:33
a
37:33
culmination
37:34
of
37:34
that
37:34
work
37:35
in
37:35
the
37:35
sort
37:36
of
37:36
latest
37:36
generation
37:37
of
37:37
what
37:37
we've
37:37
got.
37:38
And
37:38
we
37:39
want
37:39
to
37:39
take
37:39
that
37:39
overhead
37:39
RFID
37:40
platform
37:41
faster,
37:41
more
37:42
accurate,
37:43
with
37:43
a
37:43
much
37:44
lower
37:44
cost
37:44
of
37:45
ownership.
37:45
You
37:46
know,
37:46
how
37:46
do
37:46
we
37:46
get
37:46
there?
37:47
How
37:47
do
37:47
we
37:47
make
37:47
it
37:47
viable
37:48
for
37:48
all
37:48
retailers?
37:50
I
37:51
think
37:51
where
37:51
we're
37:51
heading
37:52
next
37:52
is
37:52
even
37:53
more
37:53
exciting.
37:54
Richer,
37:54
real
37:54
time
37:55
analytics,
37:55
tighter
37:56
integrations
37:57
with
37:57
additional
37:57
store
37:58
solutions
37:58
like
37:58
offering
37:59
more
37:59
automation.
38:00
And
38:00
then
38:00
ultimately
38:01
we're
38:01
enabling
38:02
retailers
38:02
to
38:02
operate
38:03
with
38:03
a
38:03
kind
38:03
of
38:03
visibility
38:04
and
38:04
responsiveness
38:05
that
38:05
consumers
38:06
now
38:06
expect.
38:08
You
38:08
know,
38:08
post
38:08
Covid,
38:09
that's
38:09
what
38:10
they
38:10
want
38:10
now,
38:10
this
38:10
whole
38:10
omnichannel
38:11
experience.
38:12
And
38:12
how
38:12
do
38:12
we,
38:13
how
38:13
do
38:13
we
38:13
grab
38:13
that
38:14
data
38:14
in
38:14
store
38:15
to
38:15
deliver
38:15
the
38:15
same
38:16
in
38:16
store
38:16
experience
38:16
as
38:17
online
38:17
experience,
38:18
you
38:18
know.
38:19
Yeah,
38:19
I
38:19
love
38:19
the
38:19
name,
38:19
the
38:20
trackmaster,
38:21
the
38:21
track
38:21
master
38:22
3X.
38:22
That's
38:22
such
38:23
a
38:23
great
38:23
name.
38:23
It's
38:23
such
38:23
a
38:23
great
38:24
name.
38:24
And
38:24
what
38:24
I
38:24
remember
38:24
too
38:25
from
38:25
the
38:25
first
38:25
conversation
38:25
which
38:26
you
38:26
hit
38:26
on
38:26
too
38:26
there,
38:26
Peter,
38:27
is
38:27
like
38:28
there's
38:28
really
38:29
two
38:29
things
38:29
when
38:29
you,
38:29
that
38:30
come
38:30
into
38:30
the
38:30
equation.
38:30
It's
38:31
about
38:31
coverage
38:31
and
38:32
precision.
38:33
And
38:33
that's
38:33
what
38:33
you
38:33
guys
38:34
really
38:34
stand
38:34
out
38:35
in
38:35
the
38:35
marketplace,
38:36
you
38:37
know,
38:37
in
38:37
terms
38:37
of
38:37
tracking
38:38
RFID
38:38
tags.
38:39
So,
38:40
so
38:40
to
38:40
that
38:40
point
38:40
about
38:41
precision,
38:42
when
38:42
you
38:42
think
38:42
about
38:43
the
38:43
times
38:43
where
38:44
with,
38:44
with
38:45
whatever,
38:45
with
38:45
whatever
38:46
partners
38:46
you
38:46
can
38:46
discuss,
38:47
you
38:47
know,
38:47
or
38:47
however
38:47
you
38:48
want
38:48
to
38:48
talk
38:48
about
38:48
this.
38:48
I'm
38:48
curious,
38:49
like
38:50
when
38:50
the,
38:50
in
38:50
those
38:51
times
38:51
where
38:51
you've
38:51
been
38:51
successful
38:52
working
38:52
with
38:53
retailers,
38:54
what
38:55
is,
38:55
what
38:55
has
38:55
generally
38:56
brought
38:56
about
38:56
that
38:56
success
38:57
and
38:57
what
38:57
does
38:57
that
38:57
process
38:58
look
38:58
like?
38:59
It's
38:59
an
38:59
interesting
39:00
one,
39:00
I
39:00
think
39:00
for,
39:01
for
39:01
us,
39:01
success
39:02
is
39:02
when
39:02
a
39:02
retailer
39:03
goes
39:03
from
39:03
guessing
39:04
to
39:04
knowing
39:05
and
39:05
you
39:06
can
39:06
see
39:06
the
39:06
light
39:06
bulb
39:07
switch
39:07
on,
39:08
you
39:08
know,
39:08
in
39:08
terms
39:08
of
39:09
what
39:09
they
39:09
were
39:09
doing
39:10
in
39:10
the
39:10
data
39:10
that
39:11
they
39:11
were
39:11
able
39:11
to
39:11
collect.
39:12
Let's
39:12
say
39:12
they
39:12
were
39:12
doing
39:12
a,
39:13
you
39:13
know,
39:13
hands
39:14
handheld
39:15
operation
39:16
typically
39:17
once
39:17
a
39:17
day
39:17
at
39:17
best,
39:18
maybe
39:18
once
39:18
a
39:18
week.
39:19
And
39:19
then
39:19
when
39:20
they
39:20
go
39:20
to,
39:22
you
39:22
know,
39:23
basically
39:23
100
39:24
online
39:24
constantly,
39:25
they're,
39:26
they're
39:26
literally,
39:26
they're
39:27
going
39:27
from
39:27
guessing
39:27
to
39:27
knowing
39:28
exactly
39:28
where
39:28
they're
39:29
at.
39:29
So
39:29
that
39:29
we're
39:29
seeing
39:30
stores
39:30
achieve
39:30
stock
39:30
accuracy
39:31
and
39:31
they're
39:31
jumping
39:32
from
39:32
70
39:32
up
39:33
to
39:33
98,
39:33
99.
39:35
And
39:35
that,
39:35
that
39:36
effectively
39:36
creates
39:37
significant
39:37
reductions
39:38
in
39:38
the
39:38
out
39:38
of
39:38
stocks,
39:39
the
39:39
lost
39:39
sales
39:40
that
39:40
they
39:40
had.
39:40
You've
39:41
got
39:41
real
39:41
measurable
39:42
shrink
39:42
reduction
39:43
to
39:43
see
39:43
the
39:43
patterns
39:44
in
39:44
the
39:44
data.
39:44
They
39:45
can
39:45
see
39:45
when
39:45
stuff
39:46
is,
39:46
is
39:46
disappearing
39:47
front
39:47
of
39:47
store,
39:47
disappearing
39:48
back
39:48
of
39:49
store,
39:49
what
39:50
percentages
39:50
of
39:51
that
39:51
really
39:51
is,
39:52
is
39:52
happening
39:52
on
39:52
a
39:52
back
39:53
shift,
39:53
on
39:53
a
39:53
night
39:53
shift.
39:54
Smoother
39:55
replenishment
39:55
and
39:56
better
39:56
staff
39:56
productivity
39:57
that
39:57
on
39:57
shelf
39:58
availability
39:58
that
39:59
you
39:59
know,
39:59
they've
39:59
been
40:00
crying
40:00
out
40:00
for.
40:01
Everybody
40:02
knows,
40:02
you
40:02
know,
40:02
you,
40:03
you,
40:03
you
40:03
walk
40:04
into
40:04
a
40:04
store
40:04
and
40:04
you're
40:05
looking
40:05
for
40:05
the
40:05
size
40:05
that
40:06
you're
40:06
looking
40:06
for
40:06
size,
40:07
you
40:07
know,
40:07
size
40:07
12,
40:07
green
40:08
top
40:08
and
40:08
it's
40:08
not
40:09
there.
40:09
You
40:09
don't
40:09
necessarily
40:10
ask
40:10
for
40:11
that,
40:11
you
40:11
just
40:11
move
40:12
on
40:12
and
40:12
that's
40:12
a
40:12
lost
40:13
sale
40:13
to
40:13
them.
40:14
Now
40:15
by
40:15
inputting
40:16
the
40:16
technology,
40:17
you
40:17
know,
40:17
every
40:18
hour,
40:19
they're
40:19
able
40:19
to
40:20
replenish
40:20
that
40:20
on
40:21
shelf
40:21
availability.
40:21
So
40:22
they're
40:22
at
40:22
100%
40:23
constantly
40:23
and
40:24
that's
40:24
increased
40:25
their
40:25
sales,
40:25
you
40:25
know,
40:26
typically
40:26
between
40:26
5
40:27
and
40:27
10%.
40:27
Somewhere
40:28
in
40:28
between
40:28
that.
40:29
So
40:30
you
40:30
can
40:31
really
40:31
see,
40:31
you
40:32
know,
40:32
when
40:32
we
40:32
first
40:33
started
40:33
looking
40:33
at
40:33
this
40:33
it
40:34
was
40:34
all
40:34
about,
40:34
for
40:35
us
40:35
it
40:35
was
40:35
about
40:35
offering
40:36
better
40:37
stock
40:37
inventory.
40:38
But
40:39
that
40:39
really
40:39
is
40:39
the
40:40
core
40:40
for
40:40
everything
40:41
they
40:41
want
40:41
to
40:41
achieve
40:42
to
40:42
run
40:42
their
40:42
business.
40:43
And
40:43
we
40:43
can
40:44
see
40:44
them
40:44
start
40:44
to
40:44
run
40:44
their
40:45
whole
40:45
business
40:45
based
40:46
off
40:46
the
40:46
data
40:46
that
40:46
they're
40:47
now
40:47
gathering
40:47
in
40:48
store.
40:48
You
40:48
know,
40:48
they're
40:48
looking
40:49
back
40:49
up
40:49
the
40:50
supply
40:50
chain
40:50
into
40:51
logistics,
40:51
their
40:51
stock
40:52
inventory,
40:52
everything
40:53
is
40:53
a
40:53
lot
40:53
leaner
40:54
and
40:54
meaner.
40:54
But
40:55
you
40:55
can
40:55
really
40:55
see
40:55
it
40:56
in
40:56
their
40:56
eyes
40:56
when
40:56
they
40:56
see
40:57
that,
40:57
you
40:57
know,
40:57
they
40:58
can
40:58
actually
40:58
make
40:58
them
40:59
informed
40:59
decisions
41:00
on
41:00
a,
41:00
an
41:00
hourly
41:01
daily
41:01
basis.
41:02
And,
41:02
and
41:03
that
41:03
goes
41:03
across
41:03
the
41:04
group.
41:04
They're
41:04
moving
41:04
stock
41:05
between
41:05
stores.
41:06
Every
41:06
store
41:06
now
41:07
is
41:07
effectively,
41:08
you
41:08
know,
41:08
they
41:08
want
41:09
to
41:09
do
41:09
click
41:09
and
41:09
collect
41:10
and
41:10
it
41:11
becomes
41:11
a
41:11
depot
41:12
and
41:12
they're
41:13
moving
41:13
stock
41:13
between
41:13
them
41:14
stores
41:14
and
41:15
effectively
41:15
selling
41:16
everything
41:16
at
41:16
full
41:16
price
41:17
rather
41:17
than
41:17
having
41:17
to
41:18
sell
41:18
it
41:18
at
41:18
a
41:18
reduced
41:19
cost
41:19
in
41:19
a
41:19
sale,
41:20
you
41:20
know,
41:20
far
41:21
more
41:21
efficient
41:21
with
41:22
the
41:22
stock
41:22
that
41:22
they
41:22
have.
41:23
So
41:23
yeah,
41:23
we,
41:24
we
41:24
see
41:24
it
41:24
as
41:25
just
41:25
their
41:25
eyes
41:25
light
41:25
up
41:26
when
41:26
they
41:26
start
41:26
to
41:26
see
41:27
that
41:27
data.
41:27
Yeah,
41:28
when
41:28
you
41:28
talk
41:28
about
41:28
it
41:28
like
41:28
that,
41:28
I
41:29
mean
41:29
this
41:29
is
41:29
another
41:29
tech
41:30
that
41:30
we're
41:30
previewing
41:30
that.
41:31
To
41:31
me
41:31
it
41:31
feels
41:31
like
41:31
a
41:31
no
41:32
brainer
41:32
that
41:32
you
41:32
know,
41:32
every
41:33
retailer
41:33
should,
41:33
every
41:33
retailer
41:34
this
41:34
is
41:34
applicable
41:35
should
41:35
at
41:35
least
41:35
be
41:35
having
41:35
a
41:35
conversation
41:37
with
41:37
you
41:37
all.
41:38
You
41:38
know,
41:38
in
41:38
our,
41:38
in,
41:38
in
41:39
our
41:39
opinion,
41:40
you
41:40
know,
41:40
the
41:40
other
41:40
thing
41:41
you
41:41
didn't
41:41
mention
41:41
that
41:41
I
41:41
wanted
41:42
to
41:42
ask
41:42
you
41:42
about
41:42
too
41:42
is
41:43
like
41:43
operationally
41:44
this
41:44
just
41:44
feels
41:45
like
41:45
an
41:45
easier
41:45
approach
41:46
too
41:46
because
41:46
you
41:47
have
41:47
the
41:48
RFID
41:48
reader
41:49
in
41:49
the
41:49
ceiling
41:50
versus
41:50
the
41:51
handhelds.
41:51
And
41:52
I
41:52
don't
41:52
have
41:52
to
41:52
train
41:53
my
41:53
employees.
41:53
I
41:53
don't
41:53
have
41:53
to
41:54
worry
41:54
about
41:54
those
41:54
devices
41:55
getting
41:55
lost.
41:55
Like
41:56
it's
41:56
just
41:56
always
41:57
there
41:57
latent
41:57
in
41:58
the
41:58
background
41:58
tracking
41:59
things.
41:59
I
41:59
mean
41:59
that's
42:00
gotta
42:00
be
42:00
a
42:00
key
42:00
component
42:01
of
42:01
this
42:01
too
42:02
fundamentally.
42:02
I
42:02
mean
42:03
the
42:03
staff
42:03
are
42:03
then
42:03
able
42:04
to
42:04
really
42:04
spend
42:04
the
42:05
time
42:05
on
42:05
the
42:05
tasks
42:05
that
42:06
the
42:06
retailer
42:06
want
42:07
them
42:07
to
42:07
spend
42:07
time
42:07
on
42:08
and
42:08
dealing
42:08
with
42:08
the
42:09
public
42:09
and
42:09
giving
42:09
them
42:09
more
42:10
time
42:10
and
42:11
proactively
42:11
trying
42:12
to
42:12
sell
42:12
more
42:12
stock
42:13
within
42:13
the
42:13
store.
42:13
You
42:14
know,
42:14
this
42:14
effectively
42:15
automates
42:15
a
42:16
lot
42:16
of
42:16
them
42:16
standard
42:17
tasks,
42:18
you
42:18
know,
42:18
rather
42:19
than
42:19
walking
42:19
around
42:19
for
42:20
two
42:20
hours
42:20
a
42:20
day
42:20
with
42:20
a
42:21
handheld
42:21
reader.
42:22
Effectively,
42:23
no
42:23
disrespect
42:24
to
42:24
the
42:24
in
42:24
store
42:25
worker
42:25
as
42:25
well,
42:25
you
42:25
know,
42:26
are
42:26
they
42:26
following
42:26
the
42:26
correct
42:27
process?
42:27
Were
42:27
they
42:27
trained
42:28
correctly?
42:28
There's
42:29
a,
42:29
there's
42:29
a
42:29
high
42:29
staff
42:30
turnover,
42:30
you
42:31
know,
42:31
and
42:31
you
42:31
don't
42:31
know
42:31
whether
42:31
the,
42:32
the
42:32
right
42:32
training
42:32
has
42:33
been
42:33
done
42:33
and
42:33
passed
42:34
on
42:34
to
42:34
the
42:34
next
42:34
level.
42:35
So
42:35
by
42:35
having
42:36
this
42:36
continuously
42:37
just
42:37
reading
42:37
from
42:37
the
42:37
ceilings
42:38
and
42:38
delivering
42:39
that
42:39
data,
42:39
they
42:39
know
42:39
they're
42:40
getting
42:40
good
42:40
data.
42:41
It's
42:42
sort
42:42
of
42:42
98,
42:43
99%
42:43
accurate,
42:44
you
42:44
know,
42:45
every
42:45
15
42:46
minutes
42:46
where
42:46
we're
42:47
feeding
42:47
that
42:47
data
42:47
back
42:48
into
42:48
their
42:48
systems.
42:49
So
42:49
if
42:49
you
42:49
think
42:49
of
42:50
click
42:50
and
42:50
collect
42:50
and
42:51
a
42:51
big
42:51
thing,
42:51
you
42:51
know,
42:51
out
42:51
the
42:51
back
42:52
of
42:52
COVID
42:52
like
42:52
I
42:52
say
42:53
you
42:53
buy
42:54
online,
42:54
pick
42:54
up
42:55
in
42:55
store,
42:55
you
42:55
would
42:56
get
42:56
to
42:56
store.
42:56
It's
42:57
like,
42:57
oh,
42:57
we're
42:57
showing
42:57
stock
42:58
but
42:58
we
42:58
don't
42:58
really
42:58
know
42:59
where
42:59
it
42:59
is.
42:59
Now
42:59
they
43:00
where
43:00
it
43:00
is,
43:01
not
43:01
only
43:01
do
43:01
they
43:01
know
43:01
that
43:02
that
43:02
stock
43:02
inventory
43:02
is
43:03
correct,
43:03
but
43:03
also
43:04
you've
43:04
got,
43:04
you
43:05
know,
43:05
the
43:05
real
43:05
time
43:05
location
43:06
system
43:06
that
43:07
the
43:07
GPS
43:07
if
43:08
you
43:08
want
43:08
to
43:08
call
43:08
it
43:08
of
43:09
actually
43:09
where
43:09
it
43:09
is.
43:10
So
43:10
that
43:10
whole
43:11
customer
43:11
experience
43:12
has
43:12
been
43:12
improved.
43:14
You
43:14
know,
43:14
when
43:14
you
43:15
turn
43:15
up
43:15
the,
43:16
the
43:16
item
43:17
is
43:17
there,
43:17
they
43:17
can
43:17
quickly
43:18
then
43:18
purchase
43:18
that
43:18
item
43:19
and
43:19
what
43:19
that
43:19
is
43:19
then
43:19
doing
43:20
is
43:20
driving
43:20
that
43:20
customer
43:21
back
43:21
to
43:21
that
43:22
store,
43:22
back
43:22
to
43:22
that
43:23
retailer.
43:23
Because
43:23
that
43:24
whole
43:24
customer
43:24
experience
43:25
is
43:25
an
43:25
improvement.
43:26
Right.
43:26
And
43:27
it's
43:27
not
43:27
just
43:27
in
43:28
the
43:28
store
43:28
too.
43:28
I
43:28
think
43:29
that's
43:29
the
43:29
other
43:29
part
43:29
is
43:29
that
43:30
this
43:30
is
43:30
also
43:30
in
43:30
the
43:31
back
43:31
of
43:31
house
43:31
too.
43:32
So
43:32
it's
43:32
not
43:32
only
43:33
keeping
43:33
track
43:34
of
43:34
what's
43:34
what
43:35
the
43:35
movement
43:35
of
43:35
products
43:35
is
43:36
on
43:36
your
43:37
store
43:37
floor,
43:38
but
43:38
also
43:38
knowing
43:39
in
43:39
real
43:39
time
43:40
like,
43:40
oh,
43:40
there's
43:41
a
43:41
product
43:41
hiding
43:42
in
43:42
the
43:42
back,
43:42
that
43:42
there's
43:43
a
43:43
whole
43:43
box
43:43
of
43:43
things
43:44
that
43:44
were
43:44
supposed
43:44
to
43:44
be
43:45
on
43:45
special
43:45
for
43:45
Black
43:46
Friday.
43:46
We
43:46
better
43:47
get
43:47
them
43:47
out
43:47
here.
43:47
To
43:48
move
43:48
that
43:49
product
43:49
quickly
43:50
is
43:51
another
43:51
area
43:51
that
43:51
I
43:52
think
43:52
people
43:52
just
43:52
don't
43:53
think
43:53
about,
43:53
they
43:53
think
43:53
about
43:54
the
43:54
front,
43:54
front
43:54
of
43:55
house
43:55
scenario.
43:56
Yeah,
43:56
I
43:56
mean
43:57
the
43:57
guys
43:57
in
43:57
the
43:57
store,
43:58
you
43:58
know,
43:58
relatively,
43:58
you
43:59
know,
43:59
the,
43:59
you
44:00
would
44:00
probably
44:00
say
44:01
that
44:01
they're,
44:01
they're
44:02
understaffed,
44:02
you
44:02
know,
44:02
and
44:03
they're
44:03
running
44:03
around
44:03
and
44:03
chasing
44:04
and
44:04
trying
44:04
to
44:04
deliver
44:04
the
44:05
best
44:05
possible
44:05
service.
44:06
But
44:06
some
44:06
of
44:06
these
44:07
decisions
44:07
could
44:07
be
44:07
made
44:08
at
44:08
sort
44:08
of
44:09
group
44:09
level,
44:09
at
44:09
head
44:10
office
44:10
level.
44:10
They
44:10
can
44:11
now
44:11
see
44:11
that
44:11
stock
44:12
that
44:12
should
44:12
be
44:12
on
44:12
the
44:12
shop
44:13
floor.
44:13
They,
44:14
they
44:14
can
44:14
proactively
44:14
tell
44:15
the
44:15
store
44:15
what
44:15
they're
44:16
missing.
44:16
If
44:16
they
44:16
haven't
44:16
had
44:17
a
44:17
chance
44:17
to
44:17
look
44:17
at
44:18
that
44:18
data
44:18
within
44:18
store,
44:19
you
44:19
know,
44:19
maybe
44:19
the
44:19
shift
44:20
manager
44:20
or
44:20
the
44:20
store
44:21
manager
44:21
hasn't
44:21
had
44:21
a
44:21
chance.
44:22
So
44:23
it's
44:23
allowing
44:23
to
44:24
have
44:24
them
44:24
proactive,
44:25
make
44:25
them
44:25
proactive
44:26
decisions
44:26
right
44:26
across
44:27
the
44:27
business
44:28
and
44:28
understand
44:29
if
44:29
they
44:30
haven't
44:30
sold
44:30
something,
44:31
why
44:31
they
44:31
didn't
44:32
sell
44:32
it.
44:32
You
44:33
know,
44:33
if
44:33
you
44:33
think
44:33
of
44:33
the
44:33
decision
44:34
making
44:34
process,
44:34
let's
44:35
say
44:35
there's
44:35
a
44:35
box
44:35
of,
44:36
of,
44:36
of
44:36
green
44:37
shirts
44:37
like
44:37
you
44:37
say
44:38
should
44:38
have
44:38
been
44:39
out.
44:39
They
44:39
didn't
44:40
sell
44:40
them
44:41
because
44:41
they
44:42
weren't
44:42
actually
44:42
on
44:42
the
44:42
shop
44:43
floor.
44:43
But
44:43
then
44:43
they're
44:44
not
44:44
reordering
44:44
that
44:45
stock.
44:45
They're
44:46
not
44:46
reordering
44:46
it
44:46
because
44:47
they
44:47
never
44:47
sold
44:47
it.
44:47
We
44:48
actually,
44:48
now
44:48
they
44:48
can
44:48
see
44:49
it
44:49
never
44:49
actually
44:49
made
44:50
it
44:50
onto
44:50
the
44:50
shop
44:51
floor.
44:51
Therefore
44:52
the
44:52
data
44:53
and
44:53
the
44:53
information
44:54
that
44:54
they've
44:54
been
44:54
working
44:54
from
44:55
has
44:55
been
44:55
incorrect.
44:56
Now
44:56
they
44:56
can
44:56
actually
44:56
see
44:57
that
44:57
right
44:57
across
44:57
the
44:58
group.
44:58
So
44:58
not
44:59
only
44:59
from
44:59
a
44:59
store
44:59
level,
44:59
but
45:00
you
45:00
know,
45:00
you
45:00
start
45:00
to
45:00
look
45:00
at
45:00
it
45:01
from
45:01
an
45:01
area,
45:02
from
45:02
a
45:02
region
45:03
and
45:04
really
45:04
start
45:04
to
45:04
understand
45:05
what's
45:05
happening.
45:06
Yeah,
45:06
and
45:06
the
45:06
other
45:06
thing
45:07
too
45:07
is
45:07
that
45:07
I
45:07
love
45:08
is
45:08
the
45:08
scenario
45:09
of
45:09
like
45:09
what's
45:09
going
45:10
in
45:10
the
45:10
fitting
45:10
rooms
45:11
and
45:11
what's
45:11
going
45:12
back
45:12
out
45:12
on
45:12
the
45:12
floor.
45:13
Like
45:13
there's
45:13
just
45:13
like
45:13
you
45:14
said,
45:14
there's
45:15
so
45:15
much
45:15
data
45:15
that
45:16
you're
45:16
able
45:16
to
45:16
collect.
45:17
And
45:17
now
45:18
what
45:18
I
45:18
find
45:18
even
45:19
more
45:19
interesting
45:20
too,
45:20
Pete,
45:20
is
45:21
like
45:21
with,
45:21
with
45:22
a
45:22
Trackmaster
45:23
3
45:23
that's
45:24
coming
45:24
out
45:24
3x.
45:25
Like
45:25
what
45:25
does
45:26
that
45:26
do
45:27
as
45:27
far
45:28
as
45:28
like
45:28
the
45:28
types
45:28
of
45:29
stores?
45:29
Because
45:29
I
45:29
imagine
45:30
that
45:30
opens
45:30
up
45:30
the
45:30
opportunity
45:31
then
45:31
for
45:31
maybe
45:32
a
45:32
smaller
45:32
format
45:33
store
45:33
that
45:34
is
45:34
RFID
45:35
tagged
45:35
but
45:35
would
45:35
have
45:35
been
45:36
using
45:36
a
45:36
handheld
45:36
reader
45:37
because
45:37
of
45:37
its
45:37
size.
45:37
Now
45:38
if
45:38
that
45:38
one
45:39
unit
45:39
can
45:39
cover
45:40
the
45:40
entire
45:40
store
45:41
like
45:41
it
45:41
couldn't
45:42
before,
45:43
is
45:43
that
45:43
opening
45:43
up
45:44
more
45:44
types
45:44
of
45:45
retailers
45:45
or
45:45
brands
45:46
that
45:46
you
45:46
can
45:46
work
45:46
with?
45:47
Yeah,
45:47
for
45:47
definite.
45:48
I
45:48
mean
45:48
what
45:48
it
45:49
gives
45:49
us
45:49
is
45:49
if
45:50
you
45:50
think
45:50
of
45:50
the
45:50
smaller
45:51
retail,
45:51
hopefully,
45:52
you
45:52
know,
45:52
we
45:52
could
45:52
use
45:53
just
45:53
one
45:53
or
45:53
two
45:53
readers
45:54
with
45:54
multiple.
45:56
And
45:56
that
45:56
ROI
45:57
is
45:57
there
45:57
for
45:57
them
45:58
guys
45:58
but
45:58
also
45:58
the
45:59
huge
45:59
large
46:00
department
46:00
stores
46:01
that
46:01
once
46:02
upon
46:02
a
46:02
time,
46:02
the
46:02
amount
46:03
of
46:03
hardware
46:03
that
46:03
was
46:04
needed
46:04
just
46:04
push
46:04
that
46:05
ROI
46:05
way
46:05
past
46:06
the
46:06
12,
46:06
18
46:06
months.
46:07
Are
46:07
you
46:07
with
46:08
me?
46:08
So
46:09
now
46:09
we've
46:10
got
46:10
the
46:10
ability
46:10
to
46:11
work
46:11
with
46:11
all
46:11
sizes
46:13
of
46:13
retailers,
46:14
all
46:14
types
46:14
of
46:15
retailers.
46:16
Also
46:17
the
46:17
design
46:17
that
46:17
we've
46:18
worked
46:18
closely
46:18
with,
46:18
like
46:19
I
46:19
say,
46:19
some
46:19
of
46:19
our
46:20
pilot
46:20
customers.
46:21
Urban's
46:21
been
46:21
a
46:21
really
46:21
good
46:21
one.
46:22
Where
46:22
our
46:23
antennas
46:24
now
46:24
are
46:24
flat
46:25
to
46:25
the
46:25
ceiling.
46:26
You
46:26
know,
46:26
they're
46:26
a
46:26
flat
46:27
antenna.
46:28
It's
46:28
in,
46:29
you
46:29
know,
46:29
typically
46:30
in
46:30
this
46:30
world
46:31
of
46:31
overhead,
46:31
it's.
46:32
They're
46:32
quite
46:33
large,
46:33
the
46:33
antennas,
46:34
you
46:34
know,
46:34
and
46:34
the
46:34
brand
46:35
teams
46:35
really
46:35
don't
46:36
like
46:36
that.
46:36
So
46:36
we've
46:37
worked
46:37
hard
46:37
over
46:37
the
46:38
last
46:38
six
46:38
months,
46:38
12
46:38
months,
46:39
with
46:39
some
46:39
of
46:39
these
46:39
brand
46:39
teams
46:40
to
46:40
come
46:40
up
46:40
with
46:40
solutions
46:41
that
46:41
are
46:42
that
46:42
fit
46:42
within
46:42
the
46:43
store.
46:43
So
46:43
you
46:43
don't
46:44
really
46:44
see
46:44
them
46:44
there.
46:44
You
46:45
don't,
46:45
you
46:45
wouldn't
46:45
notice
46:45
them
46:46
as
46:46
a
46:46
client.
46:46
You
46:46
know,
46:47
they
46:47
just
46:47
fit
46:47
with
46:47
the
46:48
rest
46:48
of
46:48
the
46:48
infrastructure.
46:49
Some
46:50
of
46:50
them
46:50
can
46:50
also
46:50
be
46:51
painted
46:51
now
46:52
in
46:52
terms
46:52
of
46:52
the
46:52
colors
46:53
to
46:53
match
46:54
the
46:54
walls
46:54
and
46:55
the
46:55
ceilings
46:56
like
46:56
that.
46:56
So
46:57
we've
46:57
got
46:57
options
46:58
in
46:58
terms
46:58
of
46:58
what
46:58
we
46:59
can
46:59
offer
46:59
them.
46:59
Pete,
47:00
you
47:00
mentioned
47:01
Urban.
47:02
Is
47:02
that
47:03
the
47:03
best
47:03
place
47:03
for
47:04
people
47:04
to
47:04
go
47:04
right
47:04
now
47:05
to
47:05
see
47:05
pervase
47:06
IDs
47:06
tech
47:07
in
47:07
action
47:07
or
47:08
to
47:08
like
47:08
look
47:08
up
47:08
and
47:08
see
47:09
the
47:09
readers?
47:10
Yeah,
47:11
that's
47:11
one
47:11
of
47:11
the
47:11
clients
47:12
where
47:13
they
47:13
would
47:13
be
47:13
able
47:14
to
47:14
see
47:14
them
47:14
in
47:14
multiple
47:15
stores.
47:15
We've
47:16
got
47:16
multiple
47:16
apparel
47:17
retailers
47:17
that
47:17
we're
47:18
rolling
47:18
out
47:18
with
47:18
currently.
47:20
What
47:20
we
47:20
could
47:21
do
47:21
is
47:21
if
47:21
somebody
47:21
wants
47:22
to
47:22
look
47:22
at
47:23
the
47:23
hardware,
47:23
they
47:24
could
47:24
get
47:24
in
47:24
contact
47:24
with
47:25
us
47:26
directly.
47:27
We'd
47:27
be
47:27
happy
47:27
to
47:27
take
47:27
them
47:28
to
47:28
a
47:28
store,
47:29
show
47:29
them
47:29
how
47:29
it's
47:29
working
47:30
in
47:30
the
47:30
real
47:30
world
47:30
environment
47:31
because
47:31
there's
47:32
no
47:32
better
47:33
place
47:33
to
47:34
see
47:34
it
47:34
actually
47:34
working
47:35
in
47:35
conjunction
47:36
with
47:37
one
47:37
of
47:37
the
47:37
partners
47:37
software
47:38
so
47:38
they
47:38
can
47:38
really
47:39
see
47:39
the
47:39
data
47:39
that's
47:40
being
47:40
collected.
47:42
So
47:42
yeah,
47:42
we'd
47:42
be
47:42
more
47:43
than
47:43
happy
47:43
if
47:43
you,
47:43
you
47:44
know,
47:44
guys
47:44
would
47:44
want
47:44
to
47:45
contact
47:45
us
47:45
then,
47:46
then
47:46
we'd
47:46
be
47:46
able
47:46
to
47:47
take
47:47
them
47:47
to
47:47
a
47:47
store
47:47
and
47:48
show
47:48
them
47:48
in
47:48
real
47:48
life.
47:49
Yeah,
47:49
I
47:50
love,
47:50
I
47:50
love
47:50
the,
47:50
I
47:50
love
47:51
the
47:51
anecdote
47:51
you
47:51
shared
47:51
too,
47:51
about
47:52
the
47:52
things
47:52
that
47:52
the,
47:52
the
47:53
types
47:53
of
47:53
questions
47:53
you
47:53
have
47:54
to
47:54
answer
47:54
from
47:54
the
47:54
retailers
47:55
and
47:55
the
47:55
brands
47:55
as,
47:55
you
47:56
know,
47:56
an
47:56
entrepreneur
47:56
or
47:56
a
47:57
tech
47:57
startup.
47:57
Like,
47:57
what
47:57
does
47:57
it
47:58
look
47:58
like
47:58
in
47:58
this
47:58
ceiling?
47:59
Where,
47:59
you
47:59
know,
47:59
from
47:59
my
48:00
perspective
48:00
as
48:00
a
48:00
former
48:00
retail
48:01
executive,
48:01
I'm
48:01
like,
48:01
if
48:02
my
48:02
customers
48:02
are
48:02
looking
48:02
at
48:03
the
48:03
ceiling,
48:03
I
48:03
got
48:03
another
48:04
problem,
48:04
man.
48:04
I
48:05
gotta
48:05
have
48:05
good
48:05
product
48:05
on
48:06
the
48:06
floor
48:06
and
48:06
I
48:06
want
48:06
to
48:06
be
48:06
able
48:07
to
48:07
see
48:07
sell
48:07
it
48:07
like
48:07
first
48:08
and
48:08
foremost.
48:08
Right.
48:08
And
48:09
that's
48:09
what
48:09
I
48:09
hope
48:09
everyone
48:09
hears
48:10
from
48:10
this
48:10
too.
48:10
But,
48:11
but
48:11
anyway,
48:12
you
48:12
mentioned
48:12
it,
48:12
so
48:12
let's
48:13
put
48:13
you
48:13
on
48:13
the
48:13
spot.
48:13
Like
48:14
if
48:14
people
48:14
want
48:14
to
48:14
get
48:14
in
48:14
touch
48:14
with
48:15
you
48:15
when
48:15
they're
48:15
out
48:15
at
48:15
nrf,
48:16
it's
48:17
like
48:17
a
48:17
few
48:17
days
48:17
away.
48:18
If
48:18
people
48:18
want
48:18
to
48:19
get
48:19
in
48:19
touch
48:19
with
48:19
you
48:19
after,
48:19
what's
48:20
the
48:20
best
48:20
way
48:20
for
48:20
them
48:20
to
48:21
do
48:21
that?
48:21
Yeah,
48:21
of
48:21
course.
48:22
So
48:22
we'll
48:22
be
48:22
at
48:22
NRF,
48:23
of
48:23
course.
48:24
A
48:24
Track
48:24
Master
48:25
3
48:25
hardware
48:25
will
48:25
be
48:26
showcased
48:26
on,
48:26
on
48:26
several
48:27
partner
48:27
booths
48:28
such
48:28
as
48:28
Spencer
48:29
Technologies.
48:31
That's,
48:31
that's
48:32
booth
48:32
3257.
48:33
It'll
48:33
be
48:33
on
48:34
the
48:34
gray
48:34
orange
48:34
booth
48:36
and
48:36
then
48:36
one
48:36
or
48:36
two
48:37
others
48:37
beyond
48:37
that.
48:38
Obviously
48:38
we've
48:38
got
48:39
live
48:39
deployments
48:39
that
48:40
we
48:40
would,
48:40
you
48:40
know,
48:40
be
48:41
happy
48:41
to
48:41
show
48:41
customers
48:42
around
48:42
if
48:42
they
48:43
wanted
48:43
to
48:43
see
48:43
that.
48:44
In
48:44
terms
48:44
of
48:45
getting
48:45
in
48:45
contact
48:45
with
48:46
me,
48:47
hit
48:47
me
48:47
up
48:47
on
48:47
LinkedIn.
48:48
I'm
48:48
very
48:48
active
48:48
on
48:48
LinkedIn
48:49
so
48:49
you
48:49
know,
48:49
hit
48:49
me
48:50
up
48:50
on
48:50
LinkedIn
48:51
or
48:51
just
48:52
visit
48:52
our
48:52
website,
48:53
www.perbaseid.com,
48:57
fill
48:57
in
48:57
the
48:57
forms
48:58
and
48:58
we'll
48:58
be
48:58
in
48:58
touch.
48:59
We'll
48:59
get
48:59
in
48:59
touch
49:00
within
49:01
a
49:01
day
49:01
or
49:01
two.
49:02
All
49:02
right,
49:02
great
49:03
stuff,
49:03
Pete.
49:03
Thanks
49:04
so
49:04
much
49:04
for
49:04
joining
49:04
us.
49:04
This
49:05
was
49:05
really
49:05
fun.
49:05
Yeah,
49:05
no,
49:06
thanks
49:06
very
49:06
much
49:06
guys.
49:07
It's
49:07
been
49:07
great.
49:08
Now
49:08
that
49:08
retailers
49:09
have
49:09
full
49:09
visibility
49:10
of
49:10
their
49:10
products
49:10
and
49:11
can
49:11
make
49:11
them
49:11
available
49:12
to
49:12
shoppers,
49:13
the
49:13
next
49:13
thing
49:14
retailers
49:14
need
49:14
to
49:14
do
49:14
is
49:15
make
49:15
sure
49:15
that
49:15
their
49:16
customers
49:16
can
49:16
check
49:16
out
49:17
as
49:17
fast
49:18
and
49:18
as
49:18
conveniently
49:19
as
49:19
is
49:19
humanly
49:20
possible.
49:21
Making
49:21
that
49:21
a
49:22
possibility
49:22
is
49:22
our
49:23
next
49:23
guest,
49:23
Arvind
49:24
Jawa,
49:25
the
49:25
VP
49:25
of
49:26
Global
49:26
strategy
49:27
and
49:27
Marketing
49:28
at
49:28
Digital
49:28
Diebold
49:28
Nixdorf.
49:29
Welcome
49:30
to
49:30
OmniTalk
49:31
Arvin,
49:31
it's
49:31
great
49:31
to
49:31
have
49:32
you
49:32
on.
49:32
Thank
49:33
you
49:33
so
49:33
much,
49:33
Anne.
49:34
I'm
49:34
a
49:34
big
49:35
fan,
49:35
as
49:36
they
49:36
say
49:36
in
49:36
radio.
49:37
I'm
49:37
a
49:37
longtime
49:38
listener,
49:38
but
49:39
first
49:39
time
49:39
caller.
49:40
Yes,
49:40
we're
49:41
so
49:41
happy
49:41
to
49:41
have
49:42
you.
49:42
Thanks
49:42
for
49:42
taking
49:43
the
49:43
time
49:43
out.
49:44
Arvin,
49:45
I'd
49:45
love
49:45
for
49:45
you
49:45
to
49:46
maybe
49:46
start
49:46
for
49:47
our
49:47
listeners
49:47
because
49:48
you're
49:48
a
49:48
first
49:48
time
49:49
caller
49:50
who
49:50
most
49:51
likely
49:51
interacted
49:52
with
49:52
a
49:52
DeWalt
49:53
Nick
49:53
Serve
49:53
product
49:53
before
49:54
but
49:54
might
49:54
not
49:54
be
49:55
familiar
49:56
with
49:56
or
49:56
might
49:56
not
49:56
realize
49:57
that
49:57
they
49:57
have
49:58
share
49:58
what
49:58
you're
49:59
known
49:59
for
49:59
and
50:00
maybe
50:00
some
50:00
things
50:01
that
50:01
they
50:01
might
50:02
not
50:02
know
50:02
about
50:02
your
50:03
approach
50:03
to
50:03
retail
50:04
and
50:04
retail
50:04
technology.
50:05
At
50:05
Diebold
50:06
Nixdorf
50:06
Retail,
50:07
we
50:07
like
50:07
to
50:07
say
50:08
that
50:08
our
50:08
purpose
50:09
in
50:09
life
50:09
is
50:10
to
50:10
transform
50:11
the
50:11
way
50:11
people
50:12
shop.
50:13
And
50:13
as
50:13
a
50:13
leading
50:14
provider
50:14
of
50:15
unified
50:16
retail
50:16
commerce
50:17
platforms,
50:17
a
50:18
globally
50:18
scalable
50:19
services
50:20
partner
50:20
and
50:21
also
50:21
a
50:22
well
50:22
known
50:23
supplier
50:23
of
50:23
world
50:24
class
50:24
technology,
50:25
we
50:26
are
50:26
focused
50:26
on
50:27
improving
50:27
performance
50:28
in
50:28
physical
50:29
Retail.
50:29
We
50:30
serve
50:30
many
50:30
of
50:31
the
50:31
world's
50:31
largest
50:32
and
50:32
most
50:32
recognizable
50:33
brands
50:33
and
50:34
retailers
50:34
in
50:35
general
50:35
merchandise
50:36
and
50:36
grocery,
50:37
hard
50:37
discount,
50:38
fashion,
50:39
specialty
50:39
fuel
50:39
and
50:40
convenience
50:40
and
50:40
qsr.
50:41
Of
50:41
course,
50:42
we
50:42
like
50:43
to
50:43
create
50:43
retail
50:44
environments
50:44
that
50:45
matter.
50:45
We
50:46
provide
50:46
flexible
50:47
checkout
50:47
options
50:48
and
50:48
journeys
50:48
to
50:49
attract
50:49
shoppers
50:50
and
50:50
leverage
50:51
store
50:51
associates
50:52
in
50:52
more
50:52
value
50:52
add
50:53
ways.
50:54
We
50:54
make
50:54
implementing
50:55
and
50:55
integrating
50:56
self
50:56
service
50:57
checkout
50:58
concepts
50:58
easier
50:59
and
50:59
help
51:00
enable
51:00
retailers
51:01
kind
51:01
of
51:02
be
51:02
free
51:02
of
51:02
lock
51:03
in
51:03
to
51:03
a
51:03
single
51:04
vendor
51:04
of
51:04
hardware
51:05
or
51:05
software.
51:06
We
51:06
tackle
51:07
some
51:07
of
51:07
the
51:07
biggest
51:07
pain
51:08
points
51:09
inside
51:09
the
51:10
store
51:10
like
51:11
shrink
51:11
and
51:12
checkout
51:12
friction.
51:13
Leveraging
51:14
sophisticated
51:15
artificial
51:15
intelligence.
51:16
We
51:17
make
51:17
sure
51:17
that
51:18
retailers
51:18
are
51:18
getting
51:19
the
51:19
most
51:19
out
51:19
of
51:20
their
51:20
technology
51:20
investments
51:21
by
51:21
ensuring
51:22
the
51:23
highest
51:23
levels
51:23
of
51:23
system
51:24
availability
51:24
through
51:25
world
51:25
class
51:26
service
51:26
that's
51:27
both
51:27
predictive
51:27
and
51:28
proactive.
51:29
One
51:29
of
51:29
our
51:29
key
51:29
differentiators,
51:30
Ann
51:30
and
51:30
Chris,
51:31
is
51:31
the
51:31
application
51:32
of
51:32
visual
51:32
AI
51:33
at
51:33
the
51:33
point
51:33
of
51:33
interaction.
51:35
That
51:35
combined
51:36
with
51:36
modular
51:36
self
51:37
service
51:37
solutions
51:38
and
51:38
our
51:38
store
51:39
evolution
51:39
advisory
51:40
services
51:41
helps
51:41
us
51:41
reduce
51:42
friction
51:42
and
51:42
loss
51:43
where
51:43
they
51:44
happen
51:44
in
51:44
the
51:44
aisles
51:45
and
51:45
at
51:45
checkout.
51:46
Arvin,
51:46
one
51:46
thing
51:46
that
51:47
you
51:47
just
51:47
mentioned
51:47
that
51:47
I
51:48
want
51:48
to
51:48
dive
51:48
a
51:48
little
51:48
bit
51:49
deeper
51:49
into
51:49
because
51:50
on
51:50
one
51:50
hand
51:51
you're
51:51
helping
51:52
retailers
51:52
by
51:53
providing
51:53
all
51:53
of
51:53
these
51:54
solutions,
51:55
but
51:55
you
51:56
mentioned
51:57
advisory
51:58
services.
51:59
So
51:59
am
52:00
I
52:00
hearing
52:00
this
52:00
right?
52:00
I
52:01
mean,
52:01
are
52:01
you.
52:01
So
52:01
you're
52:02
kind
52:02
of
52:02
a
52:02
consultant
52:03
but
52:03
then
52:03
also
52:04
a
52:04
solutions
52:05
provider.
52:05
Like
52:06
tell
52:06
me
52:06
exactly
52:06
how
52:07
that
52:07
works.
52:07
While
52:08
we
52:08
use
52:08
consultative
52:10
mechanisms,
52:10
I
52:10
wouldn't
52:11
say
52:11
that
52:11
we're
52:11
necessarily
52:12
a
52:12
consultant.
52:13
Okay,
52:14
it,
52:14
it
52:14
is
52:15
though
52:15
where
52:15
we
52:15
think
52:16
we
52:16
really
52:16
stand
52:17
apart.
52:17
We
52:18
don't
52:18
start
52:19
just
52:19
with
52:19
technology,
52:21
we
52:21
start
52:21
first
52:22
with
52:22
our
52:22
customers.
52:23
Why
52:24
are
52:25
you
52:25
fighting
52:25
long
52:26
queues?
52:26
Are
52:26
you
52:27
trying
52:27
to
52:27
reduce,
52:27
shrink?
52:28
Are
52:28
you
52:28
trying
52:29
to
52:29
deal
52:29
with
52:29
staff
52:30
shortages,
52:30
safety
52:31
concerns
52:32
or
52:32
overall
52:32
just
52:33
trying
52:33
to
52:33
create
52:33
a
52:33
better
52:34
shopper
52:34
experience?
52:35
Once
52:36
we
52:36
know
52:36
that,
52:37
why
52:37
we're
52:38
able
52:38
to
52:38
help
52:39
our
52:39
customers
52:40
map
52:40
their
52:40
store
52:41
almost
52:41
like
52:42
a
52:42
living
52:42
organism.
52:43
Traffic
52:44
flows,
52:44
peak
52:45
times,
52:45
shopper
52:46
habits,
52:46
payment
52:47
preferences,
52:48
even
52:48
how
52:49
the
52:49
existing
52:50
checkout
52:50
lanes
52:51
and
52:51
self
52:51
checkouts
52:52
are
52:52
configured.
52:53
Then
52:53
we
52:54
design
52:55
the
52:55
right
52:55
mix
52:56
of
52:56
assisted
52:57
lanes,
52:58
self
52:58
checkout
52:59
lanes
52:59
and
52:59
the
53:00
application
53:00
of
53:00
visual
53:01
artificial
53:01
intelligence
53:02
so
53:03
that
53:03
retailers
53:03
can
53:04
hit
53:04
their
53:04
goals
53:04
fast
53:05
and
53:05
at
53:05
the
53:05
lowest
53:06
total
53:06
cost
53:06
of
53:06
ownership.
53:07
And
53:07
we
53:08
see
53:08
that
53:08
as
53:08
the
53:09
difference.
53:09
We're
53:09
not
53:09
selling
53:10
hardware,
53:11
we
53:11
are
53:11
actually
53:12
engineering
53:12
outcomes.
53:14
Higher
53:14
throughput,
53:15
lower
53:15
shrink,
53:16
happier
53:16
associates.
53:17
And
53:18
we
53:18
prove
53:18
it
53:18
with
53:18
data.
53:19
And
53:19
so
53:20
when
53:20
we're
53:20
at
53:20
nrf,
53:22
our
53:22
customers
53:23
will
53:23
be
53:23
able
53:23
to
53:23
see
53:24
that
53:24
happen
53:25
live
53:26
and
53:26
with
53:27
the
53:27
examples
53:28
of
53:28
projects
53:28
that
53:29
we've
53:29
done
53:29
in
53:29
the
53:29
past.
53:29
So
53:30
we're
53:30
looking
53:31
forward
53:31
to
53:31
showing
53:31
people
53:32
that.
53:32
And
53:32
the
53:33
bottom
53:33
line
53:33
is
53:34
the
53:34
advisory
53:34
services.
53:35
It's
53:36
not
53:36
Just
53:36
an
53:36
add
53:36
on.
53:37
It's
53:37
the
53:37
foundation
53:38
of
53:38
how
53:38
we
53:39
make
53:39
technology
53:40
work
53:40
and
53:40
how
53:41
our
53:41
customers
53:42
make
53:43
it
53:43
payback.
53:43
Well
53:44
everyone,
53:44
one
53:44
of
53:44
the
53:44
other
53:44
things
53:45
that
53:45
you
53:45
mentioned
53:45
at
53:45
the
53:45
outset,
53:46
you
53:46
mentioned
53:46
computer
53:46
vision
53:47
AI
53:47
and
53:48
you
53:48
know
53:48
we've
53:48
seen
53:49
a
53:49
lot
53:49
happen
53:49
with
53:50
AI
53:50
in
53:50
retail.
53:50
That's
53:51
probably
53:51
the
53:51
biggest
53:51
understatement
53:52
of
53:52
the
53:52
year
53:52
actually.
53:52
I
53:53
can't
53:53
even
53:53
put
53:53
my
53:54
finger
53:54
on
53:54
how
53:54
much
53:54
we've
53:54
seen
53:55
with
53:55
AI
53:55
this
53:55
year
53:55
particularly.
53:56
But
53:56
also,
53:57
you
53:57
know,
53:57
we've
53:57
seen
53:58
a
53:58
lot
53:58
in
53:58
AI
53:59
in
53:59
terms
53:59
of
53:59
how
54:00
checkout
54:01
and
54:01
self
54:01
checkout
54:02
particularly
54:02
is
54:03
evolving
54:03
over
54:03
the
54:04
last
54:04
few
54:04
years.
54:05
And
54:05
if
54:05
I
54:05
can
54:06
recall,
54:06
you
54:06
all
54:07
were
54:07
one
54:07
of
54:07
the
54:07
first
54:07
to
54:07
introduce
54:08
a
54:08
real
54:09
live
54:09
referenceable
54:10
retailer
54:11
using
54:11
AI
54:11
at
54:12
checkout.
54:12
So
54:13
what's
54:13
different
54:13
about
54:14
your
54:14
approach
54:14
to
54:15
visual
54:15
AI
54:15
compared
54:16
to
54:16
the
54:17
computer
54:17
vision
54:17
retailers
54:18
we
54:18
saw
54:19
a
54:19
couple
54:19
years
54:19
ago
54:20
that
54:20
were
54:20
kind
54:20
of
54:20
the
54:20
talk
54:20
of
54:20
the
54:21
town
54:21
back
54:21
then.
54:22
The
54:22
old
54:23
way
54:23
of
54:23
doing
54:24
things
54:24
with
54:24
computer
54:24
vision
54:25
think
54:26
spot
54:26
checks
54:27
and
54:27
maybe
54:28
after
54:28
the
54:28
fact
54:29
alerts.
54:30
Today
54:31
DN's
54:32
approach
54:32
to
54:32
visual
54:33
AI
54:33
is
54:34
like
54:34
having
54:34
a
54:35
digital
54:35
brain
54:36
right
54:36
at
54:37
the
54:37
checkout
54:37
and
54:38
in
54:38
the
54:38
aisles,
54:39
watching,
54:40
learning
54:40
and
54:41
acting
54:41
in
54:41
real
54:42
time,
54:42
not
54:43
just
54:43
reviewing
54:43
footage
54:44
later.
54:44
And
54:45
so
54:45
I
54:45
think
54:45
that's
54:45
what's
54:46
different.
54:46
We've
54:47
trained
54:47
our
54:47
AI
54:48
on
54:48
real
54:49
retail
54:49
behaviors.
54:50
Multi
54:51
item
54:51
baskets
54:52
produce
54:53
without
54:53
barcodes
54:54
and
54:54
even
54:55
subtle
54:56
shopper
54:56
gestures.
54:58
It's
54:58
not
54:58
just
54:59
about
54:59
identifying
55:00
shrink
55:01
after
55:01
it's
55:01
occurred.
55:02
It's
55:02
about
55:03
using
55:04
nudging
55:04
to
55:05
help
55:05
guide
55:05
shoppers
55:06
to
55:06
self
55:07
rectify
55:07
when
55:08
a
55:08
missed
55:08
scan
55:08
is
55:09
detected
55:10
and
55:11
not
55:11
assuming
55:11
the
55:12
worst
55:12
from
55:12
the
55:12
get
55:13
go
55:13
with
55:13
the
55:13
shopper.
55:14
You
55:14
know,
55:14
I
55:15
think
55:15
there
55:15
was
55:15
a
55:15
study
55:15
back
55:16
in
55:16
December
55:18
from
55:18
LendingTree.
55:19
They
55:19
said
55:20
27%
55:21
of
55:21
consumers
55:22
surveyed
55:23
admitted
55:23
to
55:23
taking
55:24
an
55:24
item
55:24
at
55:25
the
55:25
self
55:25
checkout
55:25
without
55:26
paying.
55:27
What
55:27
if
55:28
we
55:28
nudged
55:29
a
55:29
shopper
55:30
when
55:30
we
55:30
detected
55:31
an
55:31
anomaly
55:32
and
55:32
not
55:32
assume
55:33
that
55:33
theft
55:33
is
55:34
actually
55:34
afoot?
55:35
Our
55:35
data
55:36
shows
55:36
that
55:37
80%
55:37
of
55:37
the
55:38
time
55:38
a
55:38
customer
55:39
will
55:39
self
55:40
rectify
55:41
once
55:41
they're
55:42
prompted
55:42
or
55:42
nudged.
55:43
So
55:43
I
55:44
mean
55:44
I
55:44
think
55:44
it's
55:45
big
55:45
difference
55:46
in
55:46
where
55:46
things
55:47
were
55:47
before
55:47
and
55:47
where
55:48
they're
55:48
going.
55:48
Another
55:49
example
55:49
is
55:50
age
55:50
restricted
55:51
purchases.
55:52
Facial
55:52
detection
55:53
automatically
55:54
validate
55:54
the
55:54
age
55:54
of
55:55
a
55:55
purchaser.
55:55
No
55:56
more
55:56
awkward
55:57
waiting
55:57
for
55:57
a
55:57
staff
55:58
member
55:58
to
55:59
have
55:59
your
55:59
ID
55:59
checked
56:00
or
56:00
validated
56:01
when
56:01
you're
56:01
buying
56:01
those
56:02
favorite
56:02
beverages
56:03
we
56:03
like
56:03
to
56:03
buy
56:05
or
56:05
you
56:06
know,
56:06
like
56:06
with
56:06
one
56:06
of
56:06
our
56:06
customers.
56:07
Inter
56:07
Marche
56:08
in
56:08
France.
56:08
Error
56:09
rates
56:10
for
56:10
anomaly
56:11
transactions,
56:12
they
56:13
dropped
56:13
by
56:13
over
56:14
two
56:14
thirds
56:14
when
56:15
we
56:15
deployed
56:16
the
56:16
visual
56:16
AI
56:16
we
56:17
have.
56:17
And
56:17
interventions
56:18
by
56:18
members
56:19
of
56:19
staff
56:20
drop
56:20
significantly
56:21
as
56:21
well.
56:21
And
56:21
so
56:22
that
56:22
means
56:22
associates
56:23
spend
56:24
more
56:24
time
56:24
helping
56:25
and
56:25
less
56:25
time
56:26
policing.
56:27
Another
56:27
example,
56:28
Chris,
56:28
is
56:28
safety.
56:29
Our
56:29
AI
56:30
can
56:30
spot
56:31
a
56:31
spill
56:32
before
56:32
someone
56:32
slips
56:33
or
56:33
recognize
56:34
a
56:34
gesture
56:35
like
56:35
hey,
56:35
my
56:36
hands
56:36
are
56:36
up
56:37
in
56:37
dangerous
56:37
situations.
56:38
I
56:38
love
56:39
the
56:39
word
56:39
nudging
56:39
too
56:40
because
56:40
like
56:40
this
56:40
actually
56:41
just
56:41
happened
56:41
to
56:41
me,
56:41
Arvin.
56:42
Like
56:42
literally
56:42
I
56:42
was,
56:43
I
56:43
was
56:43
at
56:43
the
56:43
grocery
56:44
store
56:44
and
56:44
I
56:45
picked
56:45
up
56:45
some
56:45
bananas
56:46
and
56:46
I
56:46
thought
56:46
the
56:47
bananas
56:47
were
56:47
regular
56:47
bananas.
56:48
So
56:48
you
56:48
know,
56:48
I
56:49
know
56:49
the
56:49
PLU
56:49
number
56:50
for
56:50
regular
56:50
bananas.
56:51
I
56:51
think
56:51
it's
56:51
4
56:52
011.
56:52
And
56:53
I
56:53
plugged
56:53
that
56:53
in.
56:54
But
56:54
I
56:54
accidentally
56:54
picked
56:54
up
56:54
the
56:55
organic
56:55
bananas
56:56
and
56:56
I
56:56
got
56:57
nudged
56:57
that,
56:57
hey,
56:58
if
56:58
you
56:58
walk
56:58
out
56:58
with
56:59
this,
56:59
you're
56:59
stealing.
57:00
And
57:00
the
57:01
computer
57:01
vision
57:01
was
57:01
what
57:02
captured
57:02
me
57:03
doing
57:03
that.
57:03
And
57:03
so
57:04
I
57:04
corrected
57:04
my
57:04
behavior.
57:05
And
57:05
that's
57:05
essentially
57:05
what
57:06
you're
57:06
talking
57:06
about.
57:06
Absolutely
57:07
right.
57:07
And
57:08
we
57:08
see
57:08
that
57:09
as
57:09
such
57:09
a
57:09
big
57:10
beneficial
57:10
aspect
57:11
of
57:11
this.
57:11
It's
57:12
a,
57:12
we're
57:12
not
57:12
assuming
57:13
that
57:13
the
57:13
consumer
57:14
is
57:15
intending
57:16
to
57:16
steal
57:16
because
57:17
there
57:17
is
57:18
shrink
57:19
that's
57:19
non
57:19
malicious.
57:20
And
57:20
then
57:20
the
57:20
second
57:21
thing
57:21
is
57:21
we,
57:21
we
57:22
enable
57:22
this
57:23
to
57:23
help
57:23
a
57:23
retailer
57:24
not
57:24
have
57:24
to
57:25
create
57:25
a
57:26
negative
57:26
environment
57:27
with
57:27
a
57:28
store
57:28
associate
57:29
assuming
57:30
that
57:30
the
57:30
consumer
57:31
that
57:31
you
57:31
were
57:32
trying
57:32
to
57:32
steal
57:33
organic
57:33
bananas
57:34
instead
57:34
of
57:35
regular
57:35
bananas.
57:36
So
57:36
we
57:37
see
57:37
it
57:37
as
57:37
really
57:37
powerful.
57:38
Chris
57:38
Arvind,
57:39
I
57:39
think
57:39
you
57:39
gave
57:40
some
57:40
great
57:40
examples
57:41
there
57:41
and
57:41
it
57:41
certainly
57:42
all
57:42
makes
57:43
sense
57:43
from
57:43
a
57:44
retailer's
57:44
perspective
57:45
why
57:45
they
57:45
would
57:46
want
57:46
to
57:46
deploy
57:46
something
57:47
like
57:47
what
57:48
you're
57:48
talking
57:48
about.
57:48
But
57:49
I
57:49
mean
57:49
can
57:50
you
57:50
share
57:50
some
57:50
hard
57:50
numbers
57:51
from
57:51
some
57:51
of
57:51
these
57:51
deployments?
57:52
I
57:53
know
57:53
that
57:53
that's
57:53
going
57:53
to
57:54
be
57:54
the
57:54
next
57:54
question
57:55
and
57:55
follow
57:55
up
57:55
that
57:56
we
57:56
get
57:56
and
57:57
that
57:57
I'm
57:57
sure
57:57
you
57:57
get
57:58
when
57:58
you
57:58
have
57:58
retailers
57:59
stopping
57:59
by
57:59
your
57:59
booth
57:59
at
57:59
nrf.
58:00
But
58:00
what's
58:01
showing
58:02
shrink
58:02
reduction
58:03
and
58:03
shorter
58:04
cues?
58:04
What
58:04
kind
58:04
of
58:05
data
58:05
do
58:05
you
58:05
have
58:05
to
58:05
send
58:06
support
58:06
that?
58:06
Yeah,
58:07
yeah,
58:07
sure.
58:07
And
58:07
of
58:08
course
58:08
we
58:08
have
58:09
to
58:09
get
58:09
specific
58:09
because
58:10
we
58:10
all
58:10
know
58:10
in
58:10
retail
58:11
results
58:11
are
58:11
what
58:11
matter.
58:12
Right?
58:12
So
58:13
we
58:14
have
58:14
a
58:14
customer
58:14
in
58:15
Germany
58:15
called
58:15
Etica.
58:16
Etika
58:17
Jager
58:18
is
58:18
the
58:18
location
58:18
and
58:19
we,
58:19
we
58:19
worked
58:20
with
58:20
them
58:20
on
58:20
their
58:20
initial
58:21
pilot
58:22
and
58:22
our
58:22
Visual
58:23
AI
58:23
now
58:24
approves
58:25
over
58:25
80%
58:26
of
58:27
transactions
58:28
with
58:28
age
58:28
restricted
58:29
products.
58:30
And
58:30
they're
58:31
done
58:31
instantly
58:32
at
58:32
self
58:32
checkout.
58:33
So
58:33
this
58:33
means
58:34
that
58:34
shoppers
58:34
really
58:35
breeze
58:35
through
58:36
the
58:36
self
58:36
checkout.
58:37
No
58:37
more
58:38
awkward
58:38
waiting
58:38
for
58:39
a
58:39
staff
58:39
member
58:40
and
58:40
associates
58:41
that
58:41
are
58:41
freed
58:42
up
58:42
to
58:42
help
58:42
not
58:43
just
58:43
check
58:44
IDs.
58:44
Another
58:45
example
58:45
is
58:46
in
58:46
France
58:46
with
58:47
a
58:47
very
58:47
large
58:47
grocer
58:48
called
58:48
Inter
58:49
Marche.
58:50
We
58:50
saw
58:51
their
58:51
transaction
58:51
anomalies,
58:52
both
58:52
accidental
58:53
miss
58:53
scans
58:54
or
58:54
intentional
58:55
theft,
58:56
drop
58:56
from
58:57
what
58:57
they
58:57
had
58:57
calculated
58:58
as
58:58
3%
58:59
of
58:59
all
58:59
transactions
59:00
to
59:00
less
59:01
than
59:01
1%
59:01
after
59:02
deploying
59:02
our
59:03
solution
59:04
and
59:04
maybe
59:05
to
59:05
put
59:05
a
59:06
little
59:06
bit
59:06
more
59:07
tangibility
59:07
around
59:08
it,
59:08
you
59:08
know,
59:09
when
59:09
we
59:09
take
59:09
the
59:09
compendium
59:10
of
59:10
results
59:10
from
59:11
our
59:11
numerous
59:12
customer
59:12
studies
59:13
in
59:14
a
59:14
typical
59:14
grocery
59:15
store
59:15
with
59:15
say
59:16
six
59:16
self
59:17
checkouts
59:17
maybe
59:18
doing
59:18
$10
59:18
million
59:19
in
59:20
annual
59:20
revenue
59:21
and
59:21
we'll
59:22
assume
59:22
that
59:22
maybe
59:22
they
59:23
have
59:23
about
59:23
two
59:23
and
59:23
a
59:24
half
59:24
percent
59:24
shrink,
59:24
which
59:25
in
59:25
some
59:26
respects
59:26
may
59:26
seem
59:27
low,
59:27
in
59:27
others
59:27
it
59:28
may
59:28
seem
59:28
high.
59:29
We
59:29
know
59:30
that
59:30
with
59:30
our
59:31
Vynamic
59:31
Smart
59:31
Vision
59:32
AI
59:32
solution
59:33
we
59:33
can
59:34
impact
59:34
70%
59:36
of
59:36
the
59:36
shrink
59:37
inducing
59:38
transactions
59:39
occurring
59:39
at
59:39
the
59:39
checkout
59:41
and
59:41
that
59:41
can
59:41
turn
59:42
into
59:42
over
59:43
$100,000
59:45
annually
59:46
in
59:46
savings
59:46
a
59:47
year.
59:47
And
59:48
that's
59:48
profit,
59:49
that's
59:49
real
59:49
profit.
59:50
That's
59:50
significant.
59:51
And
59:51
we're
59:51
super
59:51
excited
59:52
to
59:52
have,
59:52
we
59:53
have
59:53
about
59:53
100
59:53
retailers
59:55
in
59:55
some
59:55
stage
59:56
between
59:57
pilot
59:57
and
59:58
rollout
59:59
with
59:59
this
59:59
solution.
1:00:00
So
1:00:00
more
1:00:00
results
1:00:01
and
1:00:01
benefit
1:00:01
to
1:00:01
come.
1:00:02
Well,
1:00:02
I
1:00:02
want
1:00:03
to
1:00:03
talk
1:00:03
about
1:00:03
that
1:00:03
then
1:00:03
Arvind,
1:00:04
because
1:00:05
I
1:00:05
think
1:00:05
those
1:00:05
examples
1:00:06
are
1:00:06
great
1:00:06
too.
1:00:06
Like
1:00:06
the
1:00:06
Germany
1:00:07
example
1:00:07
is
1:00:08
pretty
1:00:10
pressing
1:00:10
for
1:00:10
me
1:00:10
in
1:00:11
my
1:00:11
head
1:00:11
because
1:00:11
to
1:00:11
do
1:00:12
that
1:00:12
in
1:00:12
Germany
1:00:12
where
1:00:12
the
1:00:13
standards
1:00:13
are
1:00:13
a
1:00:13
lot
1:00:13
higher
1:00:14
is
1:00:14
pretty
1:00:15
impressive.
1:00:16
To
1:00:17
your
1:00:17
last
1:00:17
point
1:00:17
that
1:00:17
you
1:00:18
raised,
1:00:19
I'm
1:00:19
curious
1:00:19
as
1:00:19
you
1:00:20
look
1:00:20
more
1:00:20
broadly
1:00:20
than
1:00:21
across
1:00:21
the
1:00:21
store
1:00:21
operation
1:00:22
and
1:00:22
you
1:00:22
think
1:00:22
about
1:00:23
unified
1:00:23
commerce
1:00:24
platforms
1:00:24
or
1:00:25
maybe
1:00:25
even
1:00:26
take
1:00:26
like
1:00:26
something
1:00:27
specific
1:00:27
like
1:00:28
electronic
1:00:29
shelf
1:00:29
labels
1:00:29
throughout
1:00:30
the
1:00:30
store,
1:00:31
how
1:00:31
do
1:00:31
your
1:00:31
solutions
1:00:32
fit
1:00:32
in
1:00:33
with
1:00:33
those
1:00:33
broader
1:00:34
solutions?
1:00:35
Yeah,
1:00:36
no,
1:00:36
that's
1:00:36
a
1:00:36
great
1:00:37
question,
1:00:37
Chris.
1:00:38
We
1:00:39
like
1:00:39
to
1:00:39
slot
1:00:40
in
1:00:40
at
1:00:40
the
1:00:40
point
1:00:40
of
1:00:40
interaction,
1:00:41
you
1:00:41
know,
1:00:41
where
1:00:42
we
1:00:42
see
1:00:42
shrink
1:00:43
and
1:00:43
checkout
1:00:44
friction
1:00:44
actually
1:00:45
occurring.
1:00:45
And
1:00:45
our
1:00:45
smart
1:00:46
vision
1:00:47
and
1:00:47
our
1:00:47
self
1:00:47
service
1:00:47
solutions
1:00:48
complement
1:00:49
unified
1:00:49
commerce
1:00:50
stacks
1:00:50
and
1:00:51
electronic
1:00:51
shelf
1:00:52
edge
1:00:52
labels
1:00:53
by
1:00:53
catching
1:00:54
fraud
1:00:54
behaviors,
1:00:55
automating
1:00:56
age
1:00:56
verification,
1:00:57
identifying
1:00:58
produce
1:00:58
right
1:00:59
as
1:00:59
the
1:00:59
items
1:01:00
are
1:01:00
being
1:01:00
rung.
1:01:01
But
1:01:01
because
1:01:01
our
1:01:02
approach
1:01:02
is
1:01:02
very
1:01:03
open
1:01:03
and
1:01:03
modular,
1:01:04
we
1:01:04
feed
1:01:05
trusted
1:01:05
stored
1:01:06
data
1:01:06
back
1:01:06
into
1:01:07
enterprise
1:01:07
platforms
1:01:08
without
1:01:09
really
1:01:09
forcing
1:01:09
retailers
1:01:10
to
1:01:10
do
1:01:10
a
1:01:10
rip
1:01:11
and
1:01:11
replace
1:01:11
of
1:01:11
their
1:01:12
core
1:01:12
systems
1:01:13
or
1:01:13
investments
1:01:13
that
1:01:14
they've
1:01:14
already
1:01:14
made
1:01:14
in
1:01:15
technology.
1:01:15
So
1:01:15
the
1:01:16
net
1:01:16
result
1:01:16
is
1:01:16
you
1:01:17
protect
1:01:17
current
1:01:18
investments
1:01:18
as
1:01:18
a
1:01:19
retailer
1:01:19
while
1:01:20
finding
1:01:20
and
1:01:21
adding
1:01:21
front
1:01:22
of
1:01:22
store
1:01:22
performance
1:01:23
gains
1:01:23
that
1:01:24
upstream
1:01:24
systems
1:01:25
alone
1:01:25
can't
1:01:26
deliver.
1:01:26
So
1:01:27
we
1:01:28
feel
1:01:28
that
1:01:28
we're
1:01:29
like
1:01:29
part
1:01:29
of
1:01:29
an
1:01:29
ecosystem.
1:01:30
Right?
1:01:31
And
1:01:31
that
1:01:31
ecosystem
1:01:32
approach
1:01:32
is
1:01:33
what
1:01:33
we
1:01:33
believe
1:01:33
retailers
1:01:34
truly
1:01:35
need
1:01:35
to
1:01:35
achieve
1:01:36
that
1:01:36
maturity.
1:01:37
Towards
1:01:38
a
1:01:38
vision
1:01:38
of
1:01:38
intelligent
1:01:39
retail
1:01:39
stores.
1:01:40
We
1:01:41
believe
1:01:41
that
1:01:41
intelligent
1:01:42
retail
1:01:42
will
1:01:42
shape
1:01:43
the
1:01:43
in
1:01:43
store
1:01:43
experience
1:01:44
for
1:01:44
shoppers
1:01:44
and
1:01:44
associates
1:01:45
in
1:01:45
the
1:01:45
future
1:01:45
and
1:01:46
we
1:01:46
are
1:01:46
just
1:01:46
being
1:01:46
a
1:01:47
part
1:01:47
of
1:01:47
it.
1:01:47
Arvin,
1:01:48
you
1:01:48
mentioned
1:01:48
intelligent
1:01:49
retail
1:01:49
stores.
1:01:50
I
1:01:50
know
1:01:50
that's
1:01:50
going
1:01:50
to
1:01:50
be
1:01:51
a
1:01:51
big
1:01:51
topic
1:01:51
of
1:01:52
conversation
1:01:52
among
1:01:53
the
1:01:53
retailers
1:01:54
out
1:01:55
wandering
1:01:56
the
1:01:56
floor
1:01:56
at
1:01:56
nrf.
1:01:58
If
1:01:58
you
1:01:58
had
1:01:58
to
1:01:59
give
1:01:59
like
1:01:59
your
1:02:00
elevator
1:02:00
pitch
1:02:01
of
1:02:01
what
1:02:02
intelligent
1:02:02
retail
1:02:03
stores
1:02:03
look
1:02:03
like,
1:02:04
what
1:02:04
would
1:02:04
that
1:02:04
be?
1:02:06
I
1:02:06
love
1:02:06
that
1:02:06
question.
1:02:08
And
1:02:09
picture
1:02:09
this.
1:02:10
You
1:02:10
walk
1:02:10
into
1:02:11
a
1:02:11
store
1:02:11
that
1:02:12
doesn't
1:02:13
just
1:02:14
react,
1:02:14
but
1:02:14
it
1:02:15
anticipates.
1:02:16
To
1:02:17
us,
1:02:18
an
1:02:18
intelligent
1:02:19
retail
1:02:19
store
1:02:20
is
1:02:20
a
1:02:20
physical
1:02:21
space
1:02:21
with
1:02:21
a
1:02:22
digital
1:02:22
brain.
1:02:23
It's
1:02:23
powered
1:02:24
by
1:02:25
AI,
1:02:26
computer
1:02:26
vision,
1:02:27
IoT,
1:02:28
maybe
1:02:29
even
1:02:29
RFID.
1:02:30
But
1:02:30
what
1:02:31
matters
1:02:31
is
1:02:32
what
1:02:32
it
1:02:32
does
1:02:32
for
1:02:33
people.
1:02:33
The
1:02:34
store
1:02:35
more
1:02:35
or
1:02:35
less
1:02:35
sees
1:02:36
what's
1:02:36
happening
1:02:36
on
1:02:37
shelves,
1:02:37
at
1:02:38
checkout
1:02:38
and
1:02:39
in
1:02:39
the
1:02:39
aisles.
1:02:40
And
1:02:40
then
1:02:40
it
1:02:41
acts
1:02:42
instantly
1:02:43
to
1:02:43
help
1:02:43
fix
1:02:44
problems
1:02:44
before
1:02:45
they
1:02:45
become
1:02:45
pain
1:02:46
points.
1:02:47
Imagine
1:02:48
a
1:02:48
long
1:02:48
line
1:02:48
forming
1:02:49
itself.
1:02:49
Checkout.
1:02:50
The
1:02:51
system
1:02:51
predicts
1:02:51
it
1:02:52
and
1:02:52
opens
1:02:53
another
1:02:53
lane
1:02:53
before
1:02:54
frustration
1:02:55
builds.
1:02:55
With
1:02:55
the
1:02:55
consumers
1:02:57
produce
1:02:58
bins
1:02:58
running
1:02:59
low,
1:02:59
the
1:02:59
store
1:03:00
knows
1:03:00
and
1:03:00
prompts
1:03:01
a
1:03:01
refill.
1:03:02
A
1:03:02
safety
1:03:03
risk
1:03:03
appears,
1:03:04
and
1:03:04
our
1:03:04
AI
1:03:05
solution
1:03:05
spots
1:03:06
it
1:03:06
and
1:03:06
alerts
1:03:06
staff
1:03:07
in
1:03:07
real
1:03:07
time.
1:03:08
I
1:03:08
think
1:03:08
the
1:03:09
magic
1:03:09
of
1:03:09
an
1:03:10
intelligent
1:03:10
retail
1:03:11
store
1:03:11
is
1:03:11
in
1:03:11
how
1:03:12
seamlessly
1:03:12
it
1:03:13
will
1:03:13
all
1:03:13
work
1:03:13
together
1:03:14
with
1:03:14
existing
1:03:14
systems.
1:03:15
As
1:03:15
I
1:03:15
mentioned
1:03:16
before,
1:03:16
not
1:03:17
ripping
1:03:17
and
1:03:17
replacing
1:03:18
out,
1:03:18
but
1:03:19
just
1:03:19
smarter,
1:03:19
faster,
1:03:20
more
1:03:20
proactive
1:03:21
retail.
1:03:22
Right.
1:03:22
We
1:03:22
shouldn't
1:03:23
notice
1:03:23
it.
1:03:23
Yeah,
1:03:24
exactly,
1:03:24
exactly.
1:03:25
It's
1:03:25
not
1:03:26
just
1:03:26
about
1:03:26
the
1:03:26
tech.
1:03:27
The
1:03:27
tech
1:03:27
should
1:03:27
be
1:03:28
in
1:03:28
the
1:03:28
background.
1:03:28
It's
1:03:28
really
1:03:29
about
1:03:29
delivering
1:03:29
the
1:03:30
kind
1:03:30
of
1:03:30
personalized,
1:03:31
frictionless
1:03:31
experiences
1:03:32
that
1:03:32
I
1:03:32
think
1:03:32
shoppers
1:03:33
expect
1:03:33
today
1:03:34
while
1:03:34
making
1:03:35
life
1:03:35
easier
1:03:35
for
1:03:36
associates.
1:03:36
And
1:03:37
we'd
1:03:37
love
1:03:37
to
1:03:38
show
1:03:38
that
1:03:38
to
1:03:38
everybody.
1:03:39
At
1:03:39
nrf,
1:03:40
you
1:03:40
won't
1:03:41
just
1:03:41
hear
1:03:41
about
1:03:41
intelligence
1:03:42
stores.
1:03:42
We'll
1:03:42
actually
1:03:43
show
1:03:43
it
1:03:43
to
1:03:44
our
1:03:44
prospects
1:03:45
and
1:03:45
customers
1:03:45
and
1:03:46
visitors
1:03:46
to
1:03:46
the
1:03:46
booth
1:03:47
in
1:03:47
action
1:03:47
and
1:03:47
they'll
1:03:48
get
1:03:48
to,
1:03:48
you
1:03:49
know,
1:03:49
see,
1:03:49
decide,
1:03:50
act
1:03:51
on
1:03:52
their
1:03:52
own
1:03:52
live
1:03:52
at
1:03:52
our
1:03:53
booth.
1:03:53
I
1:03:53
think,
1:03:54
I
1:03:54
think
1:03:54
your
1:03:54
booth
1:03:54
is
1:03:55
one
1:03:55
of
1:03:55
the
1:03:55
must
1:03:55
stops
1:03:56
every
1:03:56
year.
1:03:56
Like
1:03:56
I,
1:03:56
I
1:03:57
personally
1:03:57
am
1:03:57
a
1:03:57
massive
1:03:57
retail
1:03:58
geek
1:03:58
and
1:03:58
I
1:03:58
love
1:03:58
seeing
1:03:59
all
1:03:59
the
1:03:59
evolution,
1:03:59
particularly
1:04:00
in
1:04:00
self
1:04:00
checkout
1:04:01
and
1:04:01
even,
1:04:01
you
1:04:02
know,
1:04:02
all
1:04:02
the
1:04:02
forms
1:04:03
of
1:04:03
self
1:04:03
checkout
1:04:04
that
1:04:04
that
1:04:04
whole
1:04:04
idea
1:04:05
and
1:04:05
concept
1:04:05
can
1:04:05
take,
1:04:06
that
1:04:06
you
1:04:06
guys
1:04:06
are
1:04:06
always
1:04:06
on
1:04:07
the
1:04:07
cutting
1:04:07
edge
1:04:07
of
1:04:07
trying
1:04:07
to
1:04:08
design
1:04:08
for
1:04:08
the
1:04:09
future.
1:04:09
And
1:04:10
so
1:04:10
like,
1:04:10
I'm
1:04:10
definitely
1:04:10
going
1:04:11
to
1:04:11
come
1:04:11
by
1:04:11
the
1:04:11
booth.
1:04:11
I'm
1:04:12
curious,
1:04:12
like,
1:04:12
what
1:04:13
else
1:04:13
can
1:04:13
we
1:04:13
expect
1:04:13
from
1:04:14
you
1:04:14
all
1:04:14
at
1:04:14
nrf?
1:04:15
It's
1:04:15
only
1:04:15
a
1:04:15
few
1:04:16
days
1:04:16
away.
1:04:16
And
1:04:17
where
1:04:17
is
1:04:17
your
1:04:17
booth?
1:04:18
3522
1:04:19
Chris.
1:04:20
And
1:04:20
we're
1:04:21
glad
1:04:21
that
1:04:21
you
1:04:21
are
1:04:22
curious.
1:04:22
That's
1:04:22
what
1:04:22
we
1:04:23
want
1:04:23
you
1:04:23
to
1:04:23
bring.
1:04:23
Bring
1:04:24
your
1:04:24
curiosity.
1:04:25
There's
1:04:25
so
1:04:25
much
1:04:26
potential
1:04:26
in
1:04:26
retail.
1:04:27
Right.
1:04:27
And
1:04:27
at
1:04:28
nrf,
1:04:29
we
1:04:29
will
1:04:30
be
1:04:30
inviting
1:04:30
you
1:04:30
to
1:04:30
step
1:04:31
into
1:04:31
the
1:04:31
future
1:04:32
of
1:04:32
retail.
1:04:32
And
1:04:33
our
1:04:33
booth
1:04:33
is
1:04:33
designed
1:04:34
as
1:04:34
a
1:04:35
journey,
1:04:36
an
1:04:36
insight
1:04:36
zone
1:04:37
is
1:04:37
where
1:04:37
we'll
1:04:37
start
1:04:38
people
1:04:38
and
1:04:39
you'll
1:04:39
see
1:04:39
real
1:04:39
data
1:04:40
from
1:04:40
live
1:04:40
stores
1:04:41
and
1:04:41
test
1:04:41
drive
1:04:41
our
1:04:42
store
1:04:42
evolution
1:04:43
advisory
1:04:43
services.
1:04:44
I
1:04:44
mentioned
1:04:44
before,
1:04:45
they're
1:04:45
not
1:04:45
Just
1:04:46
dashboards.
1:04:46
It's
1:04:47
really
1:04:47
a
1:04:47
hands
1:04:47
on,
1:04:48
kind
1:04:48
of
1:04:48
interactive
1:04:48
and
1:04:49
tailored
1:04:50
to
1:04:50
the
1:04:50
challenges
1:04:51
that
1:04:51
we
1:04:51
know
1:04:51
retailers
1:04:52
care
1:04:52
most
1:04:52
about.
1:04:53
And
1:04:53
then
1:04:53
we'll
1:04:54
have
1:04:54
them
1:04:54
step
1:04:54
into
1:04:55
what
1:04:55
we
1:04:55
call
1:04:55
the
1:04:55
impact
1:04:56
zone
1:04:56
where
1:04:56
we
1:04:56
showcase
1:04:57
real
1:04:58
customer
1:04:58
stories,
1:04:59
measurable
1:04:59
results
1:05:00
and
1:05:00
you'll
1:05:01
see
1:05:01
how
1:05:01
retailers
1:05:01
like
1:05:02
Inter
1:05:02
Marche
1:05:03
or
1:05:03
Etika
1:05:03
turned
1:05:04
insights
1:05:04
into
1:05:04
action
1:05:05
and
1:05:05
then
1:05:05
that
1:05:06
action
1:05:06
into
1:05:06
real
1:05:07
roi.
1:05:09
Then
1:05:10
the
1:05:10
real
1:05:10
showstopper.
1:05:11
We've
1:05:11
got
1:05:11
a
1:05:11
retail
1:05:12
store
1:05:12
inside
1:05:13
which
1:05:13
have
1:05:14
solutions
1:05:14
that
1:05:15
are
1:05:15
live
1:05:15
in
1:05:15
action.
1:05:16
You
1:05:16
can
1:05:16
try
1:05:17
Chris.
1:05:17
The
1:05:18
world's
1:05:18
most
1:05:18
advanced
1:05:19
visual
1:05:19
AI
1:05:20
flows
1:05:20
yourself
1:05:21
scan
1:05:21
a
1:05:21
basket.
1:05:22
Okay.
1:05:22
Item
1:05:22
recognition,
1:05:24
breeze,
1:05:25
breeze
1:05:25
through
1:05:25
our
1:05:26
age
1:05:26
restricted
1:05:27
checkout
1:05:29
with
1:05:29
instant
1:05:29
approval
1:05:30
and
1:05:30
then
1:05:31
see
1:05:31
how
1:05:31
produce
1:05:32
is
1:05:32
recognized
1:05:32
without
1:05:33
a
1:05:33
barcode.
1:05:33
You
1:05:34
don't
1:05:34
need
1:05:34
to
1:05:34
key
1:05:34
in
1:05:35
that
1:05:35
4011.
1:05:37
See
1:05:37
how
1:05:37
safety
1:05:38
is
1:05:39
helping
1:05:39
with
1:05:39
AI
1:05:40
spot
1:05:40
hazards
1:05:41
before
1:05:41
it
1:05:41
becomes
1:05:41
a
1:05:41
problem.
1:05:42
So
1:05:42
I
1:05:42
hope
1:05:43
that
1:05:43
retailers
1:05:43
will
1:05:44
come
1:05:44
in
1:05:44
and
1:05:45
they'll
1:05:45
leave
1:05:45
with
1:05:45
a
1:05:46
checklist
1:05:46
of
1:05:47
proven
1:05:47
use
1:05:47
cases
1:05:48
that
1:05:48
they
1:05:48
can
1:05:48
then
1:05:49
come
1:05:49
back
1:05:50
to
1:05:50
us
1:05:50
and
1:05:50
book
1:05:50
a
1:05:51
30
1:05:51
minute
1:05:51
impact
1:05:52
session
1:05:52
with
1:05:52
our
1:05:53
team
1:05:53
right
1:05:54
there
1:05:54
on
1:05:54
the
1:05:54
show
1:05:54
floor.
1:05:55
So
1:05:55
don't
1:05:55
just
1:05:55
hear
1:05:56
about
1:05:56
the
1:05:56
future,
1:05:56
come
1:05:56
experience
1:05:57
it,
1:05:58
measure
1:05:58
it,
1:05:58
take
1:05:58
it
1:05:59
back
1:05:59
to
1:05:59
your
1:05:59
stores
1:05:59
and
1:06:00
we'll
1:06:00
see
1:06:00
you
1:06:00
at
1:06:00
booth
1:06:01
3522
1:06:02
at
1:06:02
NRF.
1:06:03
Now
1:06:03
you're
1:06:04
speaking
1:06:04
my
1:06:04
language.
1:06:04
Yeah.
1:06:05
Now
1:06:05
you're
1:06:05
hinting
1:06:05
at
1:06:06
like
1:06:06
assisted
1:06:06
checkout
1:06:07
too,
1:06:07
which
1:06:07
I
1:06:07
love.
1:06:07
Whether
1:06:08
it's
1:06:08
assisted
1:06:08
as
1:06:09
a
1:06:09
self
1:06:09
checkout
1:06:09
or
1:06:10
assisted
1:06:10
for
1:06:10
the
1:06:10
employees
1:06:11
too,
1:06:11
which
1:06:11
is
1:06:11
awesome.
1:06:12
So
1:06:12
wow.
1:06:13
Yeah,
1:06:13
you
1:06:13
got,
1:06:13
you
1:06:13
got.
1:06:14
Oh
1:06:14
man,
1:06:15
my
1:06:15
mouth's
1:06:15
watering
1:06:16
just
1:06:16
thinking
1:06:16
about
1:06:16
it.
1:06:16
Arvin,
1:06:17
thank
1:06:17
you
1:06:17
so
1:06:17
much.
1:06:18
All
1:06:18
right,
1:06:18
well
1:06:18
that
1:06:18
was
1:06:18
great.
1:06:18
Thanks
1:06:19
for
1:06:19
joining
1:06:19
us
1:06:19
today.
1:06:20
Well,
1:06:20
we
1:06:20
can't
1:06:21
wait
1:06:21
to
1:06:21
see
1:06:21
you
1:06:21
guys
1:06:21
there.
1:06:22
Thanks,
1:06:23
Arvin.
1:06:23
Well,
1:06:24
sometimes
1:06:24
ad,
1:06:24
you
1:06:25
know,
1:06:25
it's
1:06:25
easiest
1:06:25
to
1:06:26
just
1:06:26
skip
1:06:26
checkout
1:06:27
altogether
1:06:27
and
1:06:28
use
1:06:28
buy
1:06:28
online
1:06:28
pickup
1:06:29
in
1:06:29
store.
1:06:29
At
1:06:29
least
1:06:30
many
1:06:30
people
1:06:30
like
1:06:31
to
1:06:31
do
1:06:31
that.
1:06:31
So
1:06:31
there
1:06:32
are
1:06:32
several
1:06:32
ways
1:06:32
to
1:06:32
go
1:06:33
about
1:06:33
that,
1:06:33
however,
1:06:34
and
1:06:34
there's
1:06:34
one
1:06:35
concept
1:06:35
that
1:06:35
major
1:06:35
retailers
1:06:36
like
1:06:36
Zara
1:06:37
and
1:06:37
H
1:06:37
and
1:06:37
M
1:06:37
are
1:06:37
utilizing
1:06:38
and
1:06:38
that
1:06:39
is
1:06:39
where
1:06:39
Cleveron
1:06:40
comes
1:06:40
in.
1:06:40
Our
1:06:41
next
1:06:41
guest.
1:06:41
So
1:06:41
it
1:06:41
is
1:06:41
with
1:06:42
great
1:06:42
pleasure
1:06:42
that
1:06:43
we
1:06:43
welcome
1:06:43
Mark
1:06:43
Schneider,
1:06:44
the
1:06:44
business
1:06:44
development
1:06:45
director
1:06:45
at
1:06:46
Cleveron.
1:06:46
Mark,
1:06:47
welcome
1:06:47
to
1:06:47
Omnitalk.
1:06:48
Thanks
1:06:48
for
1:06:48
joining
1:06:48
us
1:06:49
today.
1:06:50
Hi
1:06:50
guys.
1:06:50
Thank
1:06:50
you
1:06:51
so
1:06:51
much
1:06:51
for
1:06:51
having
1:06:51
me
1:06:51
on.
1:06:51
Really
1:06:52
looking
1:06:52
forward
1:06:52
to
1:06:52
this.
1:06:53
Well,
1:06:53
let's
1:06:53
start,
1:06:54
let's
1:06:54
start
1:06:54
by
1:06:54
giving,
1:06:55
giving
1:06:55
a
1:06:55
little
1:06:55
background
1:06:56
about
1:06:56
Cleveron.
1:06:57
We've
1:06:57
had
1:06:57
you
1:06:57
on
1:06:57
the
1:06:57
show
1:06:57
before,
1:06:58
but
1:06:58
there's
1:06:58
probably
1:06:58
many
1:06:59
people
1:06:59
that
1:06:59
are
1:06:59
hearing
1:06:59
about
1:07:00
you
1:07:00
for
1:07:00
the
1:07:00
first
1:07:00
time.
1:07:00
So
1:07:01
talk
1:07:01
about
1:07:01
what
1:07:01
Cleveron
1:07:02
is
1:07:02
and
1:07:02
what
1:07:02
it
1:07:02
does.
1:07:03
Yeah.
1:07:03
So
1:07:04
we
1:07:04
are
1:07:04
a
1:07:04
technology
1:07:05
company
1:07:05
we
1:07:06
design,
1:07:06
build
1:07:07
and
1:07:07
deploy
1:07:08
parcel
1:07:08
solutions
1:07:09
that
1:07:09
are
1:07:09
fully
1:07:09
automated
1:07:10
using
1:07:10
lockers
1:07:11
and
1:07:11
robotics.
1:07:12
We
1:07:13
combine
1:07:13
this
1:07:13
with
1:07:13
our
1:07:13
intelligent
1:07:14
software
1:07:14
and
1:07:15
our
1:07:15
AI
1:07:15
tools.
1:07:16
And
1:07:16
what
1:07:16
we
1:07:16
aim
1:07:16
to
1:07:16
do
1:07:17
is
1:07:17
help
1:07:17
retailers
1:07:17
and
1:07:18
logistics
1:07:18
partners
1:07:19
handle
1:07:20
this
1:07:20
growing
1:07:21
parcel
1:07:21
volume
1:07:21
that
1:07:22
we're
1:07:22
seeing
1:07:23
deploying
1:07:23
a
1:07:23
faster
1:07:24
and
1:07:24
smarter
1:07:25
and
1:07:25
more
1:07:25
customer
1:07:26
friendly
1:07:26
way
1:07:26
of
1:07:27
doing
1:07:27
it
1:07:27
as
1:07:28
a
1:07:28
business.
1:07:28
We've
1:07:28
been
1:07:29
going
1:07:29
since
1:07:29
2007.
1:07:31
We're
1:07:31
based
1:07:31
in
1:07:31
Estonia,
1:07:32
which
1:07:32
is
1:07:32
in
1:07:33
kind
1:07:33
of
1:07:33
northeastern
1:07:34
Europe.
1:07:35
And
1:07:36
we
1:07:36
were
1:07:36
created
1:07:37
just
1:07:37
to
1:07:37
solve
1:07:37
a
1:07:38
problem.
1:07:38
Problem
1:07:38
that
1:07:38
was
1:07:39
becoming
1:07:39
more
1:07:40
and
1:07:40
more
1:07:40
prevalent
1:07:40
at
1:07:40
that
1:07:41
time
1:07:41
is
1:07:41
how
1:07:42
can
1:07:42
we
1:07:42
deliver
1:07:42
parcels
1:07:43
quicker
1:07:44
to
1:07:44
people
1:07:44
but
1:07:45
without
1:07:45
them
1:07:46
having
1:07:46
to
1:07:46
queue
1:07:46
up
1:07:46
in
1:07:47
a
1:07:47
store
1:07:47
at
1:07:47
a
1:07:47
collection
1:07:48
desk
1:07:48
or
1:07:48
having
1:07:49
to
1:07:49
wait
1:07:49
in
1:07:50
home
1:07:50
all
1:07:50
day
1:07:51
for
1:07:51
it
1:07:51
to
1:07:51
be
1:07:51
delivered.
1:07:52
So
1:07:52
we
1:07:52
were
1:07:53
going
1:07:53
through
1:07:53
R
1:07:53
and
1:07:54
D,
1:07:54
we
1:07:54
were
1:07:54
looking
1:07:54
at
1:07:54
what
1:07:55
do
1:07:55
we
1:07:55
do.
1:07:55
And
1:07:56
we
1:07:56
came
1:07:56
up
1:07:56
with
1:07:56
our
1:07:56
robotic
1:07:57
locker
1:07:57
concept
1:07:58
which
1:07:58
deployed
1:07:58
for
1:07:58
the
1:07:59
first
1:07:59
time
1:07:59
actually
1:07:59
in
1:08:00
2016
1:08:00
in
1:08:00
the
1:08:00
US
1:08:02
since
1:08:02
then
1:08:02
we've
1:08:03
been
1:08:03
developing
1:08:04
it,
1:08:04
making
1:08:04
it
1:08:04
quicker,
1:08:05
making
1:08:05
it
1:08:05
smarter,
1:08:06
making
1:08:06
it
1:08:06
quieter,
1:08:07
and
1:08:07
also
1:08:08
looking
1:08:08
at
1:08:08
other
1:08:09
functionality
1:08:09
that
1:08:10
we
1:08:10
can
1:08:10
give
1:08:10
that
1:08:10
system
1:08:11
because
1:08:11
we
1:08:11
want
1:08:11
to
1:08:11
make
1:08:11
it
1:08:12
more
1:08:12
reliable
1:08:13
and
1:08:13
something
1:08:13
that's
1:08:13
future
1:08:14
proof
1:08:14
for
1:08:14
stores.
1:08:16
So,
1:08:16
Mark,
1:08:17
I
1:08:17
always
1:08:17
love
1:08:17
asking
1:08:18
this
1:08:18
question
1:08:18
too.
1:08:18
What
1:08:19
does
1:08:19
a
1:08:19
business
1:08:19
development
1:08:20
director
1:08:20
do?
1:08:21
What
1:08:21
is
1:08:21
your
1:08:21
job
1:08:21
responsibility?
1:08:22
At
1:08:22
the
1:08:23
end
1:08:23
of
1:08:23
the
1:08:23
day,
1:08:24
the.
1:08:24
Easiest
1:08:25
thing
1:08:25
to
1:08:25
say,
1:08:25
Chris,
1:08:26
is
1:08:26
what
1:08:26
don't
1:08:26
we
1:08:26
do?
1:08:27
I
1:08:27
think,
1:08:27
you
1:08:28
know,
1:08:28
when
1:08:28
you're
1:08:28
looking
1:08:28
at
1:08:28
business
1:08:29
development,
1:08:30
you
1:08:30
know
1:08:30
what
1:08:31
you
1:08:31
have
1:08:31
to
1:08:31
do
1:08:31
anything
1:08:32
and
1:08:32
everything
1:08:32
you
1:08:33
have
1:08:33
to
1:08:33
get
1:08:33
really
1:08:33
stuck
1:08:34
in.
1:08:34
But
1:08:34
kind
1:08:35
of
1:08:35
what's
1:08:35
at
1:08:35
the
1:08:36
heart
1:08:36
of
1:08:36
what
1:08:36
I
1:08:36
do
1:08:37
is
1:08:37
leading
1:08:37
the
1:08:38
growth
1:08:38
that
1:08:39
we
1:08:39
have
1:08:39
in
1:08:39
terms
1:08:39
of
1:08:40
our
1:08:40
parcel
1:08:40
locker
1:08:41
systems
1:08:41
and
1:08:41
our
1:08:41
robotic
1:08:42
click
1:08:42
and
1:08:42
collect
1:08:43
solutions
1:08:43
across
1:08:44
this
1:08:44
retail
1:08:44
and
1:08:45
logistics
1:08:45
market.
1:08:46
So
1:08:46
what
1:08:46
I
1:08:46
like
1:08:47
to
1:08:47
do
1:08:47
is
1:08:47
I
1:08:47
like
1:08:47
to
1:08:47
build
1:08:48
those
1:08:48
really
1:08:48
strategic
1:08:49
long
1:08:49
term
1:08:50
partnerships
1:08:51
that
1:08:51
major
1:08:51
retailers
1:08:52
like,
1:08:53
but
1:08:53
also
1:08:53
looking
1:08:54
at
1:08:54
how
1:08:54
we
1:08:54
can
1:08:54
shape
1:08:55
that
1:08:55
commercial
1:08:55
proposition
1:08:56
for
1:08:56
them.
1:08:57
In
1:08:57
retail,
1:08:58
everyone's
1:08:58
fighting
1:08:59
for
1:08:59
space,
1:08:59
everyone's
1:09:00
fighting
1:09:00
for
1:09:00
funding.
1:09:01
So
1:09:01
what's
1:09:01
really
1:09:01
important
1:09:01
for
1:09:02
me
1:09:02
as
1:09:02
a
1:09:02
business
1:09:03
development
1:09:03
director
1:09:04
is
1:09:04
ensuring
1:09:05
we
1:09:05
can
1:09:05
prove
1:09:06
that
1:09:06
return
1:09:06
on
1:09:06
investment
1:09:07
and
1:09:07
give
1:09:07
that
1:09:07
retailer
1:09:08
every
1:09:08
tool
1:09:09
they
1:09:09
can
1:09:09
so
1:09:09
they
1:09:09
can
1:09:09
get
1:09:09
the
1:09:10
sign
1:09:10
off
1:09:10
to
1:09:10
have
1:09:10
these
1:09:10
amazing
1:09:11
kind
1:09:11
of
1:09:12
high
1:09:12
capacity,
1:09:12
customer
1:09:13
friendly
1:09:13
collection
1:09:14
hubs
1:09:14
in
1:09:14
their
1:09:14
store.
1:09:15
Ultimately,
1:09:16
I
1:09:17
help
1:09:17
retailers
1:09:18
handle
1:09:18
more
1:09:18
parcels
1:09:19
using
1:09:20
less
1:09:20
space
1:09:21
in
1:09:21
store,
1:09:22
which
1:09:22
gives
1:09:22
colleagues
1:09:23
their
1:09:23
time
1:09:23
back
1:09:23
to
1:09:24
focus
1:09:24
on
1:09:24
tasks
1:09:25
in
1:09:25
store
1:09:25
that
1:09:26
deliver
1:09:26
that
1:09:26
kind
1:09:26
of
1:09:27
everyday
1:09:27
quality
1:09:28
that
1:09:28
customers
1:09:28
expect.
1:09:29
Mark,
1:09:30
we
1:09:30
were
1:09:30
at
1:09:31
one
1:09:31
of
1:09:31
the
1:09:31
early
1:09:32
flagship
1:09:32
deployments
1:09:34
in
1:09:34
Madrid
1:09:34
that
1:09:34
Zara
1:09:35
did.
1:09:35
And
1:09:35
so
1:09:36
we,
1:09:36
Chris
1:09:36
and
1:09:36
I
1:09:36
have
1:09:37
both
1:09:37
seen
1:09:38
how
1:09:38
impressive
1:09:39
this
1:09:39
automated
1:09:39
parcel
1:09:40
locker
1:09:40
is.
1:09:42
Super
1:09:42
easy.
1:09:42
You
1:09:42
just
1:09:43
scan
1:09:43
The
1:09:43
QR
1:09:43
code
1:09:44
that
1:09:44
you
1:09:44
get
1:09:44
in
1:09:44
your
1:09:44
email,
1:09:45
next
1:09:45
thing
1:09:45
you
1:09:46
know
1:09:46
this,
1:09:46
you
1:09:46
know,
1:09:46
within
1:09:47
seconds
1:09:47
you're
1:09:48
delivered
1:09:48
your
1:09:48
parcel.
1:09:49
And
1:09:50
behind
1:09:50
the
1:09:50
scenes,
1:09:51
that's
1:09:51
saving
1:09:51
so
1:09:51
much
1:09:51
more
1:09:52
time
1:09:52
for
1:09:52
retailers
1:09:53
and
1:09:53
space,
1:09:54
as
1:09:54
you
1:09:54
mentioned.
1:09:55
But
1:09:56
I
1:09:56
want
1:09:56
to,
1:09:56
I
1:09:57
want
1:09:57
to
1:09:57
get
1:09:57
your
1:09:57
perspective.
1:09:58
That
1:09:58
was,
1:09:58
that
1:09:59
was
1:09:59
a
1:09:59
few
1:09:59
years
1:09:59
ago.
1:10:01
Who
1:10:01
are
1:10:01
some
1:10:01
of
1:10:01
Cleveron's
1:10:02
kind
1:10:02
of
1:10:02
best
1:10:03
retail
1:10:03
partners
1:10:04
that
1:10:04
are
1:10:05
leveraging
1:10:05
Cleveron
1:10:06
products
1:10:06
today?
1:10:06
Like,
1:10:06
who
1:10:07
would
1:10:07
you
1:10:07
call
1:10:07
out,
1:10:07
you
1:10:08
know,
1:10:08
for
1:10:08
people
1:10:08
listening
1:10:09
to,
1:10:09
really
1:10:09
pay
1:10:10
attention
1:10:10
to
1:10:10
or
1:10:11
use
1:10:11
as
1:10:11
maybe
1:10:12
those
1:10:12
kind
1:10:12
of
1:10:12
shining
1:10:13
examples
1:10:13
of
1:10:13
the
1:10:14
technology
1:10:14
at
1:10:14
work?
1:10:15
One
1:10:15
thing
1:10:15
to
1:10:16
add
1:10:16
with
1:10:16
that
1:10:16
end,
1:10:16
that
1:10:17
feeling
1:10:17
of
1:10:18
looking
1:10:18
at
1:10:18
that
1:10:18
parcel
1:10:19
being
1:10:19
delivered
1:10:19
for
1:10:20
the
1:10:20
first
1:10:20
time
1:10:20
on
1:10:20
a
1:10:20
robot.
1:10:21
That
1:10:21
feeling
1:10:21
doesn't
1:10:22
go
1:10:22
away.
1:10:22
That
1:10:22
happens
1:10:23
every
1:10:23
single
1:10:24
time
1:10:24
you
1:10:24
pick
1:10:24
up
1:10:24
a
1:10:24
parcel.
1:10:25
It's
1:10:25
just
1:10:25
this
1:10:26
magic
1:10:26
that
1:10:27
happens
1:10:27
and
1:10:27
this
1:10:27
parcel
1:10:28
just
1:10:28
appears
1:10:28
to
1:10:28
you
1:10:28
within
1:10:29
seconds.
1:10:29
It's
1:10:30
that
1:10:30
feeling
1:10:30
never
1:10:31
goes
1:10:31
away.
1:10:31
And
1:10:31
that's
1:10:32
something
1:10:32
that
1:10:32
actually
1:10:32
lots
1:10:33
of
1:10:33
our
1:10:33
retailers
1:10:33
like.
1:10:34
That's
1:10:34
one
1:10:34
of
1:10:34
the
1:10:34
big
1:10:34
selling
1:10:35
points
1:10:35
for
1:10:36
having
1:10:36
this
1:10:36
robotic
1:10:37
system.
1:10:37
When
1:10:37
you've
1:10:38
got
1:10:38
customers
1:10:38
filming
1:10:39
their
1:10:39
collection
1:10:39
and
1:10:40
putting
1:10:40
it
1:10:40
on
1:10:40
their
1:10:41
socials
1:10:41
about,
1:10:42
you
1:10:42
know,
1:10:42
look
1:10:42
at
1:10:42
this,
1:10:43
you
1:10:43
know,
1:10:44
living
1:10:44
in
1:10:44
2030,
1:10:46
this
1:10:46
futuristic
1:10:47
collection,
1:10:48
that's
1:10:49
the
1:10:49
great
1:10:49
thing.
1:10:50
That's
1:10:50
what
1:10:50
you
1:10:50
want.
1:10:50
That,
1:10:50
that
1:10:51
real
1:10:51
genuine
1:10:52
customer
1:10:53
engagement
1:10:53
that
1:10:53
you
1:10:53
get
1:10:54
with
1:10:54
a
1:10:54
product.
1:10:55
But
1:10:55
in
1:10:55
terms
1:10:56
of
1:10:56
looking
1:10:56
at
1:10:56
other
1:10:57
ways
1:10:57
that
1:10:57
retailers
1:10:58
are
1:10:58
doing
1:10:58
it,
1:10:58
you've
1:10:59
mentioned
1:10:59
kind
1:10:59
of
1:10:59
looking
1:11:00
at
1:11:00
Zara,
1:11:01
how
1:11:01
they
1:11:01
do
1:11:01
it
1:11:01
with
1:11:01
their
1:11:02
robotic
1:11:02
units.
1:11:03
It's
1:11:03
this
1:11:04
futuristic
1:11:05
collection
1:11:06
service
1:11:07
that
1:11:07
customers
1:11:08
really
1:11:08
like
1:11:08
to
1:11:08
use.
1:11:08
And
1:11:09
what
1:11:09
it
1:11:09
does
1:11:09
is
1:11:09
it
1:11:09
helps
1:11:10
absorb
1:11:10
that
1:11:10
volume
1:11:11
that's
1:11:11
coming
1:11:11
into
1:11:12
store
1:11:12
for
1:11:12
Click
1:11:12
and
1:11:13
Collect.
1:11:13
It
1:11:13
moves
1:11:14
it
1:11:14
away
1:11:14
from
1:11:14
the
1:11:14
service
1:11:15
desk,
1:11:15
it
1:11:15
moves
1:11:16
it
1:11:16
away
1:11:16
from
1:11:16
changing
1:11:17
rooms.
1:11:17
And
1:11:17
what
1:11:17
that
1:11:17
does
1:11:18
is
1:11:18
it
1:11:18
helps
1:11:18
reduce
1:11:18
the
1:11:19
queues
1:11:19
that
1:11:19
you
1:11:19
have
1:11:19
in
1:11:20
store
1:11:20
so
1:11:20
those
1:11:20
customers
1:11:21
in
1:11:21
store
1:11:21
that
1:11:21
are
1:11:21
queuing
1:11:22
to
1:11:22
pay
1:11:23
can
1:11:23
do
1:11:23
just
1:11:23
that.
1:11:24
They
1:11:24
haven't
1:11:24
got
1:11:24
to
1:11:24
wait
1:11:24
for
1:11:25
the
1:11:25
colleagues
1:11:25
to
1:11:25
go
1:11:25
back
1:11:26
of
1:11:26
house
1:11:26
to
1:11:26
collect
1:11:26
their
1:11:26
parcels.
1:11:27
It
1:11:28
shifts
1:11:28
it
1:11:28
to
1:11:28
a
1:11:28
dedicated
1:11:29
area
1:11:30
that
1:11:30
shortens
1:11:31
the
1:11:31
collection
1:11:32
time.
1:11:32
For
1:11:33
example,
1:11:33
you
1:11:33
mentioned
1:11:33
Zara
1:11:34
Inditex
1:11:35
have
1:11:35
over
1:11:35
300
1:11:36
of
1:11:36
these
1:11:36
now
1:11:36
deployed
1:11:37
in
1:11:37
over
1:11:37
38
1:11:38
different
1:11:38
countries
1:11:39
worldwide.
1:11:39
So
1:11:39
it's
1:11:40
not
1:11:40
just
1:11:40
a
1:11:41
US
1:11:41
or
1:11:41
a
1:11:41
European
1:11:42
thing.
1:11:42
We're
1:11:42
seeing
1:11:43
it
1:11:43
across
1:11:43
the
1:11:43
globe
1:11:43
and
1:11:44
customers
1:11:44
are
1:11:44
loving
1:11:45
it.
1:11:45
Some
1:11:46
of
1:11:46
our
1:11:46
other
1:11:46
solutions
1:11:46
that
1:11:47
we
1:11:47
have,
1:11:47
we
1:11:48
have
1:11:48
one
1:11:48
of
1:11:48
the
1:11:49
largest
1:11:49
furniture
1:11:50
retailers
1:11:50
in
1:11:50
the
1:11:51
world.
1:11:51
They
1:11:51
use
1:11:51
our
1:11:52
oversized
1:11:52
lockers.
1:11:53
So
1:11:53
what
1:11:53
this
1:11:54
means
1:11:54
is
1:11:54
as
1:11:54
a
1:11:54
customer,
1:11:55
you
1:11:55
can
1:11:55
click
1:11:56
and
1:11:56
collect
1:11:56
a
1:11:56
wardrobe,
1:11:57
the
1:11:57
hangers
1:11:58
to
1:11:58
hang
1:11:58
up
1:11:58
your
1:11:58
clothes,
1:11:59
a
1:11:59
light
1:11:59
bulb
1:12:00
to
1:12:00
change
1:12:00
the
1:12:00
light
1:12:00
in
1:12:00
your
1:12:01
dressing
1:12:01
room,
1:12:01
and
1:12:02
Some
1:12:02
knives
1:12:02
and
1:12:02
forks,
1:12:03
collect
1:12:04
it
1:12:04
all
1:12:04
from
1:12:04
the
1:12:04
same
1:12:05
compartment
1:12:05
at
1:12:05
the
1:12:06
same
1:12:06
time,
1:12:06
normally
1:12:07
on
1:12:07
a
1:12:07
trolley.
1:12:08
So
1:12:08
you
1:12:08
can
1:12:08
just
1:12:08
wheel
1:12:09
it
1:12:09
out
1:12:09
straight
1:12:09
to
1:12:09
your
1:12:10
car
1:12:10
and
1:12:10
go.
1:12:11
And
1:12:11
that
1:12:11
kind
1:12:12
of
1:12:12
convenience
1:12:12
and
1:12:12
frictionlessness
1:12:14
service
1:12:14
of
1:12:14
what
1:12:14
our
1:12:14
customers
1:12:15
and
1:12:15
what
1:12:15
retailers
1:12:16
customers
1:12:16
are
1:12:17
really
1:12:17
looking
1:12:17
for,
1:12:18
it
1:12:18
reduces
1:12:19
that
1:12:20
split
1:12:20
collections
1:12:20
that
1:12:21
you
1:12:21
might
1:12:21
get.
1:12:22
And
1:12:22
it
1:12:22
just
1:12:22
means
1:12:22
that
1:12:23
customers
1:12:23
leave
1:12:23
happy
1:12:24
and
1:12:24
they
1:12:24
want
1:12:24
to
1:12:24
do
1:12:24
it
1:12:24
again.
1:12:25
They
1:12:25
want
1:12:25
to
1:12:25
use
1:12:25
your
1:12:26
click
1:12:26
and
1:12:26
collect
1:12:27
service
1:12:27
again.
1:12:28
I
1:12:29
think
1:12:29
the
1:12:29
other
1:12:29
final
1:12:30
business
1:12:30
case
1:12:31
is
1:12:31
looking
1:12:31
at
1:12:32
grocery
1:12:32
retailers
1:12:33
and
1:12:33
convenience
1:12:34
retailers
1:12:34
that
1:12:35
they
1:12:35
do
1:12:36
have
1:12:36
a
1:12:37
small
1:12:37
amount
1:12:37
of
1:12:37
their
1:12:37
own
1:12:38
click
1:12:38
and
1:12:38
collect,
1:12:38
but
1:12:39
actually
1:12:39
have
1:12:39
generally
1:12:40
have
1:12:40
quite
1:12:40
a
1:12:40
lot
1:12:40
of
1:12:40
space
1:12:40
in
1:12:41
store.
1:12:42
So
1:12:42
one
1:12:42
particular
1:12:43
grocery
1:12:43
retailer
1:12:44
we
1:12:44
work
1:12:44
with
1:12:44
in
1:12:44
Sweden,
1:12:45
they've
1:12:45
created
1:12:45
an
1:12:45
open
1:12:46
network
1:12:46
where
1:12:46
they
1:12:47
use
1:12:47
our
1:12:47
Cleveron
1:12:48
solution,
1:12:48
but
1:12:48
actually
1:12:49
allow
1:12:49
marketplaces
1:12:50
and
1:12:51
other
1:12:51
couriers
1:12:52
to
1:12:52
deliver
1:12:52
their
1:12:52
parcels
1:12:53
into
1:12:53
these
1:12:53
hubs.
1:12:54
So
1:12:54
customers
1:12:55
go
1:12:55
to
1:12:55
one
1:12:56
place,
1:12:56
collect
1:12:57
everything
1:12:57
and
1:12:58
leave.
1:12:59
From
1:12:59
a
1:12:59
customer
1:12:59
point
1:12:59
of
1:13:00
view,
1:13:00
perfect.
1:13:01
But
1:13:01
from
1:13:01
a
1:13:01
retailer
1:13:02
point
1:13:02
of
1:13:02
view,
1:13:02
that
1:13:02
grocery
1:13:03
retailer
1:13:03
has
1:13:04
now
1:13:04
unlocked
1:13:04
a
1:13:04
new
1:13:05
revenue
1:13:05
stream.
1:13:06
So
1:13:06
they
1:13:06
are
1:13:07
earning
1:13:07
money
1:13:07
per
1:13:08
parcel
1:13:08
that's
1:13:08
collected,
1:13:09
but
1:13:09
they
1:13:09
also
1:13:10
have
1:13:10
that
1:13:10
incremental
1:13:11
sales
1:13:12
from
1:13:12
those
1:13:12
customers
1:13:12
that
1:13:13
wouldn't
1:13:13
normally
1:13:13
be
1:13:13
in
1:13:13
store,
1:13:14
coming
1:13:14
into
1:13:15
store
1:13:15
and
1:13:15
then
1:13:15
thinking,
1:13:16
oh,
1:13:16
actually,
1:13:16
let
1:13:17
me
1:13:17
pick
1:13:17
up
1:13:17
some
1:13:18
bread,
1:13:18
let
1:13:18
me
1:13:18
pick
1:13:19
up
1:13:19
some
1:13:19
milk
1:13:20
while
1:13:20
I'm
1:13:20
in
1:13:20
the
1:13:20
store.
1:13:21
And
1:13:21
I
1:13:21
think
1:13:21
across
1:13:22
all
1:13:22
the
1:13:22
examples
1:13:23
that
1:13:23
we
1:13:23
have,
1:13:24
clever
1:13:25
on
1:13:25
being
1:13:26
used
1:13:26
to
1:13:26
make
1:13:27
click
1:13:27
and
1:13:27
collect
1:13:27
like
1:13:28
a
1:13:28
natural
1:13:28
extension
1:13:29
of
1:13:29
their
1:13:29
store,
1:13:30
are
1:13:30
those
1:13:30
retailers
1:13:30
that
1:13:31
are
1:13:31
winning
1:13:31
and
1:13:31
doing
1:13:31
it
1:13:32
really,
1:13:32
really
1:13:32
well,
1:13:33
using
1:13:33
automation
1:13:34
to
1:13:35
handle
1:13:36
bigger
1:13:36
items,
1:13:37
bigger
1:13:37
volumes
1:13:37
and
1:13:38
complex
1:13:38
orders,
1:13:40
while
1:13:40
keeping
1:13:40
the
1:13:41
experience
1:13:41
simple
1:13:42
for
1:13:42
customers
1:13:43
and
1:13:43
manageable
1:13:44
for
1:13:44
store
1:13:44
teams.
1:13:45
Because
1:13:45
what
1:13:45
you
1:13:45
don't
1:13:45
want
1:13:45
to
1:13:46
do
1:13:46
is
1:13:46
burden
1:13:46
those
1:13:47
store
1:13:47
teams
1:13:47
with
1:13:48
even
1:13:48
more
1:13:48
tasks
1:13:49
and
1:13:49
even
1:13:49
more
1:13:49
complication.
1:13:50
Well,
1:13:50
and
1:13:51
Mark,
1:13:51
I
1:13:51
think
1:13:51
it's
1:13:51
important
1:13:52
just
1:13:52
to
1:13:52
kind
1:13:52
of
1:13:54
explain
1:13:54
a
1:13:54
little
1:13:54
bit.
1:13:55
So
1:13:55
each
1:13:55
of
1:13:55
these
1:13:55
solutions
1:13:56
that
1:13:56
you
1:13:56
mentioned,
1:13:56
the
1:13:57
automated
1:13:57
parcel
1:13:57
pickup
1:13:58
that
1:13:58
Zara
1:13:58
is
1:13:59
using,
1:13:59
the
1:13:59
home
1:14:00
furnishings
1:14:01
parcel
1:14:01
pickup,
1:14:02
the
1:14:02
lockers
1:14:03
that
1:14:03
they're
1:14:03
using,
1:14:03
which
1:14:04
sound
1:14:04
incredible,
1:14:04
by
1:14:05
the
1:14:05
way,
1:14:05
I
1:14:05
love
1:14:05
the
1:14:05
idea
1:14:06
of
1:14:06
just
1:14:06
rolling
1:14:07
out
1:14:07
my,
1:14:08
you
1:14:08
know,
1:14:09
piece
1:14:09
of
1:14:09
furniture
1:14:09
on
1:14:10
a
1:14:10
trolley.
1:14:10
Right.
1:14:11
House,
1:14:11
they're
1:14:11
ready
1:14:11
for
1:14:12
you
1:14:12
to
1:14:12
collect.
1:14:13
Right.
1:14:13
And
1:14:14
then
1:14:14
finally,
1:14:14
the
1:14:14
grocery
1:14:15
case
1:14:15
that
1:14:16
you
1:14:16
mentioned
1:14:16
in
1:14:16
Sweden,
1:14:17
which
1:14:17
requires,
1:14:17
you
1:14:18
know,
1:14:18
refrigerated
1:14:19
compartments,
1:14:20
you
1:14:20
know,
1:14:20
ambient
1:14:21
compartments,
1:14:21
I
1:14:22
imagine,
1:14:23
what
1:14:23
is
1:14:24
the.
1:14:24
Like,
1:14:24
how
1:14:25
do
1:14:25
you
1:14:25
set
1:14:25
the
1:14:25
expectation
1:14:26
for
1:14:26
the
1:14:26
retailers
1:14:27
for
1:14:27
each
1:14:27
of
1:14:27
those
1:14:27
cases,
1:14:28
if
1:14:28
you
1:14:28
don't
1:14:28
mind
1:14:29
kind
1:14:29
of
1:14:29
diving
1:14:30
into
1:14:30
that,
1:14:30
like,
1:14:30
what
1:14:30
is
1:14:31
the
1:14:31
Zara
1:14:31
pickup
1:14:32
capacity
1:14:33
and
1:14:33
what
1:14:33
does
1:14:33
that
1:14:33
look
1:14:34
like
1:14:34
from
1:14:34
like
1:14:34
an
1:14:35
infrastructure
1:14:35
perspective?
1:14:36
And
1:14:36
then
1:14:37
the
1:14:37
same
1:14:38
question
1:14:38
for
1:14:38
the
1:14:39
other
1:14:39
two.
1:14:39
Use
1:14:39
Cases.
1:14:40
What
1:14:41
we
1:14:41
like
1:14:41
to
1:14:42
make
1:14:42
sure
1:14:42
that
1:14:42
retailers
1:14:43
understand
1:14:43
is
1:14:43
our
1:14:44
solutions
1:14:45
give
1:14:45
you
1:14:45
flex
1:14:46
so
1:14:46
you
1:14:46
can
1:14:47
expand
1:14:47
and
1:14:47
decrease
1:14:48
size
1:14:48
depending
1:14:49
on
1:14:49
your
1:14:50
requirements.
1:14:50
Because
1:14:51
what
1:14:51
you
1:14:51
don't
1:14:51
want
1:14:51
to
1:14:52
do
1:14:52
is
1:14:52
you
1:14:52
want,
1:14:52
you
1:14:52
don't
1:14:52
want
1:14:53
to
1:14:53
install
1:14:53
a
1:14:53
new
1:14:53
system.
1:14:54
For
1:14:54
example,
1:14:54
in
1:14:55
Zara,
1:14:55
we
1:14:55
put
1:14:56
in
1:14:56
a
1:14:56
robotic
1:14:56
unit,
1:14:57
it's
1:14:57
ready
1:14:58
to
1:14:58
go.
1:14:58
And
1:14:58
actually
1:14:58
in
1:14:58
a
1:14:58
year's
1:14:59
time,
1:14:59
because
1:14:59
the
1:14:59
volumes
1:15:00
increased
1:15:00
so
1:15:00
much
1:15:01
that
1:15:01
now
1:15:02
no
1:15:02
longer
1:15:02
is
1:15:02
fit
1:15:02
for
1:15:03
purpose.
1:15:03
So
1:15:04
we
1:15:04
really
1:15:04
work
1:15:04
hardly
1:15:05
with
1:15:05
the
1:15:05
retailers,
1:15:06
Zara
1:15:07
being
1:15:07
one
1:15:07
of
1:15:07
them,
1:15:08
where
1:15:08
we
1:15:08
make
1:15:09
sure
1:15:09
that
1:15:09
we
1:15:09
look
1:15:09
at
1:15:09
the
1:15:10
volumes
1:15:10
that
1:15:10
you've
1:15:11
seen,
1:15:11
the
1:15:11
volumes
1:15:11
you
1:15:12
think
1:15:12
you're
1:15:12
going
1:15:12
to
1:15:12
see,
1:15:12
and
1:15:13
then
1:15:13
come
1:15:13
up
1:15:13
with
1:15:13
the
1:15:14
solution
1:15:15
that
1:15:15
best
1:15:15
suits,
1:15:15
that
1:15:16
allows
1:15:16
you
1:15:16
that
1:15:16
flex
1:15:17
not
1:15:17
just
1:15:17
for
1:15:17
peak
1:15:18
periods,
1:15:19
because
1:15:19
traditional
1:15:20
peak
1:15:20
periods
1:15:20
I
1:15:20
think
1:15:21
we're
1:15:21
going
1:15:21
to
1:15:21
start
1:15:21
to
1:15:22
see
1:15:22
disappear.
1:15:22
And
1:15:23
actually
1:15:23
it's
1:15:23
just
1:15:23
going
1:15:23
to
1:15:23
be
1:15:23
busy
1:15:24
all
1:15:24
the
1:15:24
time.
1:15:25
We're
1:15:25
just
1:15:25
going
1:15:26
to
1:15:26
have
1:15:26
this
1:15:26
volume
1:15:26
all
1:15:27
the
1:15:27
time
1:15:27
and
1:15:27
a
1:15:28
lot
1:15:28
more
1:15:28
unpredictable
1:15:29
when
1:15:29
we
1:15:29
look
1:15:29
at
1:15:29
things
1:15:30
like
1:15:30
social
1:15:30
commerce.
1:15:31
So
1:15:31
things
1:15:31
like
1:15:31
TikTok
1:15:32
shop,
1:15:32
where
1:15:33
you
1:15:33
can
1:15:33
have
1:15:33
a
1:15:34
product
1:15:34
selling
1:15:34
nothing
1:15:35
one
1:15:35
week
1:15:35
and
1:15:35
then
1:15:36
the
1:15:36
next
1:15:36
day
1:15:36
you're
1:15:36
selling
1:15:37
tens
1:15:37
of
1:15:37
thousands
1:15:37
of
1:15:37
units
1:15:38
because
1:15:38
someone's
1:15:39
done
1:15:39
a
1:15:39
great
1:15:39
dance
1:15:40
on
1:15:40
TikTok
1:15:40
and
1:15:40
told
1:15:40
you
1:15:41
to
1:15:41
buy
1:15:41
it.
1:15:42
When
1:15:42
we
1:15:42
talk
1:15:42
about
1:15:43
integration,
1:15:43
we
1:15:43
like
1:15:44
to
1:15:44
have
1:15:44
everything
1:15:44
integrated
1:15:45
into
1:15:45
the
1:15:45
retailer's
1:15:46
current
1:15:46
system.
1:15:46
So
1:15:47
it's
1:15:47
a
1:15:47
really
1:15:47
simple
1:15:49
integration
1:15:50
that
1:15:50
we
1:15:50
have
1:15:50
using
1:15:51
open
1:15:52
API
1:15:52
links.
1:15:53
And
1:15:53
what
1:15:54
that
1:15:54
just
1:15:54
allows
1:15:54
us
1:15:54
to
1:15:55
do
1:15:55
is
1:15:55
any
1:15:55
changes
1:15:56
that
1:15:56
the
1:15:56
retailer
1:15:56
makes
1:15:57
on
1:15:57
their
1:15:57
end
1:15:57
can
1:15:57
naturally
1:15:58
be
1:15:58
reflected
1:15:58
in
1:15:58
the
1:15:59
tech
1:15:59
that
1:16:00
we
1:16:00
have
1:16:00
in
1:16:00
store.
1:16:01
Particularly
1:16:02
when
1:16:02
it
1:16:02
comes
1:16:02
to
1:16:03
the
1:16:04
furniture
1:16:04
shop,
1:16:05
we
1:16:06
need
1:16:06
to
1:16:06
make
1:16:06
sure
1:16:06
we've
1:16:07
got
1:16:07
the
1:16:07
right
1:16:07
size
1:16:07
compartments
1:16:08
because
1:16:08
you
1:16:08
can't
1:16:09
have
1:16:09
100
1:16:10
compartments
1:16:10
that
1:16:10
fit
1:16:11
in
1:16:11
a
1:16:11
whole
1:16:11
house.
1:16:12
You're
1:16:12
not
1:16:12
going
1:16:12
to
1:16:12
have
1:16:13
everyone
1:16:13
click
1:16:13
and
1:16:13
collect
1:16:14
in
1:16:14
a
1:16:14
wardrobe.
1:16:14
There
1:16:14
are
1:16:15
going
1:16:15
to
1:16:15
be
1:16:15
smaller
1:16:16
compartments
1:16:17
needed.
1:16:17
So
1:16:17
it's
1:16:17
working
1:16:18
again
1:16:18
with
1:16:18
that
1:16:18
retailer
1:16:19
to
1:16:20
look
1:16:20
at
1:16:20
what
1:16:20
is
1:16:20
their
1:16:20
aspiration
1:16:21
and
1:16:22
then
1:16:22
we
1:16:22
can
1:16:22
tailor
1:16:23
make
1:16:23
that
1:16:24
solution
1:16:24
for
1:16:25
that
1:16:25
particular
1:16:25
retailer.
1:16:26
But
1:16:26
even
1:16:26
down
1:16:26
to
1:16:26
a
1:16:27
store
1:16:27
level,
1:16:28
we
1:16:28
know
1:16:28
that
1:16:28
not
1:16:28
every
1:16:29
single
1:16:29
unit
1:16:29
that
1:16:30
we
1:16:30
create
1:16:30
will
1:16:31
be
1:16:31
the
1:16:31
same
1:16:31
that
1:16:31
will
1:16:32
fit
1:16:32
in
1:16:32
the
1:16:32
same
1:16:32
store
1:16:33
environment.
1:16:33
So
1:16:33
it's
1:16:34
that
1:16:34
flexibility
1:16:34
that
1:16:35
we
1:16:35
really
1:16:35
need
1:16:35
to
1:16:35
give
1:16:36
retailers
1:16:37
and
1:16:37
allow
1:16:37
them
1:16:38
to
1:16:39
increase
1:16:39
over
1:16:40
time
1:16:40
and
1:16:40
in
1:16:41
the
1:16:41
future,
1:16:41
because
1:16:41
the
1:16:42
business
1:16:42
that
1:16:42
we
1:16:42
go
1:16:42
into
1:16:43
now
1:16:43
is
1:16:43
not
1:16:44
necessarily
1:16:44
going
1:16:44
to
1:16:44
be
1:16:45
the
1:16:45
same
1:16:45
retail
1:16:45
business
1:16:46
in
1:16:46
five
1:16:46
years.
1:16:46
There'll
1:16:47
be
1:16:47
different
1:16:47
things
1:16:47
they
1:16:47
want
1:16:48
to
1:16:48
add
1:16:48
and
1:16:48
we
1:16:48
just
1:16:49
want
1:16:49
to
1:16:49
make
1:16:49
it
1:16:49
as
1:16:49
easy
1:16:50
as
1:16:50
possible
1:16:50
for
1:16:50
the
1:16:51
retailers
1:16:51
to
1:16:51
do
1:16:51
that.
1:16:52
So,
1:16:52
Mark,
1:16:52
I
1:16:52
love,
1:16:52
I
1:16:52
love
1:16:53
a
1:16:53
couple
1:16:53
of
1:16:53
things
1:16:53
you
1:16:53
said
1:16:53
there,
1:16:54
I
1:16:54
think
1:16:54
one,
1:16:54
I
1:16:54
love
1:16:54
how
1:16:55
you
1:16:55
laid
1:16:55
out,
1:16:55
you
1:16:55
know,
1:16:56
everything
1:16:56
that
1:16:57
you
1:16:57
guys
1:16:57
do
1:16:57
really
1:16:57
from
1:16:57
the
1:16:58
retailer
1:16:58
perspective
1:16:59
and
1:16:59
what
1:16:59
the
1:16:59
opportunities
1:17:00
are
1:17:00
in
1:17:01
thinking
1:17:01
like
1:17:01
this.
1:17:01
And
1:17:02
then
1:17:02
the
1:17:02
other
1:17:02
point
1:17:02
that
1:17:02
you
1:17:03
mentioned
1:17:03
back,
1:17:03
you
1:17:04
know,
1:17:04
when
1:17:05
you
1:17:05
started
1:17:05
that
1:17:05
conversation
1:17:06
with
1:17:06
ann
1:17:06
was
1:17:06
around
1:17:07
2030.
1:17:07
And
1:17:08
so
1:17:08
I
1:17:08
kind
1:17:09
of
1:17:09
want
1:17:09
to
1:17:09
stay
1:17:09
on
1:17:09
the
1:17:09
2030
1:17:10
angle,
1:17:10
but
1:17:10
now
1:17:10
I
1:17:10
want
1:17:10
to
1:17:11
shift
1:17:11
from
1:17:11
the
1:17:11
retailer
1:17:12
to
1:17:12
the
1:17:13
consumer.
1:17:13
So
1:17:14
what
1:17:14
do
1:17:14
you
1:17:14
think
1:17:15
are
1:17:15
the
1:17:15
biggest
1:17:16
challenges
1:17:16
that
1:17:17
retailers
1:17:17
are
1:17:17
going
1:17:17
to
1:17:18
have
1:17:18
to
1:17:18
adapt
1:17:18
to
1:17:18
in
1:17:19
terms
1:17:19
of
1:17:19
how
1:17:19
consumer
1:17:20
behavior
1:17:20
is
1:17:21
going
1:17:21
to
1:17:21
change
1:17:21
around
1:17:22
click
1:17:22
and
1:17:23
collect,
1:17:23
delivery,
1:17:24
parcel
1:17:24
pickup,
1:17:24
whatever
1:17:25
you
1:17:25
want
1:17:25
to
1:17:25
call
1:17:25
it.
1:17:26
And
1:17:26
what
1:17:26
advice
1:17:27
then
1:17:27
do
1:17:27
you
1:17:27
have
1:17:27
for
1:17:27
the
1:17:28
retailers
1:17:28
in
1:17:28
trying
1:17:28
to
1:17:29
adjust
1:17:29
to
1:17:29
those
1:17:30
changing
1:17:30
consumer
1:17:31
predictions,
1:17:31
so
1:17:31
to
1:17:32
speak.
1:17:32
So
1:17:32
certainly
1:17:33
something
1:17:33
we're
1:17:33
seeing
1:17:34
is
1:17:35
customers
1:17:35
are
1:17:35
becoming
1:17:36
far
1:17:36
less
1:17:37
tolerant
1:17:37
of
1:17:37
friction.
1:17:38
So
1:17:38
anything
1:17:39
that
1:17:39
makes
1:17:39
a
1:17:39
customer's
1:17:40
life
1:17:40
hard,
1:17:41
they're
1:17:41
like,
1:17:41
no,
1:17:41
don't
1:17:42
want
1:17:42
to
1:17:42
do
1:17:42
it.
1:17:42
And
1:17:43
where
1:17:43
we're
1:17:43
at
1:17:43
at
1:17:43
the
1:17:43
moment
1:17:44
is
1:17:44
there's
1:17:44
choice.
1:17:45
Customers
1:17:45
have
1:17:45
choice.
1:17:46
So
1:17:46
they
1:17:46
don't
1:17:46
have
1:17:47
to
1:17:47
have
1:17:47
that,
1:17:47
that
1:17:48
collection
1:17:48
experience.
1:17:49
They
1:17:49
can
1:17:49
go
1:17:49
somewhere
1:17:50
else
1:17:50
to
1:17:50
a
1:17:50
different
1:17:50
retailer
1:17:51
and
1:17:51
have
1:17:52
a
1:17:52
much
1:17:52
better
1:17:52
experience.
1:17:54
So
1:17:54
having
1:17:54
click
1:17:55
and
1:17:55
collect
1:17:55
and
1:17:55
having
1:17:55
parcel
1:17:56
pickups
1:17:57
no
1:17:57
longer
1:17:57
viewed
1:17:58
as
1:17:58
a,
1:17:58
as
1:17:58
a
1:17:58
nice
1:17:59
to
1:17:59
have
1:17:59
alternative
1:18:00
to
1:18:00
home
1:18:00
delivery,
1:18:01
this
1:18:01
has
1:18:01
to
1:18:01
be
1:18:01
a
1:18:02
core
1:18:02
part
1:18:03
of
1:18:03
what
1:18:03
a
1:18:03
retailer
1:18:04
offers.
1:18:05
Customers
1:18:06
want
1:18:06
to
1:18:06
know
1:18:06
that
1:18:06
their
1:18:06
order
1:18:07
is
1:18:07
ready.
1:18:07
They
1:18:07
want
1:18:08
to
1:18:08
know
1:18:08
that
1:18:08
they
1:18:08
can
1:18:09
collect
1:18:09
it
1:18:09
on
1:18:09
their
1:18:10
terms
1:18:10
and
1:18:11
be
1:18:11
in
1:18:11
and
1:18:11
out
1:18:11
without
1:18:11
queues,
1:18:12
without
1:18:13
any
1:18:13
confusion
1:18:13
or
1:18:13
having
1:18:14
to
1:18:14
wait
1:18:14
for
1:18:14
a
1:18:14
colleague
1:18:15
to
1:18:15
come
1:18:15
over
1:18:15
and
1:18:16
assist
1:18:16
them.
1:18:16
So
1:18:17
that
1:18:17
self
1:18:17
service,
1:18:17
I
1:18:18
want
1:18:18
to
1:18:18
go
1:18:18
in,
1:18:18
collect
1:18:19
and
1:18:19
leave.
1:18:20
Also
1:18:21
getting
1:18:21
those
1:18:21
customers
1:18:22
used
1:18:22
to
1:18:22
and
1:18:22
being
1:18:23
more
1:18:23
comfortable
1:18:23
to
1:18:24
collect
1:18:24
in
1:18:24
store
1:18:25
as
1:18:26
parcel
1:18:26
destinations,
1:18:27
not
1:18:28
just
1:18:28
for
1:18:28
retail
1:18:28
orders,
1:18:29
but
1:18:29
again
1:18:29
for
1:18:29
those
1:18:30
carrier
1:18:30
orders
1:18:31
or
1:18:31
for
1:18:31
those
1:18:31
marketplace
1:18:32
parcels
1:18:33
to
1:18:33
you,
1:18:33
Customers
1:18:33
are
1:18:34
going
1:18:34
to
1:18:34
want
1:18:34
to
1:18:34
collect
1:18:34
everything
1:18:35
in
1:18:35
one
1:18:35
place.
1:18:35
And
1:18:36
how
1:18:36
can
1:18:36
we
1:18:36
provide
1:18:37
solutions
1:18:37
that
1:18:38
allow
1:18:38
retailers
1:18:39
to
1:18:39
offer
1:18:39
that
1:18:40
kind
1:18:40
of
1:18:40
one
1:18:40
stop
1:18:41
shop
1:18:41
where
1:18:41
everything
1:18:42
can
1:18:42
be
1:18:42
collected?
1:18:43
Convenience
1:18:43
at
1:18:43
the
1:18:44
end
1:18:44
of
1:18:44
day
1:18:44
is
1:18:44
going
1:18:44
to
1:18:44
win.
1:18:45
So
1:18:45
stores
1:18:46
that
1:18:46
can
1:18:46
offer
1:18:46
the
1:18:47
extended
1:18:47
hours,
1:18:48
that
1:18:48
can
1:18:48
simplify
1:18:49
that
1:18:49
pickup
1:18:49
journey
1:18:50
and
1:18:50
be
1:18:51
consistent
1:18:52
with
1:18:52
availability,
1:18:53
they'll
1:18:53
naturally
1:18:54
attract
1:18:55
this
1:18:55
repeat
1:18:55
footfall.
1:18:56
And
1:18:56
while
1:18:57
those
1:18:57
that
1:18:57
rely
1:18:57
heavily
1:18:58
on
1:18:58
the
1:18:58
manual
1:18:58
process,
1:18:59
they're
1:18:59
going
1:18:59
to
1:18:59
struggle
1:18:59
because
1:19:00
if
1:19:01
you
1:19:01
have
1:19:01
a
1:19:01
task
1:19:01
that's
1:19:02
repetitive,
1:19:02
let's
1:19:03
automate
1:19:03
it
1:19:03
with
1:19:03
these
1:19:04
robotic
1:19:04
collection
1:19:05
machines,
1:19:05
these
1:19:05
parcel
1:19:06
collection
1:19:06
machines,
1:19:07
because
1:19:07
you
1:19:07
don't
1:19:07
want
1:19:08
to
1:19:08
tie
1:19:08
up
1:19:08
your
1:19:08
resources
1:19:09
doing
1:19:09
the
1:19:09
same
1:19:09
thing
1:19:10
in
1:19:10
the
1:19:10
same
1:19:10
way.
1:19:11
Technology
1:19:12
can
1:19:12
do
1:19:12
that
1:19:12
for
1:19:13
you.
1:19:13
So
1:19:14
I
1:19:14
think
1:19:14
for
1:19:14
retailers
1:19:15
the
1:19:15
advice
1:19:15
would
1:19:15
be
1:19:16
look
1:19:16
at
1:19:16
scaling
1:19:17
now
1:19:18
even
1:19:18
if
1:19:18
today's
1:19:19
volume
1:19:19
feels
1:19:19
manageable
1:19:20
to
1:19:20
you.
1:19:21
Peak
1:19:21
is
1:19:22
no
1:19:22
longer
1:19:22
going
1:19:22
to
1:19:22
be
1:19:22
Christmas
1:19:23
or
1:19:23
just
1:19:23
Black
1:19:23
Friday
1:19:24
and
1:19:24
Cyber
1:19:24
Monday.
1:19:25
It's
1:19:25
going
1:19:25
to
1:19:25
be
1:19:25
more
1:19:26
frequent
1:19:26
and
1:19:26
more
1:19:26
unpredictable.
1:19:27
And
1:19:28
what
1:19:28
that
1:19:28
does
1:19:29
at
1:19:29
the
1:19:29
end
1:19:29
of
1:19:29
the
1:19:29
day
1:19:30
will
1:19:30
put
1:19:30
pressure
1:19:30
on
1:19:30
your
1:19:31
in
1:19:31
store
1:19:31
teams.
1:19:32
And
1:19:32
that's
1:19:32
what
1:19:32
we
1:19:32
want
1:19:33
to
1:19:33
avoid
1:19:33
because
1:19:33
that's
1:19:34
when
1:19:34
you
1:19:34
start
1:19:34
losing
1:19:34
the
1:19:35
customer
1:19:35
experience.
1:19:35
That's
1:19:36
where
1:19:36
you
1:19:36
start
1:19:36
losing
1:19:37
that
1:19:39
consistency
1:19:40
that
1:19:40
customers
1:19:40
come
1:19:40
to
1:19:41
expect.
1:19:41
Giving
1:19:42
them
1:19:42
that
1:19:42
self
1:19:42
service
1:19:43
option
1:19:43
as
1:19:43
a
1:19:43
customer
1:19:44
that
1:19:44
helps
1:19:44
you
1:19:45
create
1:19:45
that
1:19:45
resilience
1:19:46
in
1:19:46
the
1:19:46
operation
1:19:47
and
1:19:47
not
1:19:47
just
1:19:48
looking
1:19:48
at
1:19:48
the
1:19:48
speed
1:19:49
in
1:19:49
the
1:19:49
front
1:19:50
end.
1:19:51
And
1:19:51
I
1:19:51
think
1:19:51
in
1:19:51
terms
1:19:52
of
1:19:52
retailers,
1:19:52
space
1:19:52
is
1:19:53
going
1:19:53
to
1:19:53
be
1:19:53
key.
1:19:54
Every
1:19:54
square
1:19:54
meter
1:19:55
in
1:19:55
a
1:19:55
store
1:19:56
has
1:19:56
to
1:19:56
work
1:19:57
and
1:19:57
has
1:19:57
to
1:19:57
generate
1:19:58
revenue
1:19:58
for
1:19:59
a
1:19:59
store.
1:19:59
So
1:19:59
when
1:20:00
we
1:20:00
look
1:20:00
at
1:20:00
store
1:20:00
planning
1:20:01
teams,
1:20:01
everyone's
1:20:02
fighting
1:20:02
for
1:20:03
space
1:20:03
within
1:20:03
store.
1:20:04
Do
1:20:04
we
1:20:04
put
1:20:04
merchandise
1:20:05
there
1:20:05
to
1:20:05
sell?
1:20:06
Are
1:20:06
stores
1:20:06
going
1:20:07
to
1:20:07
put
1:20:07
in
1:20:07
self
1:20:07
service
1:20:07
checkouts?
1:20:08
Are
1:20:08
they
1:20:08
going
1:20:08
to
1:20:08
put
1:20:09
in
1:20:09
a
1:20:09
collection
1:20:10
robot
1:20:11
through
1:20:11
Cleveron?
1:20:12
And
1:20:12
what
1:20:12
we
1:20:12
want
1:20:13
to
1:20:13
do
1:20:13
with
1:20:13
retailers
1:20:13
is
1:20:14
help
1:20:14
with
1:20:14
that
1:20:14
return
1:20:14
and
1:20:15
investment
1:20:15
calculation
1:20:16
as
1:20:16
to
1:20:17
how
1:20:17
you
1:20:17
can
1:20:17
best
1:20:18
make
1:20:18
use
1:20:18
of
1:20:18
that
1:20:19
space.
1:20:19
And
1:20:19
this
1:20:20
also
1:20:20
comes
1:20:20
down
1:20:20
to
1:20:20
our
1:20:21
own
1:20:21
R
1:20:21
and
1:20:21
D.
1:20:21
We
1:20:22
want
1:20:22
to
1:20:22
make
1:20:22
a
1:20:22
smaller
1:20:23
footprint
1:20:23
but
1:20:23
higher
1:20:24
volume
1:20:24
so
1:20:25
you
1:20:25
can
1:20:25
pack
1:20:25
in
1:20:25
as
1:20:26
many
1:20:26
parcels
1:20:26
as
1:20:26
you
1:20:26
can
1:20:27
in
1:20:27
that
1:20:27
small
1:20:27
footprint.
1:20:28
Because
1:20:28
we
1:20:28
understand
1:20:28
that
1:20:29
as
1:20:29
demand
1:20:30
change
1:20:30
and
1:20:30
as
1:20:31
you
1:20:31
can
1:20:31
flex,
1:20:32
that's
1:20:32
going
1:20:32
to
1:20:32
be
1:20:32
critical
1:20:33
whether
1:20:33
it's
1:20:34
the
1:20:34
smart
1:20:34
lockers
1:20:35
doing
1:20:35
that,
1:20:36
quietly
1:20:36
absorbing
1:20:37
that
1:20:37
parcel
1:20:38
growth
1:20:38
without
1:20:39
adding
1:20:39
that
1:20:39
complexity
1:20:40
to
1:20:40
the
1:20:40
store.
1:20:41
Well,
1:20:41
I'm
1:20:42
really
1:20:42
excited
1:20:42
Mark
1:20:42
to
1:20:43
announce
1:20:43
that
1:20:44
we're
1:20:45
going
1:20:45
to
1:20:45
be
1:20:45
part
1:20:45
of
1:20:46
a
1:20:46
very
1:20:46
special
1:20:47
tour
1:20:48
of
1:20:48
the
1:20:48
Zara
1:20:49
flagship
1:20:50
store
1:20:50
at
1:20:50
Hudson
1:20:51
Yards
1:20:51
where
1:20:51
retailers
1:20:52
at
1:20:52
NRF
1:20:53
will
1:20:53
be
1:20:53
able
1:20:53
to
1:20:53
join
1:20:54
us
1:20:55
and
1:20:55
Cleveron
1:20:56
for
1:20:57
a
1:20:57
one
1:20:57
on
1:20:58
one
1:20:58
tour
1:20:58
of
1:20:59
this
1:20:59
technology
1:20:59
to
1:21:00
see
1:21:00
it
1:21:00
live
1:21:00
in
1:21:01
person,
1:21:01
to
1:21:01
try
1:21:01
it
1:21:02
for
1:21:02
themselves.
1:21:04
Mark,
1:21:04
maybe
1:21:04
tell
1:21:05
our
1:21:05
audience
1:21:05
a
1:21:05
little
1:21:05
bit
1:21:06
more
1:21:06
about
1:21:06
what,
1:21:07
what
1:21:08
the
1:21:08
retailers
1:21:09
participating
1:21:09
in
1:21:09
that
1:21:10
might
1:21:10
expect
1:21:11
from
1:21:11
that
1:21:11
experience
1:21:13
and
1:21:13
then
1:21:14
where
1:21:14
people
1:21:15
can
1:21:15
find
1:21:15
you
1:21:15
out
1:21:16
at
1:21:16
nrf.
1:21:17
Yeah,
1:21:17
I
1:21:17
mean
1:21:18
this
1:21:18
is
1:21:18
an
1:21:18
experience
1:21:19
that
1:21:19
you
1:21:19
don't
1:21:19
get
1:21:19
very
1:21:19
often.
1:21:20
I
1:21:20
mean
1:21:20
I
1:21:20
work
1:21:20
for
1:21:21
CLEVR
1:21:21
on
1:21:21
and
1:21:21
I
1:21:21
haven't
1:21:22
had
1:21:22
a
1:21:22
chance
1:21:22
to
1:21:22
do
1:21:22
this
1:21:23
yet.
1:21:23
So
1:21:23
what
1:21:24
we
1:21:24
have
1:21:24
is
1:21:25
a
1:21:25
really
1:21:25
exciting
1:21:25
opportunity
1:21:26
at
1:21:27
the
1:21:28
Hudson
1:21:28
Yard
1:21:29
store
1:21:29
of
1:21:30
Zara,
1:21:30
which
1:21:30
is
1:21:31
their
1:21:31
flagship
1:21:31
store
1:21:31
in
1:21:32
New
1:21:32
York
1:21:33
on
1:21:33
Monday
1:21:33
12th
1:21:34
January
1:21:34
between
1:21:35
4:30
1:21:35
and
1:21:36
5:30
1:21:37
we're
1:21:37
going
1:21:37
to
1:21:37
be
1:21:37
having
1:21:38
a
1:21:38
small
1:21:38
selected
1:21:39
number
1:21:40
of
1:21:40
people
1:21:41
come
1:21:41
to
1:21:41
the
1:21:42
store,
1:21:42
have
1:21:42
an
1:21:43
introduction
1:21:43
from
1:21:44
some
1:21:44
of
1:21:44
the
1:21:44
Zara
1:21:45
team
1:21:45
to
1:21:45
talk
1:21:45
about
1:21:46
why
1:21:46
they've
1:21:46
used
1:21:46
Cleveron
1:21:47
and
1:21:47
then
1:21:48
we've
1:21:48
got
1:21:48
a
1:21:48
really
1:21:48
great
1:21:49
lineup.
1:21:49
So
1:21:49
Indrik,
1:21:50
who's
1:21:50
one
1:21:50
of
1:21:50
our
1:21:51
co
1:21:51
founders
1:21:51
at
1:21:51
Cleveron,
1:21:52
he's
1:21:52
going
1:21:52
to
1:21:52
be
1:21:52
there
1:21:53
and
1:21:53
he's
1:21:53
going
1:21:53
to
1:21:53
take
1:21:53
us
1:21:54
around
1:21:54
to
1:21:54
the,
1:21:54
to
1:21:55
the
1:21:55
robotic
1:21:55
locker,
1:21:56
show
1:21:56
us
1:21:57
how
1:21:57
it
1:21:57
works,
1:21:58
explain
1:21:58
how
1:21:59
he
1:21:59
got
1:21:59
to
1:22:00
create
1:22:00
and
1:22:00
got
1:22:01
to
1:22:01
the
1:22:01
stage
1:22:01
where
1:22:01
we
1:22:01
have
1:22:02
this
1:22:02
amazing
1:22:02
piece
1:22:03
of
1:22:03
technology
1:22:03
and
1:22:04
look
1:22:04
at
1:22:04
it
1:22:04
in
1:22:04
a
1:22:04
live
1:22:05
flagship
1:22:06
environment.
1:22:07
We
1:22:07
get
1:22:07
to
1:22:07
ask
1:22:08
questions
1:22:09
to
1:22:09
Indrik,
1:22:09
gain
1:22:10
that
1:22:10
real
1:22:10
behind
1:22:11
the
1:22:11
scenes
1:22:11
view
1:22:12
of
1:22:12
how
1:22:12
does
1:22:12
it
1:22:12
work,
1:22:13
why
1:22:13
do
1:22:13
retailers
1:22:14
do
1:22:14
this
1:22:14
but
1:22:14
also
1:22:15
have
1:22:15
that
1:22:15
opportunity
1:22:16
to
1:22:16
look
1:22:16
at
1:22:17
that
1:22:17
technology
1:22:17
and
1:22:17
try
1:22:17
it
1:22:18
for
1:22:18
yourself.
1:22:19
So
1:22:19
in
1:22:19
the
1:22:19
chat
1:22:20
section
1:22:20
underneath
1:22:21
we're
1:22:21
going
1:22:21
to
1:22:21
have
1:22:21
a
1:22:21
link
1:22:22
there
1:22:22
but
1:22:22
also
1:22:22
check
1:22:23
on
1:22:23
our
1:22:23
LinkedIn
1:22:24
pages.
1:22:24
So
1:22:24
either
1:22:25
with
1:22:25
Clevron
1:22:25
or
1:22:26
with
1:22:26
myself,
1:22:27
we're
1:22:27
going
1:22:27
to
1:22:27
share
1:22:28
how
1:22:28
you
1:22:28
can
1:22:28
sign
1:22:29
up
1:22:29
for
1:22:29
that.
1:22:30
Now
1:22:30
don't
1:22:31
panic
1:22:31
everyone,
1:22:31
if
1:22:32
you're
1:22:32
busy
1:22:32
4:30
1:22:33
to
1:22:33
5:30
1:22:33
on
1:22:34
Monday
1:22:34
12th
1:22:34
January,
1:22:35
we
1:22:35
are
1:22:36
also
1:22:36
going
1:22:36
to
1:22:36
be
1:22:36
giving
1:22:36
you
1:22:37
the
1:22:37
chance
1:22:37
to
1:22:37
go
1:22:37
to
1:22:38
the
1:22:38
store
1:22:38
and
1:22:38
experience
1:22:39
it
1:22:39
in
1:22:39
your
1:22:39
own
1:22:39
time
1:22:40
over
1:22:40
those
1:22:41
three
1:22:41
days
1:22:41
of
1:22:41
the
1:22:41
nrf.
1:22:42
So
1:22:43
throughout
1:22:43
the
1:22:44
the
1:22:44
NRF
1:22:44
we're
1:22:45
going
1:22:45
to
1:22:45
have
1:22:45
some
1:22:45
QR
1:22:46
codes
1:22:46
that
1:22:46
we
1:22:46
can
1:22:46
give
1:22:47
out
1:22:47
to
1:22:47
different
1:22:48
customers.
1:22:48
You
1:22:49
can
1:22:49
go
1:22:49
along
1:22:49
to
1:22:49
Zara,
1:22:50
scan
1:22:50
it
1:22:51
and
1:22:51
you
1:22:51
can
1:22:51
have
1:22:51
a
1:22:51
go
1:22:52
at
1:22:52
the
1:22:52
machine
1:22:53
yourself.
1:22:53
And
1:22:53
you
1:22:54
never
1:22:54
know,
1:22:54
there
1:22:54
may
1:22:55
be
1:22:55
a
1:22:55
small
1:22:55
surprise
1:22:56
waiting
1:22:56
for
1:22:56
you.
1:22:57
You
1:22:57
never
1:22:57
know.
1:22:58
I
1:22:58
can't
1:22:58
promise
1:22:58
anything
1:22:59
but,
1:22:59
but
1:23:00
if.
1:23:00
I
1:23:00
know,
1:23:01
if
1:23:01
I
1:23:01
know
1:23:01
anything
1:23:01
about
1:23:02
the
1:23:02
Estonians,
1:23:03
they
1:23:03
are
1:23:03
very
1:23:03
hospitable
1:23:04
people
1:23:05
and
1:23:05
have
1:23:05
always
1:23:06
brought
1:23:07
the
1:23:07
surprise
1:23:08
treats.
1:23:09
So
1:23:09
I
1:23:09
love
1:23:09
that
1:23:10
Mark
1:23:11
like
1:23:11
as
1:23:12
like
1:23:12
Mark
1:23:12
said,
1:23:12
we'll
1:23:13
put
1:23:13
the
1:23:13
link
1:23:13
to
1:23:14
the
1:23:14
registration.
1:23:15
If
1:23:16
you're
1:23:16
interested,
1:23:16
you
1:23:17
can
1:23:17
sign
1:23:17
up
1:23:17
there.
1:23:18
And
1:23:19
as
1:23:19
he
1:23:19
mentioned,
1:23:19
there'll
1:23:19
be
1:23:20
a
1:23:20
select
1:23:20
few
1:23:20
during
1:23:21
that
1:23:21
Monday
1:23:22
night
1:23:22
event,
1:23:23
but
1:23:23
there
1:23:24
will
1:23:24
be
1:23:24
an
1:23:24
opportunity
1:23:25
to
1:23:25
open
1:23:25
up
1:23:26
if
1:23:26
we
1:23:26
have
1:23:27
interest.
1:23:27
So
1:23:28
Mark,
1:23:28
thank
1:23:28
you
1:23:29
so
1:23:29
much
1:23:29
for
1:23:29
your
1:23:29
time
1:23:30
today.
1:23:30
We're
1:23:30
excited
1:23:31
to
1:23:31
see
1:23:31
Cleveron
1:23:32
out
1:23:33
at
1:23:33
nrf.
1:23:33
Excellent.
1:23:34
Thank
1:23:34
you
1:23:34
very
1:23:34
much
1:23:34
for
1:23:35
having
1:23:35
me
1:23:35
and
1:23:35
look
1:23:35
forward
1:23:35
to
1:23:36
seeing
1:23:36
you
1:23:36
all
1:23:36
in
1:23:36
New
1:23:36
York.
1:23:37
Well,
1:23:37
and
1:23:37
I
1:23:38
guess
1:23:38
that
1:23:38
wraps
1:23:38
up
1:23:39
another
1:23:39
NRF
1:23:40
tech
1:23:40
preview.
1:23:40
We're
1:23:41
thankful
1:23:41
to
1:23:41
all
1:23:41
our
1:23:42
guests
1:23:42
and
1:23:42
to
1:23:43
those
1:23:43
of
1:23:43
you
1:23:43
who
1:23:43
are
1:23:43
able
1:23:44
to
1:23:44
join
1:23:44
us
1:23:44
live
1:23:44
today
1:23:45
and
1:23:45
those
1:23:46
listening
1:23:46
on
1:23:46
their
1:23:47
way
1:23:47
even
1:23:47
to
1:23:48
NRF
1:23:48
this
1:23:48
weekend.
1:23:49
We'll
1:23:49
be
1:23:49
heading
1:23:49
out
1:23:49
shortly
1:23:50
ourselves
1:23:50
and
1:23:50
you
1:23:50
can
1:23:51
catch
1:23:51
our
1:23:51
full
1:23:52
live
1:23:52
coverage
1:23:52
of
1:23:52
NRF
1:23:53
brought
1:23:53
to
1:23:53
you
1:23:53
by
1:23:54
the
1:23:54
Fusion
1:23:54
group
1:23:55
beginning
1:23:55
this
1:23:55
weekend
1:23:56
and
1:23:56
available
1:23:56
to
1:23:56
you
1:23:57
on
1:23:57
LinkedIn
1:23:58
or
1:23:58
our
1:23:58
YouTube
1:23:59
channel
1:23:59
at
1:24:00
YouTube.com
1:24:02
omnitalk
1:24:03
retail
1:24:03
or
1:24:03
of
1:24:03
course
1:24:04
and
1:24:04
wherever
1:24:05
you
1:24:05
get
1:24:05
your
1:24:05
podcast
1:24:06
or
1:24:07
even
1:24:07
better
1:24:08
if
1:24:08
you
1:24:08
want
1:24:08
to
1:24:08
stop
1:24:09
by
1:24:09
and
1:24:09
say
1:24:09
hello
1:24:09
to
1:24:10
Ann
1:24:10
and
1:24:10
to
1:24:10
myself.
1:24:11
You
1:24:11
can
1:24:11
see
1:24:11
us
1:24:11
at
1:24:11
booth
1:24:12
4921.
1:24:13
Every
1:24:14
time
1:24:14
one
1:24:14
of
1:24:14
you
1:24:14
stops
1:24:14
by,
1:24:15
I
1:24:15
gotta
1:24:15
say,
1:24:16
it
1:24:16
just
1:24:16
melts
1:24:17
and
1:24:17
warms
1:24:17
our
1:24:18
hearts.
1:24:18
So
1:24:18
until
1:24:18
then,
1:24:19
travel
1:24:19
safe
1:24:20
this
1:24:20
weekend.
1:24:21
And
1:24:21
be
1:24:21
careful
1:24:21
out
1:24:21
there.
1:24:22
There.