Latest / Omni Talk Retail / Footlongs, Fresh Produce & A Side Of Legislation | Fast Five

Transcript

Footlongs, Fresh Produce & A Side Of Legislation | Fast Five · Omni Talk Retail

0:00
Hello
0:00
and
0:00
welcome
0:01
to
0:01
the
0:01
Omnitalk
0:01
Retail
0:02
Fast
0:02
5,
0:02
which
0:03
comes
0:03
to
0:03
you
0:03
by
0:03
way
0:03
of
0:03
the
0:04
Omnitalk
0:04
retail
0:05
podcast
0:06
network.
0:06
I
0:06
am
0:07
Chris
0:07
Walton.
0:07
I
0:07
am
0:08
remote
0:08
from
0:08
Barcelona,
0:09
Spain,
0:10
taking
0:10
a
0:10
break
0:11
from
0:11
the
0:11
Shop
0:11
Talk
0:12
Europe
0:12
conference
0:13
to
0:13
record
0:13
this
0:14
week's
0:14
podcast.
0:15
And
0:15
you
0:15
know,
0:16
you
0:16
know,
0:16
with
0:16
so
0:17
much
0:17
talk
0:17
of
0:17
AI
0:18
and
0:18
delivery
0:19
speed
0:19
enhancements
0:20
over
0:20
the
0:20
past
0:21
few
0:22
Fast5
0:22
podcasts
0:23
and
0:23
seeing
0:24
the
0:24
headlines
0:24
on
0:24
the
0:24
docket
0:25
again
0:25
this
0:25
week,
0:26
I
0:26
just
0:27
said
0:27
to
0:27
myself,
0:27
I
0:27
said,
0:28
you
0:28
know
0:28
what?
0:28
We
0:29
need
0:29
to
0:29
bring
0:29
in
0:30
another
0:30
big
0:31
gun
0:31
to
0:31
co
0:32
host
0:32
this
0:32
week's
0:33
pod.
0:33
And
0:33
I
0:33
think,
0:34
you
0:34
know,
0:34
everybody,
0:35
I
0:35
think
0:35
I
0:35
really
0:35
hit
0:36
the
0:36
guest
0:36
lottery
0:36
this
0:37
time.
0:37
I
0:37
think
0:37
I
0:37
hit
0:37
the
0:37
guest
0:38
host
0:38
lottery
0:38
because
0:39
Chap
0:40
Aiken,
0:41
the
0:41
Gartner
0:42
analyst
0:42
for
0:42
digital
0:42
commerce
0:43
and
0:43
supply
0:44
chain,
0:44
joined
0:45
me
0:45
to
0:45
break
0:45
down
0:46
this
0:46
week's
0:46
headlines.
0:47
Someone.
0:48
Someone
0:48
whose
0:49
job
0:49
it
0:49
literally
0:50
is
0:51
to
0:51
provide
0:51
insight
0:52
and
0:52
research
0:52
on
0:53
how
0:53
digital
0:53
commerce
0:54
and
0:54
supply
0:55
chain
0:55
retail
0:55
ecosystems
0:56
are
0:56
evolving.
0:58
Sounds
0:59
like
0:59
he's
0:59
tailor
1:00
made
1:00
for
1:00
us
1:00
here
1:01
at
1:01
omnitok,
1:01
doesn't
1:02
it,
1:02
everybody?
1:02
So
1:03
sit
1:03
back,
1:03
enjoy
1:04
and
1:04
think
1:04
about
1:04
what
1:04
Chap
1:05
and
1:05
I
1:05
got
1:06
right
1:06
and
1:06
wrong
1:06
this
1:07
week
1:07
as
1:07
you
1:08
decide
1:08
for
1:09
yourself
1:09
where
1:09
the
1:10
future
1:10
of
1:10
retail
1:11
is
1:11
headed.
1:12
Producer
1:12
Ella,
1:13
let's
1:13
cue
1:14
the
1:14
tape.
1:15
Uber
1:16
eats
1:16
and
1:17
DoorDash.
1:18
I
1:18
hope
1:18
you
1:18
are
1:19
paying
1:19
attention
1:20
because,
1:20
wow,
1:21
a
1:21
shot
1:22
has
1:22
been
1:22
fired
1:23
across
1:23
your
1:24
bow.
1:25
I
1:25
think
1:25
there
1:25
is
1:25
some
1:26
aspect
1:26
of
1:26
the
1:27
future
1:27
of
1:27
retail
1:28
online
1:28
merchandising
1:29
being
1:29
driven
1:29
than
1:30
buy
1:30
content.
1:31
I
1:31
think
1:31
it's
1:32
sad
1:32
that
1:32
we
1:32
need
1:33
legislation
1:33
to
1:34
deal
1:34
with
1:34
these
1:34
kinds
1:35
of
1:35
issues.
1:36
Watch
1:36
out,
1:36
world.
1:37
Walmart
1:37
seems
1:38
to
1:38
have
1:38
it
1:38
dialed
1:38
in
1:39
right
1:39
now.
1:39
We
1:40
need
1:40
lots
1:40
of
1:40
scale
1:41
on
1:41
the
1:41
consumer
1:41
side
1:42
for
1:42
that
1:42
monetization
1:43
to
1:43
work.
1:44
And
1:44
right
1:44
now,
1:45
I
1:45
don't
1:45
see
1:46
the
1:46
compelling
1:47
consumer
1:47
angle
1:47
that
1:48
drives
1:48
that
1:48
kind
1:48
of
1:49
scale.
1:51
All
1:51
right,
1:52
Chap
1:52
Akin,
1:53
longtime
1:53
fan
1:53
of
1:53
the
1:54
show,
1:54
longtime
1:54
LinkedIn
1:55
pal
1:55
of
1:56
mine.
1:56
How
1:57
are
1:57
you,
1:59
Chris?
1:59
I
1:59
am
2:00
great.
2:01
Is
2:02
an
2:02
incredible
2:02
time
2:03
to
2:03
be
2:03
covering
2:04
retail.
2:04
So
2:05
I
2:05
am
2:05
super
2:05
excited
2:06
to
2:06
be
2:06
on
2:07
your
2:07
podcast.
2:08
I,
2:09
as
2:09
a
2:09
fan
2:09
of
2:10
this
2:10
show,
2:10
I
2:11
actually
2:11
get
2:11
a
2:12
lot
2:12
of
2:12
my
2:12
retail
2:13
news
2:13
from
2:13
this
2:13
show.
2:14
So
2:14
I
2:14
found
2:15
it
2:15
surreal
2:15
that
2:16
I'm
2:16
going
2:16
to
2:16
be
2:17
your
2:17
co
2:18
host.
2:18
I'm
2:18
honored
2:19
to
2:19
be
2:19
here.
2:20
I
2:20
just
2:20
want
2:20
to
2:21
tell
2:21
you
2:21
that
2:21
I
2:21
actually
2:21
listened
2:22
to
2:22
this
2:22
show
2:23
on
2:23
my
2:23
long
2:24
road
2:25
and
2:25
gravel
2:25
bikes
2:26
as
2:26
a
2:26
source
2:27
of
2:27
idea
2:28
inspiration
2:28
for
2:28
my
2:29
research.
2:29
So
2:30
there
2:30
you
2:30
go.
2:31
Really?
2:31
All
2:31
right.
2:32
Wow.
2:32
What
2:32
an
2:32
endorsement
2:33
to
2:33
start
2:34
off.
2:34
Thank
2:34
you,
2:35
man.
2:35
That's
2:36
so
2:36
great.
2:36
That's
2:36
so
2:37
great.
2:37
And
2:37
you're,
2:37
you're
2:38
a
2:38
fellow
2:38
Minnesotan
2:39
too,
2:39
right?
2:39
Am
2:39
I,
2:39
am
2:40
I,
2:40
am
2:40
I
2:40
not
2:40
correct
2:41
in
2:41
saying
2:41
that,
2:42
that's
2:42
right.
2:43
Red
2:43
Wing,
2:43
Minnesota.
2:44
Yeah,
2:44
Red
2:45
Wing.
2:45
Just
2:45
right
2:45
down
2:46
the
2:46
road.
2:46
So,
2:46
so
2:47
tell
2:47
our
2:47
audience
2:47
about
2:47
yourself
2:48
and,
2:48
and
2:49
also
2:49
about
2:49
Gardner
2:49
because
2:50
I
2:50
think,
2:50
you
2:51
know,
2:51
you
2:51
know,
2:51
there's
2:52
a.
2:52
You
2:52
can
2:52
go,
2:53
you
2:53
can
2:53
be
2:53
in
2:53
retail
2:54
for
2:54
a
2:54
long
2:54
time
2:54
and
2:54
not
2:55
understand
2:55
how
2:55
Gartner
2:56
fits
2:56
into
2:56
the
2:56
whole
2:56
ecosystem.
2:57
So
2:57
I'd
2:57
love
2:57
for
2:57
you
2:57
to
2:58
touch
2:58
on
2:58
that
2:58
too.
2:59
Yeah,
2:59
absolutely.
3:01
So
3:01
Gartner
3:02
supports
3:02
retailers
3:03
on
3:04
their
3:04
mission
3:04
critical
3:05
priorities.
3:06
And
3:06
now
3:06
Gartner
3:07
is
3:08
mostly
3:08
known
3:08
for
3:09
like
3:09
their
3:09
magic
3:10
quadrants
3:10
and
3:10
their
3:11
hype
3:11
cycles.
3:11
I'll
3:12
actually
3:12
be
3:12
doing
3:12
a
3:13
magic
3:13
quadrant
3:13
this
3:14
year
3:14
myself.
3:15
But
3:15
we
3:15
provide
3:16
insights
3:16
across
3:16
marketing,
3:17
hr,
3:18
finance,
3:18
IT
3:19
and
3:19
of
3:20
course
3:20
supply
3:20
chain.
3:20
That's
3:21
the
3:21
practice
3:21
that
3:21
I'm.
3:23
And
3:23
in
3:23
this
3:24
crazy
3:24
world
3:24
of
3:24
AI,
3:25
our
3:25
job
3:26
is
3:26
to
3:26
help
3:26
clients
3:27
see
3:27
around
3:28
corners
3:28
as
3:29
they
3:29
navigate
3:29
this
3:30
crazy
3:30
world
3:30
that
3:30
we're
3:31
in
3:31
right
3:31
now.
3:32
My
3:32
coverage
3:33
is
3:33
specifically
3:33
a
3:34
few
3:34
areas.
3:34
It's
3:35
the
3:35
intersection
3:35
of
3:35
digital
3:36
commerce
3:36
and
3:36
supply
3:37
chain.
3:38
I
3:38
cover
3:38
a
3:38
software
3:39
market,
3:39
distributed
3:40
order
3:40
management,
3:41
I
3:41
cover
3:41
retail
3:42
returns
3:43
and
3:43
I
3:43
help
3:44
B2B
3:44
and
3:44
retailers
3:45
with
3:45
order
3:46
management
3:46
technology
3:47
and
3:47
process.
3:48
And
3:48
for
3:48
retailers,
3:49
that's
3:49
stuff
3:49
like
3:50
how
3:50
to
3:50
get
3:51
better
3:51
at
3:51
omnichannel
3:52
fulfillment.
3:52
For
3:53
B2B,
3:53
that
3:53
might
3:54
be
3:54
how
3:54
do
3:55
I
3:55
do,
3:55
how
3:55
do
3:55
I
3:55
execute
3:56
real
3:56
time
3:56
inventory
3:57
visibility
3:58
in
3:58
their
3:59
expanding
3:59
sales
4:00
channels
4:01
and
4:01
of
4:01
course,
4:01
where
4:02
and
4:02
how
4:02
they
4:02
should
4:03
be
4:03
investing
4:03
in
4:04
AI.
4:05
And
4:05
for
4:06
me
4:06
that
4:06
means
4:06
I
4:06
also
4:07
get
4:07
to
4:07
cover
4:07
agentic
4:08
commerce
4:09
as
4:09
an
4:09
area,
4:09
which
4:10
from
4:10
a
4:10
supply
4:10
chain
4:10
perspective
4:11
is
4:12
fascinating.
4:13
So
4:13
prior
4:14
to
4:14
Gartner,
4:15
I
4:15
spent
4:15
a
4:15
good
4:16
25
4:16
years
4:16
in
4:17
retail,
4:18
you
4:18
know,
4:19
at
4:19
two
4:19
great
4:19
Minnesota
4:20
companies.
4:21
I
4:21
spent
4:22
about
4:22
12
4:22
years
4:22
at
4:23
best
4:23
Buy
4:23
where
4:23
I
4:23
led
4:24
Omnichannel
4:25
fulfillment
4:26
back
4:26
in
4:26
2001.
4:27
I
4:27
was
4:28
part
4:28
of
4:28
the
4:28
first
4:29
Buy
4:29
online
4:29
pickup
4:30
experience
4:31
that
4:31
was
4:31
ever
4:32
done
4:32
at
4:32
a
4:32
retailer.
4:33
So
4:33
going
4:33
back
4:34
a
4:34
ways.
4:35
Oh
4:35
my
4:35
God.
4:35
And
4:35
then
4:36
I
4:36
spent.
4:36
That's
4:36
right.
4:37
Spent
4:38
some
4:38
time
4:38
at,
4:38
spent
4:39
about
4:39
a
4:39
decade
4:39
at
4:39
Red
4:40
Wing
4:40
Shoes
4:41
helping
4:41
build
4:42
the
4:42
foundations
4:42
for
4:43
Omnichannel
4:43
E
4:43
commerce
4:44
for
4:44
them
4:44
across
4:45
their
4:45
500
4:46
or
4:46
so
4:46
stores.
4:47
So
4:48
that's
4:48
a
4:49
little
4:49
bit
4:49
about
4:49
me
4:49
and
4:49
Gartner.
4:50
Yeah,
4:50
it's
4:51
great.
4:51
Yeah.
4:51
I
4:51
mean
4:51
you,
4:52
you,
4:52
you
4:52
see
4:52
it
4:53
all
4:53
in
4:53
your
4:53
role.
4:53
There's
4:54
quite
4:54
a
4:54
lot
4:54
under
4:54
your
4:55
remit
4:55
that
4:55
you
4:55
just
4:56
described
4:56
there.
4:56
That's
4:56
why
4:57
I
4:57
think
4:57
you're
4:57
like
4:57
the
4:58
perfect
4:58
host.
4:58
I
4:58
was
4:59
joking
4:59
at
4:59
the
4:59
outset
4:59
that
4:59
I
5:00
think
5:00
I
5:00
hit
5:00
the
5:00
guest
5:00
lottery
5:01
with
5:01
this
5:01
one
5:01
and
5:01
I,
5:01
I
5:02
think,
5:02
I
5:02
think
5:02
that's
5:02
going
5:02
to
5:03
prove
5:03
to
5:03
be
5:03
true.
5:03
But
5:04
chap.
5:04
I'm
5:04
curious,
5:05
please,
5:05
for
5:05
the
5:05
audience
5:06
too,
5:06
because
5:06
we
5:06
have
5:06
a
5:07
lot
5:07
of
5:07
just
5:07
like
5:07
casual
5:08
people
5:08
that
5:08
listen
5:09
to
5:09
our
5:09
podcast
5:09
that
5:09
are
5:10
just
5:10
interested
5:10
in
5:10
retail
5:11
and,
5:11
and
5:12
just
5:12
get
5:12
really
5:13
excited
5:13
about
5:13
hearing
5:14
how
5:14
it's
5:14
going
5:14
to
5:14
unfold
5:15
in
5:15
the
5:15
future.
5:16
What
5:16
is
5:16
the
5:16
quote
5:17
unquote,
5:17
Magic
5:18
Quadrant
5:18
and
5:18
what
5:19
magic
5:19
quadrant
5:19
are
5:20
you
5:20
working
5:20
on?
5:22
So
5:23
Gartner
5:23
does,
5:24
you
5:25
know,
5:25
over
5:26
a
5:26
hundred
5:26
Magic
5:26
Quadrants
5:27
a
5:27
year,
5:27
which
5:27
are
5:28
really,
5:28
you
5:28
know,
5:28
our
5:29
analysis
5:30
of
5:30
a
5:30
given
5:31
software
5:31
market
5:31
and
5:32
who
5:32
are
5:33
the
5:33
top
5:33
companies
5:34
in
5:34
that
5:34
market.
5:35
It's
5:35
actually
5:35
get.
5:36
It
5:36
gets
5:36
confused
5:37
as
5:37
a
5:37
product
5:38
evaluation.
5:38
It's
5:39
really
5:39
a
5:39
vendor
5:39
evaluation
5:40
in
5:41
a
5:41
given
5:41
software
5:42
market.
5:42
So
5:43
for
5:43
me,
5:43
I
5:43
cover
5:43
distributed
5:44
order
5:44
management
5:44
that
5:45
helps
5:45
retailers
5:46
do
5:46
stuff
5:46
like
5:46
ship
5:47
from
5:47
store
5:47
and
5:48
buy
5:48
online,
5:48
pick
5:48
up
5:49
in
5:49
store,
5:50
and
5:51
then
5:51
they
5:51
get
5:51
paired
5:51
with
5:52
a
5:52
critical
5:52
capabilities
5:53
document,
5:53
which
5:54
is
5:54
really
5:54
a
5:54
deep
5:54
dive
5:55
on
5:55
all
5:55
the
5:55
product
5:56
features
5:57
that
5:57
you
5:57
know.
5:57
So
5:58
a
5:58
given
5:59
company
5:59
could
5:59
say,
6:00
well,
6:00
I'm
6:00
not
6:00
interested
6:00
in
6:01
these
6:01
features,
6:02
so
6:02
these
6:02
might
6:02
be
6:02
a
6:03
better
6:03
set
6:03
of
6:03
vendors
6:04
for
6:04
me.
6:04
But
6:04
it
6:04
is
6:04
a,
6:05
it's
6:06
Gartner's
6:07
very
6:07
thorough
6:08
objective,
6:09
you
6:10
know,
6:10
evaluation
6:11
of
6:11
a
6:11
software
6:11
market.
6:13
And
6:13
who
6:13
are
6:14
the
6:14
vendors
6:14
that
6:14
our
6:14
clients
6:15
should
6:15
think,
6:15
you
6:16
know,
6:16
should
6:16
think
6:16
about
6:16
if
6:16
they're
6:17
investing
6:17
in
6:17
that
6:17
market?
6:18
Yeah.
6:18
And
6:19
you
6:19
seem
6:19
like
6:19
the
6:19
right
6:19
person
6:19
for
6:20
the
6:20
job
6:20
given
6:20
that
6:20
you
6:20
have
6:21
click,
6:21
click
6:21
and
6:21
collect
6:22
experience
6:22
going
6:22
back
6:23
to
6:23
2001
6:24
like
6:24
that.
6:25
That's
6:25
incredible.
6:26
That's
6:26
incredible.
6:26
All
6:27
right.
6:27
I
6:27
don't
6:27
normally
6:27
do
6:28
this,
6:28
but
6:28
I
6:28
do
6:28
want
6:28
to
6:29
touch
6:29
on
6:29
a
6:29
topic
6:30
that
6:30
we
6:30
talked
6:30
about
6:31
last
6:31
week
6:31
specifically
6:32
because
6:32
you're
6:33
on
6:33
the
6:33
show
6:33
today,
6:34
and
6:34
that
6:34
is
6:34
the
6:35
Google's
6:35
universal
6:36
cart.
6:37
What's
6:37
your
6:37
whole
6:37
take
6:38
on
6:38
that
6:38
concept?
6:38
Are
6:38
we
6:39
headed
6:39
down
6:39
that
6:39
road
6:40
in
6:40
the
6:40
US
6:40
One
6:41
super
6:41
app
6:41
that
6:41
does
6:41
everything
6:42
for
6:42
us,
6:42
you
6:42
know,
6:43
as
6:43
Google's
6:43
Env
6:44
it
6:44
or,
6:44
you
6:45
know,
6:45
how
6:45
do
6:45
you
6:45
think
6:45
about
6:46
that?
6:47
Yeah,
6:47
you
6:48
know,
6:48
I
6:48
am
6:48
a
6:48
bit
6:49
torn
6:49
on
6:49
this
6:49
one.
6:50
So
6:50
as
6:50
someone
6:50
who
6:51
covers
6:51
agentic
6:52
commerce
6:52
from
6:52
the
6:53
supply
6:53
chain
6:53
side,
6:54
I
6:54
think
6:54
it's
6:55
another
6:55
reason
6:55
retailers
6:56
need
6:56
to
6:56
get
6:57
in
6:57
the
6:57
agentic
6:58
commerce
6:58
game.
6:59
And
7:00
it's
7:00
not
7:01
so
7:01
much
7:01
about
7:01
commerce
7:02
right
7:02
now
7:03
on
7:03
all
7:03
these
7:03
Google
7:04
services,
7:05
but
7:05
being
7:06
discovered
7:07
in
7:07
these
7:07
services
7:08
that
7:08
I
7:08
think
7:09
it's
7:09
really
7:09
a
7:09
discovery
7:10
game.
7:10
It's
7:10
not
7:10
a
7:10
commerce
7:11
game.
7:11
And
7:12
so
7:12
yes,
7:12
it's
7:12
about
7:12
product
7:13
data,
7:13
but
7:13
it's
7:13
also
7:14
about
7:14
supply
7:15
chain
7:15
data.
7:15
So
7:16
people
7:17
are
7:17
asking
7:17
product
7:18
outcome
7:18
questions,
7:19
but
7:19
they're
7:19
asking
7:20
those
7:20
in
7:21
the
7:21
context
7:21
of
7:21
time
7:22
horizons
7:22
for
7:22
those
7:23
outcomes
7:23
as
7:23
well.
7:23
So
7:24
I
7:24
could
7:24
say
7:25
show
7:25
me
7:25
highly,
7:26
highly
7:26
rated
7:26
44
7:27
millimeter
7:27
gravel
7:28
bike
7:28
tires
7:29
that
7:29
I
7:29
can
7:29
get
7:30
by
7:30
Friday.
7:31
Right.
7:31
And
7:32
if
7:32
your
7:32
data
7:32
is
7:33
not
7:33
accurate
7:34
and
7:34
compelling
7:34
and,
7:35
and
7:35
discoverable,
7:36
then
7:37
you're
7:37
not
7:37
going
7:37
to
7:37
be
7:38
considered
7:38
in
7:38
that,
7:38
in
7:39
that
7:39
particular
7:39
query.
7:40
So
7:40
if
7:41
Google's
7:41
making
7:41
discovery
7:42
either
7:43
easier
7:43
by
7:44
allowing
7:44
me
7:44
to
7:44
throw
7:45
multiple
7:46
tires
7:46
into
7:46
my
7:47
universal
7:47
cart,
7:48
I
7:49
think
7:49
that's
7:49
another
7:49
reason
7:50
to
7:50
get
7:50
in
7:50
the
7:50
game.
7:51
But
7:52
as
7:52
a
7:52
supply
7:52
chain
7:53
operator,
7:53
I
7:54
don't
7:54
love
7:54
this.
7:55
Okay,
7:55
go.
7:56
So
7:56
Google
7:56
did
7:57
the
7:57
easy
7:57
part,
7:57
the
7:58
discovery
7:58
part,
7:59
and
7:59
now
7:59
my
7:59
multi
8:00
item
8:00
cart
8:01
is
8:01
going
8:01
to
8:01
four
8:02
different
8:02
merchants
8:03
who
8:04
don't
8:04
love
8:04
the
8:04
fact
8:05
that
8:05
their
8:05
average
8:05
items
8:06
per
8:06
order
8:06
probably
8:06
just
8:07
went
8:07
down
8:07
as
8:07
a
8:08
result
8:08
of
8:08
this.
8:09
Right.
8:09
Consumer
8:10
isn't
8:11
going
8:11
to
8:11
love
8:12
four
8:12
different
8:12
fulfillment
8:13
experiences
8:14
that
8:14
probably
8:15
get
8:15
executed
8:15
at
8:16
different
8:16
levels.
8:18
And
8:18
you
8:18
know,
8:18
Gartner
8:19
research
8:19
shows
8:20
us
8:20
that
8:20
loyalty
8:21
is
8:21
formed
8:21
through
8:22
the
8:22
execution
8:23
of
8:23
a
8:23
promise.
8:24
But
8:25
whose
8:25
promise
8:26
is
8:26
this?
8:27
Is
8:27
it
8:27
Google's
8:28
promise
8:28
or
8:28
is
8:29
it
8:29
the
8:29
merchant's
8:29
promise?
8:30
Because
8:31
that
8:31
gets
8:31
really
8:31
confusing
8:32
because
8:33
here
8:33
I
8:34
can
8:34
use
8:34
one
8:35
credit
8:35
card
8:35
in
8:36
this
8:36
Google
8:36
cart
8:36
that's
8:37
going
8:37
to
8:37
four
8:38
different
8:38
merchants.
8:38
I
8:39
don't
8:39
know
8:39
how
8:39
Google
8:39
figured
8:40
that
8:40
out.
8:40
That's
8:40
crazy.
8:41
But
8:41
they
8:42
did.
8:42
But
8:43
from
8:43
a
8:43
consumer
8:44
perspective,
8:44
who
8:45
just
8:45
made
8:45
this
8:45
promise?
8:46
Did
8:46
Google
8:46
make
8:47
it
8:47
or
8:47
did
8:47
the
8:47
merchants
8:48
make
8:48
it?
8:48
So
8:50
I
8:50
will
8:50
be
8:50
definitely
8:50
watching
8:51
with
8:51
much
8:51
interest
8:52
to
8:52
see
8:52
how
8:52
this
8:52
plays
8:53
out.
8:53
Right?
8:54
Right.
8:54
Yeah.
8:54
And
8:54
you're
8:54
kind
8:55
of
8:55
on.
8:55
You
8:55
kind
8:55
of
8:55
mentioned
8:56
the
8:56
same
8:56
points
8:56
that
8:56
I
8:56
mentioned
8:57
last
8:57
week
8:57
too,
8:57
you
8:57
know,
8:58
in
8:58
terms
8:58
of
8:58
like
8:58
wondering
8:59
just
8:59
how
8:59
this
8:59
all
8:59
plays
8:59
out
8:59
in
9:00
the,
9:00
in
9:00
the
9:00
consumer's
9:00
mind
9:01
in
9:01
terms
9:01
of
9:01
who
9:01
they're
9:01
going
9:01
to
9:01
hold
9:02
accountable
9:02
for
9:02
it
9:02
at
9:02
the
9:03
end
9:03
of
9:03
the
9:03
day.
9:03
Because
9:03
you're
9:03
right.
9:03
You
9:04
know,
9:04
you're
9:04
right,
9:04
chap.
9:04
Like
9:05
if
9:05
I'm
9:05
ordering
9:05
four
9:05
different
9:06
things
9:06
for
9:06
four
9:06
different
9:06
merchants,
9:07
you
9:07
know,
9:08
and
9:08
they,
9:08
and
9:08
one
9:08
of
9:09
them
9:09
or
9:09
two
9:09
of
9:09
them
9:09
arrives
9:10
at
9:10
a
9:10
different
9:10
time
9:10
or
9:11
of
9:11
a
9:11
different
9:11
quality.
9:11
Yeah.
9:12
Who
9:12
do
9:12
I
9:12
go
9:12
to
9:12
to
9:13
complain
9:13
about
9:13
that?
9:13
It's
9:14
gonna
9:14
be,
9:14
it's
9:14
gonna
9:14
be
9:15
a
9:15
much
9:15
bigger
9:15
hassle
9:15
than
9:16
I
9:16
probably
9:16
ever
9:17
experienced
9:17
previously,
9:18
so.
9:18
All
9:18
right,
9:19
well,
9:19
thanks,
9:19
man.
9:19
I
9:19
just
9:19
wanted
9:20
to
9:20
touch
9:20
on
9:20
that
9:20
because
9:20
I
9:20
thought
9:20
your
9:21
experience
9:21
level
9:22
was
9:22
pretty
9:22
unique
9:23
to
9:23
that
9:23
question.
9:23
And
9:24
I
9:24
love
9:24
how
9:24
you
9:24
brought
9:24
the
9:24
supply
9:25
chain
9:25
angle
9:25
into
9:25
it
9:26
too,
9:26
because
9:26
I
9:26
don't
9:26
think
9:26
we
9:26
touched
9:27
on
9:27
that
9:27
at
9:27
all
9:28
in
9:28
last
9:28
week's
9:28
episode.
9:29
Well,
9:29
are
9:29
you
9:30
ready
9:30
to
9:30
get
9:30
to
9:31
this
9:31
week's
9:31
show?
9:31
Let's
9:32
get
9:32
it
9:32
on.
9:33
All
9:33
right,
9:33
let's
9:34
get
9:34
to
9:34
this
9:34
week's
9:35
Fast
9:35
5,
9:35
which
9:35
is
9:36
of
9:36
course
9:37
brought
9:37
to
9:37
you
9:37
with
9:37
the
9:37
help
9:37
and
9:38
support
9:38
of
9:38
the
9:38
A
9:39
and
9:39
M
9:39
consumer
9:39
and
9:40
retail
9:40
group,
9:41
Miracle
9:42
Corso,
9:43
Ocampo
9:44
Capital
9:44
and
9:45
Valoc.
9:46
In
9:46
this
9:46
week's
9:46
Fast5,
9:47
we've
9:47
got
9:47
news
9:48
on
9:48
Amazon
9:48
expanding
9:49
fresh
9:49
grocery
9:50
same
9:51
day
9:51
delivery
9:51
in
9:51
London,
9:52
New
9:52
York
9:53
passing
9:53
legislation
9:54
to
9:54
curb
9:55
personalized
9:55
or
9:56
so
9:56
called
9:56
surveillance
9:57
pricing.
9:58
Amazon
9:58
rolling
9:59
out
9:59
new
9:59
AI
9:59
driven
10:00
visual
10:00
search
10:01
features
10:01
in
10:01
its
10:01
shopping
10:02
app,
10:03
a
10:03
new
10:03
startup
10:03
called
10:04
the
10:04
Mall
10:04
that
10:05
wants
10:05
to
10:05
be
10:05
the
10:05
Spotify
10:06
of
10:06
online
10:07
shopping.
10:07
And
10:08
Duvo
10:08
CEO
10:09
Tom
10:09
Cooper
10:10
joins
10:10
us
10:10
for
10:11
five
10:11
insightful
10:11
minutes
10:12
on
10:12
what
10:12
he
10:12
is
10:12
seeing
10:13
in
10:13
the
10:13
market
10:14
in
10:14
terms
10:14
of
10:14
retailers
10:15
abilities
10:15
to
10:15
adapt
10:16
AI
10:16
solutions
10:17
for
10:18
their
10:18
organizational
10:19
benefit.
10:19
But
10:20
we
10:20
begin
10:20
today
10:20
with
10:21
Walmart
10:21
doing
10:21
something
10:22
no
10:22
major
10:23
retailer
10:23
has
10:23
ever
10:24
done
10:24
before
10:25
delivering
10:25
restaurant
10:26
meals
10:26
straight
10:27
to
10:27
your
10:27
door
10:28
alongside
10:29
your
10:29
groceries.
10:30
Headline
10:30
number
10:31
one
10:31
is
10:31
of
10:31
course
10:31
brought
10:32
to
10:32
you
10:32
by
10:32
the
10:32
A
10:32
and
10:32
M
10:32
Consumer
10:33
and
10:33
Retail
10:33
Group.
10:33
The
10:34
A
10:34
and
10:34
M
10:34
Consumer
10:34
and
10:35
Retail
10:35
Group
10:35
is
10:35
a
10:35
management
10:36
consulting
10:36
firm
10:37
that
10:37
tackles
10:37
the
10:37
most
10:38
complex
10:38
challenges
10:39
and
10:39
advances
10:40
its
10:40
clients,
10:41
people
10:41
and
10:41
communities
10:42
toward
10:42
their
10:42
maximum
10:43
potential.
10:44
CRG
10:44
brings
10:44
the
10:45
experience,
10:45
tools
10:45
and
10:45
operator
10:46
like
10:46
pragmatism
10:47
to
10:47
help
10:47
retailers
10:48
and
10:48
consumer
10:48
products
10:49
companies
10:49
be
10:50
on
10:50
the
10:50
right
10:50
side
10:51
of
10:51
disruption
10:52
all
10:52
right
10:52
chap,
10:52
here
10:52
we
10:52
go.
10:53
Headline
10:53
number
10:53
one.
10:54
Walmart
10:54
is
10:54
now
10:55
delivering
10:55
Subway
10:56
sandwiches
10:58
directly
10:58
to
10:58
customers
10:58
through
10:59
the
10:59
Walmart
10:59
app
10:59
and
11:00
Walmart.com,
11:01
marking
11:01
the
11:02
first
11:02
time
11:02
a
11:03
major
11:03
retailer
11:03
has
11:04
integrated
11:04
made
11:04
to
11:04
order
11:05
restaurant
11:05
meals
11:05
into
11:06
its
11:06
Express
11:06
Delivery
11:07
service.
11:08
Announced
11:08
during
11:09
Walmart's
11:09
Shareholders
11:10
Week
11:10
In
11:10
Bentonville
11:11
on
11:11
June
11:12
4,
11:12
the
11:12
service
11:12
allows
11:13
customers
11:13
in
11:13
select
11:14
markets
11:14
to
11:15
order
11:15
freshly
11:15
made
11:15
Subway
11:16
meals
11:16
for
11:16
delivery
11:17
in
11:17
as
11:17
little
11:18
as
11:18
30
11:18
minutes
11:19
on
11:20
their
11:20
own
11:20
or
11:20
bundled
11:21
alongside
11:21
groceries,
11:22
prescriptions
11:22
and
11:23
household
11:23
staples
11:24
through
11:24
Walmart's
11:25
existing
11:25
Express
11:26
delivery
11:26
service.
11:27
The
11:27
service
11:28
has
11:28
been
11:28
tested
11:29
in
11:29
Connecticut
11:29
and
11:30
Dallas
11:30
thus
11:30
far,
11:31
and
11:31
Walmart
11:31
plans
11:31
to
11:31
expand
11:32
it
11:32
to
11:32
approximately
11:33
1400
11:34
Walmart
11:34
Supercenter
11:35
locations
11:36
where
11:36
Subway
11:37
franchises
11:37
operate
11:38
inside
11:38
the
11:38
stores
11:38
by
11:39
the
11:39
end
11:39
of
11:39
the
11:39
summer.
11:40
This
11:41
move
11:41
also
11:41
puts
11:42
Walmart
11:42
in
11:42
more
11:42
direct
11:43
competition
11:43
with
11:43
UberEats
11:44
and
11:44
DoorDash
11:45
as
11:45
it
11:45
now
11:45
combines
11:46
grocery
11:46
and
11:46
meal
11:47
delivery
11:47
under
11:47
a
11:47
single
11:48
transaction
11:48
and
11:49
app
11:49
experience
11:50
with
11:50
further
11:51
room
11:51
to
11:51
grow
11:51
over
11:52
time.
11:52
Because
11:52
according
11:53
to
11:53
friend
11:53
of
11:53
OmniTok
11:54
Walmart
11:54
SVP
11:55
Greg
11:55
Cathy
11:56
quote
11:56
Almost
11:57
all
11:57
quick
11:57
service
11:58
restaurant
11:58
brands
11:59
are
11:59
within
12:00
five
12:00
miles
12:01
of
12:01
a
12:01
Walmart
12:01
store.
12:02
End
12:02
quote.
12:03
Chap.
12:03
Walmart
12:04
has
12:04
officially
12:04
entered
12:05
the
12:05
food
12:05
delivery
12:05
wars.
12:06
Is
12:07
subway
12:07
delivery
12:07
a
12:08
flash
12:08
in
12:08
the
12:08
pan
12:08
or
12:09
a
12:09
sign
12:09
of
12:09
yet
12:10
another
12:10
consequential
12:11
growth
12:11
strategy
12:12
for
12:12
Walmart?
12:13
Wow.
12:13
So
12:13
Chris,
12:14
I'd
12:15
have
12:15
to
12:15
say
12:16
uber
12:16
eats
12:17
and
12:17
DoorDash.
12:18
I
12:18
hope
12:18
you
12:19
are
12:19
paying
12:19
attention
12:20
because
12:21
wow,
12:21
a
12:22
shot
12:22
has
12:22
been
12:23
fired
12:23
across
12:24
your
12:24
bow.
12:25
You
12:26
know,
12:26
delivery
12:26
infrastructure
12:27
seems
12:27
to
12:27
be
12:28
Walmart's
12:28
flywheel
12:29
right
12:29
now,
12:29
so
12:30
this
12:30
clearly
12:30
makes
12:31
sense
12:31
for
12:31
them.
12:31
With
12:32
basket
12:32
size
12:32
more
12:33
reason
12:33
to
12:33
shop
12:33
there.
12:34
Free
12:35
delivery
12:35
of
12:35
the
12:36
Subway
12:36
if
12:36
you're
12:36
Pairing
12:37
it
12:37
with
12:37
groceries
12:38
so
12:38
it
12:38
gets
12:39
more
12:39
use
12:39
cases
12:39
into
12:40
this
12:40
delivery
12:40
flywheel.
12:42
And
12:42
of
12:42
course
12:42
the
12:42
biggest
12:42
use
12:43
case
12:43
of
12:43
all
12:43
in
12:43
delivery
12:44
for
12:44
fast
12:44
delivery
12:45
is
12:45
restaurants.
12:46
And
12:47
when
12:47
you
12:47
pair
12:48
this
12:48
with
12:48
the
12:48
drone
12:49
infrastructure
12:49
as
12:50
well,
12:50
your
12:50
head
12:50
starts
12:51
to
12:51
explode
12:52
with
12:52
the
12:52
possibilities.
12:53
So
12:54
yes
12:54
to
12:55
Subway,
12:55
but
12:56
a
12:56
bigger
12:56
yes
12:56
to
12:57
Jimmy
12:57
John's
12:57
and
12:57
Jersey
12:58
Mike's,
12:58
etc.
12:59
If
13:00
Walmart
13:00
can
13:01
figure
13:01
out
13:02
a
13:02
middle
13:02
mile
13:03
infrastructure
13:04
that
13:04
connects
13:04
the
13:04
delivery
13:05
execution
13:05
from
13:06
Walmart
13:06
to
13:06
stores
13:07
surrounding
13:07
the
13:07
restaurants,
13:09
yes.
13:09
Now
13:09
all
13:09
of
13:09
a
13:10
sudden
13:10
Walmart
13:10
is
13:10
your
13:11
super
13:11
app
13:11
for
13:12
food
13:12
delivery.
13:13
The
13:13
downside
13:14
here
13:15
I
13:15
see
13:15
is
13:16
that
13:16
Walmart
13:17
is
13:17
attaching
13:17
their
13:18
delivery
13:18
execution
13:19
to
13:19
Subway's
13:20
execution.
13:21
Right.
13:21
And
13:21
any
13:21
other
13:21
restaurant
13:22
that
13:22
comes
13:22
into
13:23
this,
13:23
something
13:23
I
13:24
am
13:24
sure
13:24
that
13:24
they
13:25
will
13:25
monitor.
13:26
But
13:26
if
13:26
delivery
13:27
is
13:27
the
13:27
thing
13:28
that's
13:28
building
13:28
trust
13:29
and
13:29
improving
13:30
repeat
13:31
purchase
13:31
rate,
13:32
which
13:32
is
13:32
what
13:32
Walmart
13:33
said
13:33
in
13:33
their
13:34
last
13:34
quarterly
13:34
earnings,
13:35
then
13:36
be
13:36
careful
13:37
here.
13:38
But
13:38
overall,
13:39
as
13:39
a
13:39
retail
13:39
supply
13:40
chain
13:40
guy,
13:40
I
13:41
love
13:41
seeing
13:41
retailers
13:42
make
13:42
hay
13:42
with
13:43
their
13:43
supply
13:43
chain
13:43
assets
13:44
and
13:44
nobody
13:45
has
13:45
that
13:45
more
13:46
dialed
13:46
in
13:46
than
13:46
Walmart
13:47
right
13:47
now.
13:48
Yeah,
13:48
Chad,
13:48
I'm
13:49
curious
13:49
that,
13:49
that
13:49
the
13:50
point
13:50
about
13:50
the
13:50
drone
13:50
thing
13:51
is
13:51
really
13:51
interesting.
13:51
I
13:51
hadn't
13:51
thought
13:52
about
13:52
that
13:52
either.
13:52
So,
13:52
like
13:53
what,
13:53
so
13:53
you
13:54
know,
13:54
delivering
13:55
Subway
13:55
out
13:55
of
13:55
the
13:55
store
13:56
is
13:56
one
13:56
thing.
13:57
Coordinating
13:58
deliveries
13:59
with,
13:59
you
13:59
know,
14:00
existing
14:01
retailers
14:02
around
14:02
a
14:03
Walmart
14:03
is
14:03
another
14:03
thing.
14:04
And
14:05
then
14:05
coordinating
14:05
drone
14:06
deliveries
14:06
from
14:06
a
14:07
Walmart
14:07
with
14:07
those
14:07
existing
14:08
retailers
14:08
is
14:08
then
14:09
a
14:09
third
14:09
thing.
14:09
So
14:10
what
14:10
are
14:10
some
14:10
of
14:10
the
14:11
complications
14:11
that
14:12
arise
14:12
as
14:12
you
14:12
go
14:13
from,
14:13
you
14:14
know,
14:14
each
14:15
of
14:15
those,
14:16
you
14:16
know,
14:16
as,
14:17
as
14:17
Walmart?
14:17
Yeah,
14:18
well,
14:19
I,
14:19
I,
14:19
I
14:20
think
14:20
obviously,
14:20
you
14:20
know,
14:21
Walmart's
14:21
scaling
14:21
drone
14:22
drones
14:23
aggressively
14:24
because
14:24
they've
14:25
got
14:25
parking
14:25
lots,
14:26
right.
14:26
They've
14:26
got
14:26
the
14:27
infrastructure.
14:27
And
14:27
I
14:28
think
14:28
the
14:28
complicating
14:29
factor
14:29
on
14:29
how,
14:30
how
14:30
do
14:30
we
14:30
connect
14:31
more
14:31
delivery
14:32
use
14:32
cases
14:32
and
14:32
then
14:33
we
14:33
pair
14:33
it
14:33
with
14:33
drones
14:34
is
14:34
like,
14:34
well
14:34
what
14:34
is
14:34
this?
14:36
Weren't,
14:36
you
14:36
know,
14:37
how
14:37
do
14:37
we
14:37
get
14:37
drones
14:38
into
14:38
some
14:38
of
14:38
these
14:39
other
14:39
locations
14:39
that
14:40
aren't
14:40
going
14:40
to
14:41
have
14:41
that
14:42
kind
14:42
of
14:42
real
14:42
estate
14:43
to
14:43
do
14:43
that
14:43
with.
14:44
Which
14:44
is
14:44
where
14:45
I
14:45
think
14:45
there
14:45
needs
14:45
to
14:46
be
14:46
some
14:46
interesting
14:47
middle
14:48
mile
14:48
infrastructure
14:49
that
14:49
might
14:49
not
14:49
be
14:49
drones.
14:50
It
14:50
might
14:50
be
14:51
motorcycles
14:51
or
14:52
bikes.
14:52
Right.
14:52
That's
14:53
connecting
14:55
the,
14:55
the
14:55
food
14:55
service
14:56
with
14:56
the
14:56
main
14:57
delivery
14:57
infrastructure.
14:58
Because
14:58
obviously
14:59
we
15:00
still
15:00
gotta
15:00
execute
15:01
on
15:01
speed
15:01
here.
15:01
So
15:02
we
15:02
can't
15:02
like
15:03
make
15:03
milk
15:03
runs
15:03
over
15:03
five
15:04
hours
15:04
to
15:04
pick
15:04
up
15:04
food
15:05
orders
15:05
and
15:05
then
15:05
bring
15:05
them
15:06
back
15:06
to
15:06
Walmart.
15:07
So
15:07
it's
15:08
going
15:08
to
15:08
be
15:08
a
15:08
super
15:09
complicated
15:09
dance.
15:10
But
15:11
I
15:11
mean
15:11
if
15:12
anyone
15:12
can
15:12
figure
15:12
out
15:13
that
15:13
dance,
15:13
I
15:13
would
15:13
think
15:14
it
15:14
would
15:14
be,
15:14
it
15:15
would
15:15
be
15:15
Walmart.
15:16
Yeah,
15:16
but
15:16
Chad,
15:17
I
15:17
think
15:17
to
15:17
your
15:17
point
15:17
too,
15:17
I
15:18
think
15:18
we
15:18
also,
15:18
you
15:19
know,
15:19
it's
15:19
interesting
15:19
to
15:20
me,
15:20
like
15:20
they
15:20
said
15:20
1400
15:21
subway
15:21
locations,
15:22
you
15:22
know,
15:22
in
15:22
a
15:22
Walmart
15:23
store
15:24
there's
15:24
probably
15:24
X
15:25
number
15:25
of
15:25
the
15:25
4600
15:26
Walmart
15:27
Super
15:27
Centers
15:28
that
15:28
have,
15:29
you
15:29
know,
15:30
restaurants
15:31
in
15:31
the
15:31
same
15:31
strip
15:32
mall
15:32
as
15:32
them
15:33
as
15:33
well,
15:34
where
15:34
that
15:34
parking
15:35
lot
15:35
is
15:35
very
15:36
readily
15:36
available
15:37
to
15:37
them
15:37
to
15:38
then
15:38
tie
15:38
into
15:38
the
15:39
drones
15:39
too.
15:39
So
15:40
it
15:40
doesn't
15:40
necessarily
15:40
need
15:41
to
15:41
be
15:41
an
15:41
all
15:41
or
15:41
nothing
15:42
thing,
15:42
which
15:42
I'd
15:42
never
15:42
thought
15:43
about
15:43
until
15:43
you
15:43
just
15:44
said
15:44
that
15:44
because,
15:45
you
15:45
know,
15:45
there's
15:46
probably
15:46
some
15:47
subset
15:47
of
15:47
stores
15:48
where
15:48
this
15:49
is
15:49
going
15:49
to
15:49
work.
15:50
And
15:50
you
15:50
know,
15:50
for
15:50
that
15:50
reason,
15:51
I'm
15:51
with
15:52
you,
15:52
man.
15:52
I
15:52
think,
15:52
I
15:53
think
15:53
this
15:53
is
15:53
a
15:54
brilliant
15:54
extension
15:56
and
15:56
it's
15:57
something
15:57
that
15:57
I
15:57
actually
15:57
have
15:58
firsthand
15:58
experience
15:58
trying
15:59
to
16:00
get
16:00
going
16:01
at
16:01
Target
16:01
too.
16:02
But
16:03
the
16:03
first
16:03
thing
16:03
I'd
16:03
say
16:03
about
16:04
it
16:04
is
16:05
it's
16:05
like
16:05
you
16:05
said,
16:06
it
16:06
makes
16:06
you
16:06
really
16:06
sticky
16:07
to
16:07
Walmart
16:08
if
16:08
they
16:08
pull
16:08
it
16:08
off
16:08
because
16:09
food
16:09
delivery
16:09
is
16:09
something
16:10
especially
16:10
in
16:10
those
16:11
higher
16:11
income
16:11
demographics
16:12
which
16:12
Walmart's
16:12
also
16:13
trying
16:13
to
16:13
push
16:13
its
16:13
way
16:13
into.
16:15
They
16:15
come,
16:16
if
16:17
you're
16:17
using
16:17
food
16:17
delivery
16:18
sometimes
16:18
you're
16:18
doing
16:18
it
16:18
multiple
16:19
times
16:19
a
16:20
week,
16:20
more
16:20
often
16:20
than
16:21
you're
16:21
probably
16:21
interacting
16:21
with
16:22
Walmart
16:22
on
16:22
a
16:22
given
16:23
week.
16:24
And
16:24
then
16:25
the
16:25
other
16:25
thing
16:25
too
16:25
I
16:25
talk
16:25
about
16:26
in
16:26
the
16:26
delivery
16:26
space
16:27
is,
16:27
you
16:27
know,
16:28
the
16:28
size
16:29
of
16:29
the
16:29
basket
16:29
is
16:30
so
16:30
integral
16:30
to
16:30
making
16:31
that
16:31
delivery
16:32
profitable.
16:32
So
16:33
even
16:33
if,
16:33
you
16:33
know,
16:33
even
16:34
if
16:34
you
16:34
take
16:34
the
16:34
drones
16:35
part
16:35
out
16:35
of
16:35
it
16:35
in
16:36
the
16:36
long
16:36
run,
16:36
there's
16:37
still
16:37
just
16:37
good
16:38
economics
16:39
here
16:39
in
16:39
terms
16:39
of
16:40
what
16:40
they're
16:40
trying
16:40
to
16:40
do
16:40
just
16:41
even
16:41
with
16:41
Subway
16:41
out
16:42
of
16:42
their
16:42
stores.
16:42
So
16:43
you
16:43
get
16:43
the
16:44
basket
16:44
size
16:44
increase,
16:44
that's
16:45
what
16:45
you
16:45
want.
16:45
And
16:46
then
16:46
the
16:47
other
16:47
thing
16:47
too,
16:47
I'll
16:48
say
16:48
in
16:48
closing,
16:49
and
16:49
then
16:49
we'll
16:49
move
16:49
on
16:50
is
16:51
I
16:51
think
16:51
if
16:51
you're
16:51
keeping
16:52
score
16:52
at
16:52
home,
16:53
you
16:53
have
16:53
to
16:53
keep
16:53
score
16:54
at
16:54
home
16:54
here
16:54
because
16:55
just
16:55
between
16:55
this
16:56
show
16:56
and
16:56
last
16:57
week
16:57
alone,
16:57
I
16:57
want
16:58
to,
16:58
I
16:58
just
16:58
want
16:58
to
16:58
break
16:58
down
16:58
Walmart's
16:59
growth
16:59
strategies.
17:00
I
17:00
did
17:00
it
17:00
a
17:00
little
17:00
bit
17:00
last
17:01
week,
17:01
but
17:01
you've
17:02
got
17:02
Walmart's
17:02
marketplace,
17:03
you've
17:04
got
17:04
them
17:04
going
17:04
up
17:04
market
17:05
with
17:05
Walmart
17:05
plus,
17:06
which
17:06
they
17:06
also
17:06
just
17:07
took
17:07
to
17:07
Canada,
17:07
which
17:08
didn't
17:08
even
17:08
make
17:08
this
17:09
week's
17:09
Fast
17:09
five
17:09
headlines.
17:11
You've
17:11
got
17:11
the
17:11
convenience
17:12
play
17:12
with
17:12
drones
17:13
overall
17:13
just
17:13
for
17:14
the
17:14
average
17:14
convenience
17:14
purchase.
17:15
And
17:15
now
17:15
you've
17:16
got
17:16
restaurant
17:17
delivery
17:17
and
17:17
the
17:18
future
17:18
of
17:18
restaurant
17:18
delivery
17:19
too.
17:20
And
17:20
all
17:20
of
17:20
those,
17:21
all
17:21
of
17:21
those,
17:22
when
17:22
they're
17:22
working
17:22
together,
17:23
expand
17:24
your
17:24
retail
17:24
media
17:25
reach
17:25
too,
17:25
which
17:25
means
17:25
you're
17:26
going
17:26
to
17:26
get
17:26
even
17:26
more
17:26
profit.
17:27
So
17:27
it's
17:28
perfect
17:28
synergy.
17:29
And
17:29
it's,
17:30
it's
17:30
just
17:30
a
17:30
testament,
17:31
like
17:31
you
17:31
said,
17:32
to
17:32
how
17:32
Walmart
17:33
is
17:33
humming
17:34
on
17:34
their
17:34
fulfillment
17:35
capabilities
17:35
right
17:36
now.
17:36
Chad.
17:37
Yeah,
17:37
yeah,
17:38
yeah.
17:39
Watch
17:39
out
17:39
world.
17:39
Walmart
17:40
seems
17:40
to
17:41
have
17:41
it
17:41
dialed
17:41
in
17:41
right
17:42
now.
17:43
100.
17:43
100.
17:45
All
17:45
right
17:45
Chris.
17:46
Our
17:46
headline
17:46
number
17:47
two
17:47
is
17:47
brought
17:48
to
17:48
you
17:48
by
17:49
Miracle,
17:49
the
17:49
catalyst
17:50
of
17:50
Commerce.
17:51
Over
17:51
450
17:52
retailers
17:53
are
17:53
opening
17:53
new
17:54
revenue
17:54
streams
17:54
with
17:55
marketplaces,
17:55
dropship
17:56
and
17:56
retail
17:56
media.
17:57
And
17:57
succeeding
17:58
with
17:58
Miracle,
17:59
you
17:59
can
17:59
unlock
18:00
more
18:00
products,
18:00
more
18:01
partners
18:01
and
18:01
more
18:02
profits
18:02
without
18:03
the
18:03
heavy
18:03
lifting.
18:04
So
18:05
what's
18:05
holding
18:05
you
18:06
back?
18:06
Visit
18:07
miracle.com
18:08
to
18:08
learn
18:08
more.
18:09
Nicely
18:09
done
18:10
on
18:10
that
18:10
read.
18:11
Nicely
18:11
done.
18:13
Headline
18:13
number
18:14
two,
18:15
Amazon
18:15
has
18:15
launched
18:16
Fresh
18:16
Grocery
18:17
Same
18:17
day
18:18
delivery
18:18
in
18:18
parts
18:19
of
18:19
London,
18:19
allowing
18:20
customers
18:21
ineligible
18:22
postcodes
18:22
to
18:22
add
18:23
perishable
18:23
items
18:23
like
18:24
produce,
18:24
meat,
18:24
dairy
18:25
and
18:25
frozen
18:25
foods
18:25
to
18:26
the
18:26
same
18:26
basket
18:27
as
18:27
millions
18:27
of
18:27
other
18:28
Amazon
18:28
products
18:28
for
18:29
delivery
18:29
within
18:30
hours.
18:31
According
18:31
to
18:32
Amazon
18:32
blog
18:32
posts,
18:33
the
18:33
service
18:33
is
18:33
initially
18:34
live
18:34
in
18:34
parts
18:34
of
18:34
central
18:35
and
18:35
East
18:35
London
18:36
with
18:36
plans
18:36
to
18:36
expand
18:37
to
18:37
additional
18:37
postcodes
18:38
and
18:38
more
18:39
areas
18:39
across
18:39
the
18:40
UK
18:40
in
18:40
the
18:40
coming
18:40
months.
18:42
Prime
18:42
members
18:42
get
18:43
free
18:43
same
18:43
day
18:43
delivery
18:44
on
18:44
orders
18:44
over
18:44
£20
18:45
with
18:45
fresh
18:46
groceries
18:46
now
18:46
included
18:47
at
18:47
no
18:48
additional
18:48
delivery
18:48
fee.
18:49
Non
18:49
prime
18:50
customers
18:50
pay
18:50
a
18:51
599
18:51
delivery
18:52
fee
18:52
regardless
18:53
of
18:53
basket
18:53
size.
18:54
This
18:55
launch
18:55
builds
18:56
on
18:56
Amazon's
18:56
broader
18:57
UK
18:57
grocery
18:58
expansion
18:58
which
18:58
includes
18:59
Amazon
18:59
now
19:00
growing
19:00
Amazon
19:01
Fresh
19:01
selection
19:01
and
19:02
partnerships
19:02
with
19:02
Morrisons
19:03
Co
19:03
Op,
19:03
Iceland
19:04
and
19:04
Gopuff
19:05
and
19:06
Whole
19:06
Food
19:06
Market
19:06
stores
19:07
in
19:07
the
19:07
uk.
19:09
Chris,
19:09
how
19:10
significant
19:11
is
19:11
Fresh
19:12
same
19:12
day
19:12
delivery
19:13
grocery
19:13
in
19:14
the
19:14
UK
19:14
for
19:14
the
19:15
future
19:15
of
19:15
grocery
19:17
retail
19:17
globally?
19:18
And
19:19
how
19:19
closely
19:19
should
19:20
U
19:20
S
19:20
grocers
19:21
be
19:21
watching
19:21
Amazon's
19:22
moves
19:22
overseas?
19:23
Oh
19:24
my
19:24
God.
19:24
A
19:24
two
19:25
parter
19:25
chap.
19:25
All
19:26
right.
19:27
I
19:27
mean
19:27
I
19:27
think
19:27
you've
19:28
got
19:28
to
19:28
watch
19:28
it
19:28
closely
19:29
and
19:30
I
19:30
think
19:30
it's
19:30
a
19:30
very
19:30
significant
19:31
move.
19:31
That's,
19:32
that's
19:32
what
19:32
I
19:32
think
19:32
here.
19:32
And
19:32
I
19:33
say
19:33
that
19:33
for,
19:33
I
19:33
say
19:33
that
19:33
for
19:34
two
19:34
reasons.
19:34
You
19:34
know,
19:34
number
19:35
one,
19:36
you
19:36
know,
19:37
their
19:37
strategies
19:37
are
19:37
this
19:38
strategy
19:38
is
19:38
already
19:38
being
19:39
deployed
19:39
in
19:39
the
19:39
us.
19:39
This
19:40
is
19:40
funny
19:40
that
19:40
this
19:40
is
19:40
actually
19:41
one
19:41
time
19:41
where
19:41
we're
19:41
seeing
19:42
something
19:42
come
19:42
to
19:42
the
19:42
US
19:43
before
19:43
it
19:43
comes
19:44
to
19:44
Europe,
19:44
especially
19:45
in
19:45
grocery.
19:46
That's
19:46
what's
19:46
so
19:46
fascinating
19:47
to
19:47
me
19:47
about
19:47
this
19:47
story.
19:48
And
19:48
then
19:48
the
19:49
second
19:49
thing
19:49
I'd
19:49
say
19:49
is,
19:50
and
19:51
for
19:51
that
19:51
reason
19:51
is
19:52
like
19:52
the
19:52
online
19:52
grocery
19:53
market
19:53
in
19:53
the
19:54
UK
19:54
is
19:54
so
19:55
much
19:55
more
19:55
entrenched.
19:56
But
19:56
it's,
19:57
I
19:57
was
19:57
talking
19:58
to
19:58
friends
19:58
of
19:58
mine
19:58
out
19:58
here
19:58
at
19:59
Shop
19:59
Talk
19:59
Europe
19:59
about
19:59
this
20:00
to
20:00
make
20:00
sure
20:00
I
20:00
had
20:00
this
20:00
right
20:01
and
20:01
they
20:01
said
20:01
I
20:01
did
20:02
but,
20:02
but
20:02
it's
20:03
really
20:03
based
20:03
on
20:03
next
20:04
day.
20:04
Like
20:05
an
20:05
ocado
20:06
is
20:06
really
20:06
the,
20:06
you
20:06
know
20:07
the
20:07
winner
20:07
and
20:07
Tesco
20:08
is
20:08
the
20:08
winner
20:08
there
20:08
in
20:09
terms
20:09
of
20:09
next
20:09
day
20:10
grocery
20:11
delivery.
20:11
So
20:12
if
20:12
Amazon
20:12
can
20:13
actually
20:13
make
20:13
a
20:14
chunk
20:15
or
20:15
a
20:15
sizable
20:16
dent
20:16
in
20:16
same
20:17
day
20:17
or
20:17
make
20:17
that
20:18
the
20:18
consumer
20:18
expectation
20:20
via
20:20
just.
20:20
Even
20:21
if
20:21
it's
20:21
just
20:21
the
20:21
add
20:21
ons
20:22
like
20:22
the
20:22
milk
20:22
with
20:22
the
20:22
electronics
20:23
purchase
20:23
like
20:23
we
20:24
hear
20:24
in
20:24
the
20:24
States
20:24
here
20:24
all
20:24
the
20:25
time,
20:25
the
20:25
Andy
20:26
Jassy's
20:26
always
20:26
trying
20:27
to
20:27
hype
20:27
up,
20:28
then
20:28
I
20:28
think
20:29
there's
20:29
a
20:29
real
20:29
there
20:29
there
20:30
and
20:31
it
20:31
all
20:31
comes
20:31
back
20:32
to
20:32
me.
20:32
Chap
20:32
to
20:33
the
20:33
long
20:33
game.
20:33
We've
20:34
talked
20:34
about
20:34
this
20:34
on
20:34
past
20:34
shows
20:35
but
20:36
for
20:36
Amazon
20:36
it's
20:36
just
20:36
about
20:37
winning
20:37
the
20:37
war
20:37
of
20:37
attrition,
20:38
the
20:38
slow
20:38
kill,
20:39
the
20:39
death
20:39
by
20:39
a
20:40
thousand
20:40
cuts.
20:41
But
20:42
over
20:42
time
20:43
then
20:44
Amazon
20:45
becomes
20:45
the
20:45
default
20:46
grocery
20:46
fulfillment
20:47
for
20:47
all
20:47
regional
20:48
grocers,
20:48
both
20:50
in
20:50
the
20:50
US
20:50
particularly
20:51
but
20:51
then
20:51
also
20:52
the
20:52
smaller
20:52
grocers
20:53
in
20:53
the
20:53
UK
20:54
too
20:54
who
20:54
are
20:55
already
20:55
becoming
20:55
part
20:56
of
20:56
their
20:56
marketplace.
20:57
I
20:57
think
20:57
Morrison's
20:58
part
20:58
of
20:58
their
20:58
marketplace
20:59
if
20:59
I'm
20:59
not
20:59
mistaken.
21:00
And
21:00
so
21:01
you
21:01
know,
21:01
if
21:01
they
21:02
do
21:02
that,
21:02
you
21:02
know,
21:02
10
21:02
to
21:03
15
21:03
years
21:03
from
21:03
now
21:04
all
21:05
of
21:05
those
21:05
retailers
21:05
are
21:06
going
21:06
to
21:06
be
21:06
less
21:06
and
21:06
less
21:07
valuable
21:07
in
21:07
the
21:07
market
21:08
and
21:08
Amazon
21:08
can
21:08
either
21:09
buy
21:09
them
21:09
on
21:09
the
21:09
cheap
21:10
or
21:10
just
21:10
watch
21:10
them
21:10
go
21:10
out
21:10
of
21:11
business.
21:11
I
21:11
think
21:11
that's
21:11
what
21:12
Amazon's
21:12
play
21:12
is
21:12
here.
21:12
But
21:13
what
21:13
do
21:13
you
21:13
think?
21:15
Interesting.
21:16
Yeah,
21:16
you
21:16
know,
21:17
so
21:17
the
21:17
Walmart
21:18
move
21:18
made
21:18
a
21:18
ton
21:18
of
21:18
sense
21:19
because
21:19
we're
21:19
pairing
21:20
more
21:20
food
21:20
with
21:20
food.
21:21
It's
21:21
the
21:21
same
21:21
use
21:22
case,
21:24
I
21:24
need
21:24
food
21:25
now
21:25
on
21:26
Amazon
21:26
we're
21:26
mixing
21:27
use
21:27
cases,
21:27
I
21:28
need
21:28
food
21:28
and
21:28
a
21:29
phone
21:29
charger
21:29
now
21:31
of
21:31
course,
21:31
as
21:31
you
21:31
said,
21:32
we
21:32
have
21:32
seen
21:32
Amazon
21:33
do
21:33
this
21:33
in
21:34
the
21:34
US
21:34
and
21:35
it
21:35
worked.
21:35
Right.
21:35
Amazon
21:36
reported
21:36
they
21:36
met
21:37
their
21:37
expansion
21:37
targets
21:38
of
21:38
the
21:38
same
21:38
day
21:39
and
21:41
with
21:41
people
21:41
ordering
21:42
perishable
21:42
items
21:43
with
21:43
normal
21:43
Amazon
21:44
items
21:44
and
21:44
they
21:44
reported
21:45
customers
21:45
coming
21:46
back
21:46
at
21:46
2x
21:47
the
21:47
rate
21:47
of
21:47
customers
21:48
who
21:48
didn't.
21:49
So
21:49
they
21:50
did
21:50
prove
21:50
that
21:50
it
21:51
works.
21:51
Right.
21:51
So
21:53
it
21:53
didn't
21:53
make
21:53
sense
21:53
to
21:54
me
21:54
at
21:54
the
21:54
beginning
21:54
but
21:54
like
21:55
the
21:55
consumers
21:55
are,
21:55
it's,
21:56
it's
21:56
resonating.
21:57
But
21:57
the
21:57
nuance
21:58
here
21:58
is
21:58
the
21:59
market.
21:59
As
21:59
you
22:00
said,
22:00
the
22:00
US
22:00
online
22:01
grocery
22:01
market
22:01
is
22:02
in
22:02
a
22:02
period
22:02
of
22:02
rapid
22:03
growth.
22:03
Right.
22:04
Of
22:04
and
22:05
the
22:05
consumer
22:05
is
22:06
more
22:06
open
22:06
to
22:06
experimenting
22:07
with
22:07
online
22:08
grocery.
22:08
And
22:08
so
22:09
that's
22:09
driving
22:10
trial
22:11
in
22:11
the
22:11
uk
22:11
growth
22:12
rates
22:12
are
22:12
much
22:12
more
22:13
modest,
22:13
the
22:13
market's
22:14
much
22:14
more
22:14
mature.
22:16
So
22:16
Amazon's
22:16
going
22:16
to
22:17
have
22:17
to
22:17
steal
22:18
market
22:18
share
22:19
to
22:19
make
22:19
this
22:20
work.
22:20
Right.
22:21
And
22:21
the
22:21
data
22:22
supports
22:22
the
22:22
customer
22:23
adoption
22:23
and
22:23
the
22:23
retention.
22:24
But
22:25
here
22:25
I've
22:25
got
22:25
to
22:25
go
22:25
steal
22:26
it
22:26
from
22:26
more
22:27
entrenched
22:28
players
22:28
to
22:29
make
22:29
it
22:29
grow
22:29
in
22:29
the
22:29
market
22:30
so
22:30
that's
22:30
to
22:31
me
22:31
the
22:31
challenge
22:31
for
22:31
Amazon
22:32
here
22:33
if
22:33
I
22:33
zoom
22:34
out
22:34
to
22:34
the
22:35
macro.
22:36
Amazon
22:37
Grocery
22:38
Delivery
22:38
vs
22:39
Omni
22:39
Channel
22:40
Grocery
22:40
delivery.
22:41
I
22:42
still
22:42
place
22:42
my
22:43
bet
22:43
on
22:44
omnichannel
22:44
grocery
22:45
LED
22:46
delivery
22:47
because
22:47
of
22:48
this.
22:48
Right.
22:49
So
22:49
we've
22:49
been
22:49
selling
22:49
stuff
22:50
online
22:50
for
22:50
25
22:51
plus
22:51
years
22:52
and
22:52
we're
22:52
at
22:53
20
22:53
to
22:54
40%
22:55
penetration
22:55
rates
22:56
given
22:56
the
22:56
category.
22:57
Right,
22:57
right.
22:58
But
22:58
the
22:59
consumer,
22:59
so
23:00
the
23:00
consumer
23:00
did
23:01
not
23:01
stop
23:01
shopping
23:01
at
23:02
stores
23:02
and
23:02
will
23:03
not
23:03
stop
23:03
shopping
23:04
at
23:04
stores,
23:04
especially
23:05
grocery
23:05
stores.
23:06
So
23:07
in
23:07
the
23:07
long
23:08
game,
23:08
I
23:08
give
23:09
better
23:09
chances
23:10
to
23:10
those
23:10
that
23:10
can
23:11
execute
23:11
both
23:12
offline
23:12
and
23:13
online
23:14
at
23:14
a
23:14
high
23:15
level.
23:16
But
23:17
note
23:17
that
23:17
I
23:18
said
23:18
at
23:18
a
23:18
high
23:18
level
23:19
because
23:20
just
23:20
because
23:20
you
23:20
have
23:21
both
23:21
channels
23:21
does
23:22
not
23:22
mean
23:22
success.
23:23
And
23:23
we
23:24
know
23:24
Amazon
23:24
can
23:25
execute
23:25
at
23:26
a
23:26
super
23:26
high
23:26
level.
23:27
So
23:28
this
23:28
is
23:28
the
23:28
key
23:28
issue
23:29
for
23:29
grocers
23:30
in
23:30
the
23:30
long
23:30
game
23:31
is
23:32
you
23:32
need
23:32
to
23:33
execute
23:33
at
23:33
the
23:33
same
23:34
high
23:34
level
23:34
as
23:35
Amazon
23:36
in
23:36
order
23:36
to
23:36
retain
23:37
this
23:37
omnichannel
23:38
advantage.
23:39
It
23:39
can't
23:39
just
23:39
be
23:39
about
23:40
your
23:40
in
23:40
store
23:40
experience
23:41
because
23:42
consumers
23:43
have
23:43
this
23:43
great
23:43
option
23:44
online
23:44
option
23:45
over
23:45
here
23:45
on
23:45
Amazon
23:47
and
23:47
if
23:47
that
23:47
gets
23:47
really
23:48
good,
23:48
yeah,
23:49
they
23:49
are
23:49
going
23:49
to
23:49
stop
23:50
shopping
23:50
as
23:51
frequently
23:51
in
23:51
your
23:51
stores.
23:52
So
23:52
that's
23:52
the
23:53
super
23:53
interesting
23:54
long
23:54
game
23:55
I
23:55
think
23:55
that's
23:55
being
23:56
played
23:56
here.
23:57
Yeah,
23:57
yeah.
23:57
Or,
23:58
or
23:58
if
23:58
I
23:58
say
23:58
it
23:58
another
23:59
way,
23:59
you
23:59
can't
23:59
just
23:59
dig
23:59
in
23:59
your,
24:00
dig
24:00
your
24:00
head
24:00
in
24:00
the
24:00
sand
24:01
if
24:01
you
24:01
happen
24:01
to
24:02
run
24:02
stores
24:02
and
24:02
think
24:02
the
24:03
stores
24:03
are
24:03
going
24:03
to
24:03
save
24:03
you.
24:04
Which
24:04
is
24:04
what,
24:04
you
24:04
know,
24:05
we
24:05
saw
24:05
the
24:05
industry
24:05
do
24:06
for
24:06
all
24:06
of
24:06
the
24:06
2000s.
24:07
Really,
24:07
you
24:08
know.
24:08
Right.
24:09
Chap.
24:10
That's
24:10
kind
24:11
of
24:11
what
24:11
you're
24:11
saying
24:11
again.
24:11
But
24:12
yeah,
24:12
the
24:13
other
24:13
point
24:13
about
24:13
this
24:13
too,
24:13
which
24:14
I
24:14
want
24:14
to
24:14
go,
24:14
I
24:15
want
24:15
to
24:15
go
24:15
retail
24:15
wonk
24:16
a
24:16
little
24:16
bit
24:16
too.
24:16
And
24:16
Chad,
24:16
I
24:17
think
24:17
you'll
24:17
appreciate
24:17
this
24:17
too.
24:18
The
24:18
one
24:18
thing
24:18
that
24:19
we're
24:19
not
24:19
so
24:20
certain
24:20
about
24:20
in
24:20
the
24:21
media
24:22
and
24:22
in
24:22
terms
24:22
of
24:23
covering
24:23
Amazon
24:24
is,
24:25
you
24:25
know,
24:25
how
24:25
well
24:26
is
24:26
this
24:26
strategy
24:27
actually
24:27
doing?
24:28
Because
24:29
you
24:29
know,
24:29
they
24:29
keep
24:29
talking
24:30
about
24:30
the
24:30
essentials
24:31
too,
24:31
as
24:32
it
24:32
was
24:32
really
24:33
written
24:33
in
24:33
this
24:33
headline
24:33
to
24:34
you.
24:34
And
24:34
they
24:34
talk
24:35
about
24:35
their,
24:35
their
24:35
market
24:36
share
24:36
in
24:36
grocery
24:37
being
24:37
very
24:37
high.
24:38
But
24:38
generally
24:38
speaking,
24:38
that's
24:39
driven
24:39
by
24:39
the
24:39
essentials
24:40
purchases
24:40
and
24:41
by
24:41
the
24:41
essentials,
24:41
I
24:41
mean,
24:42
not
24:43
all
24:43
the
24:43
stuff
24:43
you
24:43
can
24:43
find
24:44
in
24:44
a
24:44
grocery
24:44
store,
24:45
but
24:45
not
24:45
the
24:45
food.
24:46
Right.
24:46
You
24:46
know,
24:46
it's
24:47
like
24:47
the
24:47
dishwashing
24:48
detergent,
24:49
the
24:49
stuff
24:49
that
24:49
you're
24:49
going
24:49
to
24:49
go,
24:50
the
24:50
laundry,
24:50
the
24:51
laundry
24:51
detergent,
24:51
the
24:52
soap,
24:52
that
24:53
kind
24:53
of
24:53
stuff.
24:53
So,
24:54
and
24:54
that
24:54
might
24:55
still
24:55
be
24:55
working
24:55
like
24:56
this.
24:56
Like
24:56
the,
24:56
you
24:57
can
24:57
get
24:57
your,
24:57
you
24:58
can
24:58
get
24:58
your
24:58
dishwasher
24:59
soap
24:59
with
24:59
your
24:59
electronics.
25:00
Yeah,
25:00
sure,
25:01
right.
25:01
But
25:01
like
25:02
Is
25:02
that
25:02
really
25:02
grocery?
25:03
I
25:03
don't
25:03
know.
25:03
Time's
25:04
going
25:04
to
25:04
tell.
25:04
But
25:04
I
25:04
think
25:04
that's
25:05
one
25:05
caveat
25:05
I
25:06
would
25:06
say,
25:06
you
25:06
know,
25:06
as
25:07
we're
25:07
seeing
25:07
these
25:07
moves
25:08
from
25:08
Amazon,
25:08
it's
25:08
a
25:08
little
25:09
unclear
25:09
to
25:09
me,
25:10
you
25:10
know,
25:10
exactly
25:10
how
25:11
well
25:11
it's.
25:11
Yeah,
25:12
yeah,
25:12
I
25:12
agree
25:12
with
25:12
that.
25:13
It
25:13
goes
25:13
back
25:13
to
25:13
the
25:13
definition
25:14
of
25:14
Omni
25:15
Channel
25:15
all
25:15
products
25:15
to
25:16
all
25:16
consumers
25:16
across
25:17
all
25:17
sales
25:17
channels.
25:18
And
25:18
the
25:18
Amazon
25:19
experience
25:19
doesn't
25:20
quite
25:20
feel
25:20
like
25:20
Omni
25:21
Channel
25:21
grocery
25:22
right
25:22
now.
25:23
It
25:23
feels
25:23
like
25:24
a
25:24
part
25:24
of
25:24
the
25:24
grocery
25:25
store
25:25
which
25:26
goes
25:26
back
25:26
to
25:26
the
25:27
point
25:27
of
25:27
like
25:27
if
25:27
you
25:28
can,
25:28
if
25:28
you
25:28
are
25:30
a
25:30
store
25:30
based
25:30
grocer
25:31
and
25:31
you
25:31
can
25:31
pull
25:32
off
25:32
Omni
25:33
Channel,
25:34
you
25:35
know,
25:35
I
25:35
give,
25:36
I
25:36
give
25:36
the
25:36
nod
25:37
to
25:37
those
25:37
folks.
25:38
Right,
25:38
right.
25:38
Yeah,
25:38
yeah.
25:39
Because
25:39
it
25:39
is
25:39
different
25:39
delivering
25:40
ice
25:40
cream,
25:41
you
25:41
know,
25:41
as
25:41
an
25:41
example
25:42
with
25:42
your
25:43
electronics
25:43
than
25:43
it
25:43
is
25:44
say
25:44
so.
25:45
All
25:45
right,
25:45
let's
25:46
keep
25:46
rolling.
25:46
Headline
25:47
number
25:47
three.
25:47
This
25:47
is
25:47
great.
25:48
Headline
25:48
number
25:48
three
25:49
is
25:49
brought
25:49
to
25:49
you
25:49
by
25:49
Corso.
25:50
Your
25:50
stores
25:50
are
25:51
full
25:51
of
25:51
data,
25:51
but
25:51
are
25:52
your
25:52
teams
25:52
acting
25:53
on
25:53
it?
25:53
Corso
25:54
turns
25:54
retail
25:55
data
25:55
into
25:55
personalized
25:56
daily
25:56
to
25:56
dos
25:57
that
25:57
drive
25:57
sales,
25:57
reduce
25:58
waste
25:58
and
25:58
improve
25:59
execution.
26:00
No
26:00
fluff,
26:01
just
26:01
action.
26:02
Help
26:02
your
26:02
managers
26:02
focus
26:03
on
26:03
what
26:03
matters
26:03
most.
26:04
Visit
26:04
corso.com
26:05
to
26:06
see
26:06
intelligent
26:07
management
26:07
in
26:07
motion.
26:08
Headline
26:08
number
26:09
three.
26:09
New
26:09
York
26:10
legislators
26:10
have
26:11
passed
26:11
the
26:12
One
26:12
Fair
26:13
Price
26:13
act,
26:13
making
26:14
New
26:14
York
26:14
the
26:14
third
26:15
state
26:15
in
26:15
the
26:15
nation
26:15
after
26:16
Maine
26:16
and
26:16
Connecticut
26:17
to
26:17
pass
26:18
legislation
26:19
limiting
26:19
companies
26:20
from
26:20
using
26:20
consumer
26:21
data
26:21
to
26:21
set
26:21
personalized
26:22
prices,
26:23
according
26:23
to
26:23
Chain
26:24
Storage.
26:24
The
26:24
bill
26:25
passed
26:25
on
26:25
June
26:25
4th
26:26
and
26:26
now
26:26
heads
26:26
to
26:26
New
26:27
York's
26:27
governor
26:27
for
26:28
consideration.
26:29
The
26:29
legislation
26:29
targets
26:30
what
26:30
advocates
26:31
call
26:31
surveillance
26:32
pricing,
26:33
which
26:33
is
26:33
the
26:33
practice
26:34
of
26:34
using
26:34
AI
26:34
and
26:35
algorithms
26:35
to
26:35
analyze
26:36
a
26:36
consumer's
26:36
browsing
26:37
history,
26:37
real
26:38
time
26:38
location,
26:38
inferred
26:39
family
26:39
size
26:40
or
26:40
income
26:41
to
26:41
set
26:41
individualized
26:42
prices
26:43
or
26:43
discounts.
26:44
The
26:44
bill
26:45
does,
26:45
however,
26:45
carve
26:46
out
26:46
an
26:46
exception
26:47
for
26:47
what
26:47
it
26:47
calls
26:48
bonafide
26:49
discounts.
26:50
That
26:50
is
26:50
sales
26:51
coupons
26:52
and
26:52
loyalty
26:52
programs
26:53
are
26:53
preserved,
26:54
which
26:54
was
26:54
a
26:54
key
26:55
concern
26:55
raised
26:55
by
26:56
critics
26:56
who
26:56
argue
26:56
the
26:57
legislation
26:58
could
26:58
eliminate
26:58
consumer
26:59
friendly
26:59
targeted
27:00
offers.
27:01
Notably,
27:02
Colorado
27:02
recently
27:03
vetoed
27:03
a
27:03
similar
27:04
surveillance
27:05
pricing
27:05
bill.
27:05
And
27:06
California
27:06
and
27:06
New
27:07
Jersey
27:07
are
27:07
both
27:08
currently
27:08
considering
27:09
comparable
27:09
legislation
27:10
making
27:11
this
27:11
a
27:11
fast
27:12
moving
27:12
patchwork
27:12
of
27:13
state
27:13
level
27:13
action.
27:14
Chap.
27:15
This
27:15
is.
27:16
You're
27:16
the
27:16
lucky,
27:16
the
27:17
lucky
27:17
winner
27:17
this
27:18
week.
27:18
You
27:18
get
27:18
the.
27:19
You
27:19
get
27:19
the
27:19
A
27:19
and
27:19
M.
27:20
Consumer
27:20
and
27:20
retail
27:20
groups
27:21
put
27:21
you
27:21
on
27:21
the
27:22
spot
27:22
question.
27:23
All
27:24
right,
27:24
here
27:24
it
27:24
is.
27:24
This
27:24
one,
27:25
this
27:25
one's
27:25
a.
27:26
They
27:26
went,
27:26
they
27:26
went
27:27
in
27:27
a
27:27
different
27:27
angle
27:28
this
27:28
week
27:28
too.
27:28
Chap.
27:29
So
27:29
better
27:29
you
27:30
than
27:30
me
27:30
on
27:30
this
27:30
one.
27:31
So
27:31
here
27:31
it
27:31
is.
27:33
They
27:34
said,
27:34
quote,
27:34
you
27:35
can't
27:35
stop
27:35
him.
27:36
You
27:36
can
27:36
Only
27:36
hope
27:37
to
27:37
contain
27:37
him
27:38
was
27:38
a
27:38
popular
27:39
Dan
27:39
Patrick
27:40
catchphrase
27:40
on
27:40
SportsCenter
27:41
in
27:41
the
27:41
1990s.
27:42
God,
27:42
I
27:43
just
27:43
read
27:43
this.
27:44
I
27:44
have
27:44
no
27:44
idea
27:44
where
27:44
this
27:44
is
27:44
going
27:45
to
27:45
go.
27:45
And
27:46
that
27:46
was
27:46
to
27:46
describe
27:47
the
27:47
dominant
27:47
athletic
27:48
forces
27:48
who
27:48
were
27:49
simply
27:49
too
27:49
big
27:50
or
27:50
too
27:50
strong
27:51
to
27:51
neutralize.
27:52
So
27:52
their
27:52
question
27:53
is,
27:53
in
27:53
10
27:53
years,
27:54
who
27:55
will
27:55
we
27:55
say
27:55
was
27:56
more
27:56
difficult
27:57
to
27:57
contain
27:58
comparatively?
27:59
Michael
27:59
Jordan
28:00
or
28:00
AI?
28:03
Okay,
28:04
that's
28:04
from
28:04
left
28:04
field.
28:05
Chris.
28:06
I.
28:07
So
28:07
I
28:08
lived
28:08
in
28:08
Chicago
28:09
in
28:09
the
28:09
late
28:09
90s,
28:10
so
28:10
I
28:10
had
28:10
the
28:10
privilege
28:11
of
28:11
seeing
28:11
Jordan
28:12
live.
28:12
Wow.
28:13
Incredible.
28:15
So
28:15
the
28:15
easy
28:15
answer
28:16
to
28:16
this
28:16
question
28:17
is
28:18
AI.
28:18
However,
28:19
the
28:19
key
28:20
point
28:20
in
28:20
the
28:20
question
28:21
was
28:21
in
28:22
10
28:22
years,
28:23
which
28:23
makes
28:23
me
28:23
more
28:24
skeptical,
28:24
actually.
28:25
Really?
28:25
So
28:26
from
28:26
my
28:26
vantage
28:27
point
28:27
at
28:28
Gartner,
28:29
yes,
28:30
AI
28:30
is
28:30
everywhere
28:31
in
28:31
our
28:31
discussions,
28:32
but
28:32
the
28:32
reality
28:33
is,
28:33
in
28:33
my
28:34
domain
28:34
and
28:34
supply
28:35
chain,
28:35
only
28:36
17%
28:37
of
28:37
AI
28:38
pilots
28:38
are
28:38
scaling
28:39
successfully
28:40
in
28:40
the
28:40
supply
28:40
chain
28:40
right
28:41
now.
28:41
Only
28:42
23%
28:43
of
28:43
supply
28:44
chains
28:44
have
28:44
a
28:45
formal
28:45
AI
28:45
strategy.
28:47
Only
28:47
23%.
28:48
Right,
28:49
right,
28:49
right.
28:50
So.
28:51
And
28:51
the
28:51
gap
28:51
between
28:52
what
28:52
the
28:52
vendors
28:53
say
28:53
you
28:53
can
28:53
do
28:53
with
28:54
AI
28:54
and
28:54
what
28:55
folks
28:55
are
28:55
actually
28:56
doing
28:56
with
28:56
AI
28:57
has
28:57
never
28:57
been
28:58
larger.
28:59
And
28:59
if
29:00
we're
29:00
going
29:00
to
29:00
get
29:01
AI
29:01
in
29:01
this
29:01
dominant
29:02
position
29:03
where
29:03
it
29:03
really
29:04
changes
29:05
your
29:05
operating
29:06
model,
29:07
you
29:07
have
29:07
to
29:07
give
29:07
AI
29:08
some
29:08
big,
29:08
big
29:09
decision
29:09
rights.
29:10
That's
29:11
going
29:11
to
29:11
take
29:12
time,
29:13
a
29:13
long
29:13
time
29:14
to
29:14
get
29:15
right.
29:15
So
29:16
maybe
29:17
I'm
29:17
giving
29:17
the
29:17
edge
29:17
to
29:18
MJ
29:18
here.
29:19
Okay.
29:20
Wow.
29:20
Okay,
29:20
wait.
29:21
Okay,
29:21
so
29:21
first
29:21
of
29:21
all,
29:21
I
29:22
want
29:22
to
29:22
say,
29:22
so
29:23
the
29:23
goat,
29:23
Michael
29:23
Jordan
29:24
still
29:24
beats
29:24
AI.
29:25
Wow.
29:25
That
29:25
tells
29:26
you
29:26
how.
29:26
That
29:26
tells
29:26
you.
29:27
It
29:27
tells
29:28
you
29:28
how
29:28
awesome
29:28
Michael
29:28
Jordan
29:29
was
29:29
as
29:29
a
29:29
basketball
29:30
player.
29:30
What
29:30
it.
29:31
What
29:31
it
29:31
also
29:31
tells
29:31
me
29:32
is
29:32
that,
29:32
you
29:32
know,
29:32
and
29:33
the
29:33
reason
29:33
for
29:33
the
29:34
A
29:34
M
29:34
Consumer
29:35
Retail
29:35
Group's
29:35
question
29:35
is
29:35
like,
29:36
you
29:36
know,
29:36
I
29:36
think
29:36
they're
29:37
kind
29:37
of,
29:37
you
29:37
know,
29:37
you
29:38
know,
29:38
asking
29:38
like,
29:39
you
29:39
know,
29:39
what's
29:40
the
29:40
point
29:40
if
29:40
AI
29:41
is
29:41
going
29:41
to
29:42
always
29:42
be,
29:43
you
29:43
know,
29:43
out
29:44
there
29:44
and
29:44
such
29:45
an
29:45
important
29:45
thing
29:46
in
29:46
terms
29:46
of
29:46
how
29:47
retailers
29:47
do
29:47
business,
29:48
how
29:48
consumers
29:48
interact
29:49
with
29:49
things,
29:49
is
29:50
it
29:50
even
29:50
possible
29:50
to
29:50
keep
29:51
up?
29:51
And
29:52
your
29:52
point.
29:52
Your
29:52
point
29:53
that
29:53
I'm
29:53
hearing
29:53
you
29:53
say
29:54
is,
29:55
yeah,
29:55
there
29:55
actually
29:56
may
29:56
be
29:56
a
29:56
point
29:56
to
29:56
trying
29:57
to
29:57
keep
29:57
up
29:57
with
29:57
it
29:57
because
29:58
it
29:58
may
29:58
not
29:58
happen
29:58
as
29:59
quickly
29:59
as
30:00
we
30:00
think
30:01
it's
30:01
going
30:01
to.
30:01
And
30:02
so
30:02
then
30:02
my
30:02
question
30:02
to
30:03
you
30:03
comes
30:03
down
30:03
to,
30:03
if
30:04
you
30:04
look
30:04
back
30:04
at
30:04
the
30:04
legislation
30:05
like
30:05
I
30:05
just
30:06
described
30:06
in
30:06
the
30:06
headline,
30:07
what
30:07
do
30:07
you
30:07
think
30:08
it
30:08
gets
30:08
right
30:08
and
30:08
what
30:09
do
30:09
you
30:09
think
30:09
it
30:09
gets
30:09
wrong
30:10
when
30:10
it
30:10
comes
30:10
to
30:11
regulating
30:11
pricing
30:12
as
30:12
it's
30:13
trying
30:13
to
30:13
do
30:13
across
30:13
retail?
30:15
Yeah.
30:16
You
30:17
know,
30:17
Chris,
30:17
I
30:18
think
30:18
it's
30:18
sad
30:19
that
30:19
we
30:19
need
30:19
legislation
30:20
to
30:20
deal
30:21
with
30:21
these
30:21
kinds
30:21
of
30:21
issues.
30:22
You
30:22
know,
30:23
while
30:23
dynamic
30:24
pricing
30:24
is
30:24
happening
30:25
all
30:25
around
30:25
us,
30:26
right?
30:26
Concert
30:26
tickets,
30:27
hotels,
30:27
Uber
30:27
rides,
30:28
like,
30:28
the
30:29
consumer
30:29
remains
30:30
very
30:30
sensitive
30:31
to
30:31
this
30:31
issue
30:32
on
30:32
product
30:32
pricing.
30:33
And
30:34
smart
30:34
retailers
30:35
would
30:35
tread
30:35
very
30:36
carefully
30:36
here
30:37
without
30:37
the
30:38
need
30:38
for
30:38
some
30:38
law
30:39
to
30:39
prevent
30:40
them
30:40
from
30:40
doing
30:41
so.
30:42
My
30:42
advice
30:43
to
30:43
a
30:43
retailer
30:43
would
30:44
be
30:44
to
30:44
lean
30:44
into
30:45
personalization
30:46
in
30:47
other
30:47
ways
30:47
that
30:48
can
30:48
still
30:48
drive
30:48
margin
30:49
optimization.
30:50
And
30:50
we
30:50
know
30:51
that
30:51
personalization
30:52
overall
30:52
as
30:52
a
30:52
strategy
30:53
works.
30:53
Like
30:53
Gartner
30:54
research
30:54
says,
30:55
consumers
30:55
are
30:56
1.3
30:56
times
30:57
more
30:57
likely
30:57
to
30:57
make
30:58
a
30:58
purchase
30:59
when
30:59
it
30:59
is
30:59
personalized.
31:01
And
31:02
for
31:02
me,
31:02
so
31:03
I'll
31:03
just
31:03
give
31:03
you
31:03
an
31:03
example
31:04
as
31:04
a
31:04
supply
31:04
chain
31:05
guy,
31:05
personalization
31:06
could
31:07
be
31:07
about
31:08
how
31:08
do
31:08
we
31:08
use
31:08
supply
31:09
chain
31:09
data
31:09
to
31:10
personalize
31:11
and
31:11
optimize
31:12
the
31:12
fulfillment
31:13
offer
31:13
itself,
31:14
right?
31:14
We
31:14
know
31:15
the
31:15
consumer
31:15
wants
31:16
personalized
31:17
delivery
31:17
experiences.
31:18
So
31:19
I
31:19
actually
31:19
did
31:19
some
31:20
consumer
31:20
research
31:20
on
31:21
this
31:21
last
31:21
year
31:21
where
31:21
we
31:22
asked
31:22
consumers
31:23
about
31:23
speed
31:23
over
31:24
reliability
31:25
and
31:26
accuracy.
31:26
Reliability
31:27
was
31:27
actually
31:28
more
31:28
appealing
31:29
than
31:29
raw
31:29
speed.
31:30
And
31:30
that,
31:31
but
31:31
that,
31:31
that,
31:32
that
31:32
accuracy,
31:33
that,
31:33
that's
31:33
personalization,
31:34
right?
31:34
That's
31:35
the
31:35
promise
31:35
for
31:35
you
31:36
based
31:36
on
31:36
where
31:36
you
31:37
are.
31:37
And
31:38
we
31:38
know
31:38
conversion
31:39
rates
31:39
go
31:39
up
31:39
3
31:39
to
31:40
7%
31:40
when
31:40
we
31:41
personalize
31:41
the,
31:41
the
31:42
delivery
31:42
estimate
31:43
on
31:43
the
31:43
product
31:43
detail
31:44
page.
31:45
And
31:45
you
31:45
know,
31:46
when
31:46
I
31:46
talk
31:46
to
31:46
retailers
31:47
who
31:47
are
31:47
always
31:47
right,
31:48
complaining
31:49
about
31:49
the
31:49
speed
31:50
expectations
31:51
that
31:51
Amazon
31:52
sets,
31:53
you
31:53
know,
31:53
my,
31:53
my,
31:54
my
31:54
comeback
31:54
to
31:55
them
31:55
is,
31:56
well,
31:56
Amazon
31:56
set
31:57
another
31:58
expectation
31:58
for
31:58
the
31:59
consumer,
31:59
and
31:59
that's
32:00
the
32:00
personalization
32:01
of
32:01
the
32:01
delivery
32:02
offer,
32:02
right?
32:02
We
32:03
all
32:03
get
32:03
a
32:04
different
32:04
delivery
32:04
experience
32:05
based
32:05
on
32:06
where
32:06
we're
32:06
browsing
32:07
from
32:07
and
32:07
our
32:07
browsing
32:08
history,
32:08
our
32:08
order
32:08
history,
32:09
et
32:09
cetera.
32:09
So
32:10
that
32:11
expectation
32:11
has
32:11
been
32:12
set
32:12
for
32:12
the
32:12
consumer
32:13
as
32:13
well,
32:13
which
32:13
is
32:13
an
32:14
area
32:14
I
32:14
think
32:15
retailers
32:15
can
32:16
lean
32:16
into.
32:17
Right?
32:17
And
32:18
when
32:18
we
32:18
shift
32:18
our
32:19
thinking
32:19
this
32:19
way
32:20
to
32:20
using
32:20
supply
32:21
chain
32:21
data
32:21
to
32:22
help
32:22
personalize
32:23
the
32:23
offer,
32:24
that,
32:25
that's,
32:25
that,
32:25
that's
32:26
a
32:26
mar.
32:26
That's
32:26
a
32:27
margin
32:27
driver.
32:27
So
32:28
when
32:28
I,
32:28
when
32:28
I
32:29
search
32:29
for
32:30
men's
32:31
khaki
32:31
pants
32:31
on
32:32
your
32:32
website,
32:33
like
32:33
today,
32:34
that's
32:34
a
32:34
merchant's
32:35
job
32:35
to
32:35
figure
32:35
out
32:35
which
32:36
five
32:36
pairs
32:36
of
32:36
pants
32:37
appear
32:37
above
32:37
the
32:38
fold.
32:38
But
32:39
what
32:39
about
32:39
using
32:40
supply
32:40
chain
32:40
data
32:41
too?
32:41
You
32:41
know
32:41
which,
32:41
in
32:42
which
32:42
of
32:42
these
32:42
pants
32:43
are
32:43
in
32:43
stock
32:43
near
32:44
you
32:44
that
32:44
I
32:44
can
32:44
deliver
32:44
at
32:45
a
32:45
more
32:45
profitable
32:45
cost.
32:46
Like.
32:47
So
32:47
this
32:47
is
32:47
just
32:48
like
32:48
one
32:48
example
32:49
of
32:49
personalization
32:50
that
32:50
I
32:50
think
32:51
retailers
32:52
can
32:52
be
32:52
leaning
32:52
into
32:54
that
32:54
stays
32:55
away
32:55
from
32:55
this
32:56
red
32:56
line
32:57
of
32:57
dynamic
32:58
product
32:59
pricing
32:59
that
33:00
I
33:00
think
33:00
retailers,
33:02
smart
33:02
retailers
33:03
will
33:03
avoid.
33:04
And
33:04
now,
33:05
you
33:05
know,
33:05
unfortunately,
33:05
it
33:06
seems
33:06
like
33:06
some
33:06
states
33:06
are
33:07
jumping
33:07
ahead
33:07
to
33:07
ensure
33:08
that
33:08
the
33:09
not
33:09
smart
33:09
retailers
33:10
will
33:10
not
33:10
cross
33:11
this
33:11
line
33:11
even
33:11
if
33:12
they
33:12
try.
33:12
So
33:13
that's
33:13
my
33:13
take
33:13
on
33:14
it.
33:14
Right,
33:15
right.
33:15
Yeah.
33:15
And,
33:16
yeah,
33:16
no,
33:16
you're
33:16
bringing
33:17
up
33:17
good
33:17
points,
33:17
too.
33:17
And
33:17
then,
33:17
like,
33:18
where
33:18
does,
33:18
where
33:18
does,
33:18
like,
33:19
the
33:19
shipping
33:19
costs
33:20
fall
33:20
into
33:20
this
33:20
legislation
33:21
too?
33:21
Like,
33:21
is
33:22
that
33:22
a
33:22
product
33:22
price
33:22
or
33:23
is
33:23
that
33:23
something
33:23
that's
33:23
happening
33:24
ancillary
33:24
from
33:25
all
33:25
this?
33:25
Like,
33:26
it
33:26
gets
33:26
really
33:26
complicated
33:27
really
33:27
fast.
33:28
And
33:28
so,
33:28
like,
33:28
you
33:29
know,
33:29
I
33:29
think
33:29
overall
33:29
it's,
33:30
it's
33:30
grounded
33:31
in
33:31
the
33:31
right
33:31
direction
33:32
as
33:32
an
33:32
idea.
33:33
And
33:33
I
33:33
think
33:33
what
33:34
they're
33:34
trying
33:34
to
33:34
prevent
33:34
is
33:34
like
33:35
the
33:35
Instacart
33:35
example
33:36
that
33:36
we
33:36
had
33:36
on
33:36
the
33:36
show
33:36
a
33:36
couple
33:37
months
33:37
ago
33:37
where
33:37
there
33:37
was
33:38
just
33:38
blatantly
33:38
different
33:39
prices
33:39
for
33:40
different
33:40
people.
33:42
And
33:42
so
33:42
I'm
33:42
all
33:42
for,
33:43
you
33:43
know,
33:43
protecting
33:43
that,
33:44
but
33:44
the
33:44
couple
33:44
of
33:45
things
33:45
I
33:45
would
33:45
say,
33:46
just
33:46
for
33:46
anyone,
33:47
you
33:47
know,
33:47
listening,
33:47
particularly
33:48
if
33:48
you're
33:48
in
33:48
government
33:48
and
33:48
you're
33:49
thinking
33:49
about
33:49
doing
33:49
this,
33:49
like,
33:50
the
33:50
one
33:50
thing
33:51
that
33:51
is
33:51
driving
33:51
me
33:52
absolutely
33:52
crazy
33:53
now
33:53
is
33:53
all
33:53
the
33:53
negative
33:54
blowback
33:54
on
33:55
ESLs,
33:56
particularly
33:56
around
33:56
dynamic
33:57
prices.
33:57
Because
33:58
if
33:58
you
33:58
think
33:58
that
33:58
that's
33:59
going
33:59
to
33:59
happen,
34:00
you're
34:00
just
34:00
totally,
34:01
you're
34:01
just,
34:02
you're
34:02
not
34:02
getting
34:03
it
34:03
because
34:03
that's
34:03
not
34:03
how
34:04
retail
34:04
works.
34:04
No
34:05
retailer
34:05
is
34:05
going
34:05
to
34:06
put
34:06
dynamic
34:06
pricing
34:07
down
34:07
their
34:07
aisles,
34:08
you
34:08
know,
34:08
as
34:08
different
34:09
individuals
34:09
are
34:09
coming
34:10
down
34:10
them.
34:10
So
34:11
it's
34:11
just
34:11
not
34:11
going
34:11
to
34:12
happen.
34:12
So
34:12
just
34:13
stop,
34:13
you
34:13
know,
34:14
stumping
34:14
that.
34:15
But,
34:15
you
34:15
know,
34:15
I
34:16
think
34:16
with
34:16
this,
34:16
I
34:16
think
34:16
there,
34:17
there
34:17
is
34:17
some,
34:17
there
34:17
is
34:18
some
34:18
things
34:18
here
34:18
and
34:18
I
34:18
think
34:18
you've
34:19
got
34:19
to
34:19
keep.
34:21
So
34:21
I'm
34:21
all
34:21
for
34:22
using.
34:23
I'm.
34:23
I
34:23
had
34:24
this
34:24
conversation
34:24
with
34:24
Scott
34:25
Price
34:25
of
34:25
the,
34:25
he's,
34:26
he's
34:26
a
34:26
CEO
34:27
over
34:27
in,
34:27
in
34:28
Asia
34:28
of
34:29
a
34:29
DFI
34:29
group.
34:30
And
34:30
he
34:30
said,
34:30
like,
34:31
yes,
34:31
we
34:31
have
34:31
to
34:31
be
34:32
very
34:32
smart
34:32
about
34:32
how
34:33
we
34:33
monitor
34:33
our
34:33
reg
34:34
pricing
34:34
and
34:34
the
34:35
personal
34:35
data
34:35
we're
34:35
using
34:36
to
34:37
decide
34:37
what
34:37
those
34:38
reg
34:38
prices
34:38
are.
34:39
But
34:39
we
34:40
have
34:40
to
34:40
have
34:40
the
34:40
ability
34:41
to,
34:41
you
34:42
know,
34:42
make
34:42
the
34:42
promotional
34:43
decisions
34:43
that
34:44
we
34:44
want
34:44
to
34:44
make
34:44
because
34:45
that's
34:45
where
34:45
the
34:45
merchant
34:46
really
34:46
comes
34:46
into
34:47
play.
34:47
And
34:47
it
34:48
sounds
34:48
like
34:48
the
34:48
legislation,
34:49
for
34:49
the
34:49
most
34:49
part,
34:50
you
34:50
know,
34:50
has
34:50
got
34:51
that,
34:52
you
34:52
know,
34:52
and
34:53
the
34:53
legislation
34:53
that
34:53
we
34:53
just
34:54
described
34:54
is
34:54
thinking
34:55
about
34:55
that,
34:55
you
34:56
know,
34:56
that
34:56
they're
34:56
still
34:56
allowing
34:57
that
34:57
to
34:57
happen.
34:57
So
34:58
that,
34:58
so
34:58
that's
34:59
good.
34:59
The
34:59
part,
34:59
the
34:59
part
35:00
that
35:00
sticks
35:00
with
35:00
me,
35:00
though,
35:00
is
35:01
the
35:01
whole
35:01
geography
35:01
thing.
35:02
I
35:02
think,
35:02
you
35:03
know,
35:03
the
35:03
geography
35:03
thing
35:03
is
35:04
just
35:04
really
35:04
tricky,
35:04
you
35:04
know,
35:05
I
35:05
mean,
35:05
you
35:05
know,
35:06
because
35:06
like,
35:06
if
35:06
you're
35:07
a
35:07
store,
35:08
if
35:08
you're
35:08
a
35:08
store
35:09
in.
35:09
I'll
35:09
just
35:10
make
35:10
states
35:10
up,
35:10
Seattle
35:11
versus
35:11
Arizona.
35:12
Those
35:12
stores
35:12
are
35:12
gonna
35:12
have
35:12
different
35:13
prices.
35:13
And
35:14
so,
35:14
like
35:14
online,
35:15
you're
35:15
gonna
35:15
have
35:15
different
35:15
prices
35:16
based
35:16
on
35:16
that
35:17
geography.
35:18
Too.
35:18
And
35:18
so,
35:19
so
35:19
I
35:19
think
35:19
that
35:19
is
35:20
a
35:20
very
35:20
complicating
35:21
factor
35:21
for
35:22
retailers
35:22
to
35:22
get
35:22
around
35:23
and
35:23
is
35:23
going
35:23
to
35:23
be
35:24
very
35:24
hamstringing
35:25
for,
35:25
for
35:26
them
35:26
as
35:26
well.
35:26
And
35:26
so
35:27
the
35:27
legislation,
35:27
you
35:27
know,
35:28
in
35:28
that
35:28
regard
35:28
doesn't
35:28
appear
35:28
to
35:29
answer
35:29
that
35:29
for
35:29
me,
35:29
Chad.
35:31
Yeah,
35:31
no,
35:31
I
35:32
agree.
35:32
And
35:32
yeah,
35:33
where
35:33
do
35:33
we,
35:33
like
35:34
you
35:34
mentioned
35:34
shipping
35:35
costs?
35:35
Like
35:35
we've
35:35
been
35:35
charging
35:36
more
35:36
for
35:36
Alaska
35:37
Hanawaii
35:38
shipments
35:38
for
35:38
like
35:38
two
35:38
decades.
35:39
Right.
35:39
Because
35:40
we're
35:40
penalizing
35:40
people
35:41
that
35:41
live
35:41
there.
35:41
Right.
35:42
So.
35:43
Right,
35:43
yeah,
35:43
it's
35:44
already
35:44
happening.
35:45
It's
35:45
happening
35:46
already.
35:46
So
35:47
that's
35:47
a
35:47
really
35:47
good
35:47
point.
35:48
Yeah.
35:48
Wow.
35:48
Yeah.
35:49
Yep.
35:50
All
35:50
right,
35:50
Chad,
35:50
before
35:51
we
35:51
get
35:51
to
35:51
headline
35:51
number
35:51
four,
35:52
let's
35:52
welcome
35:52
Tom
35:53
on
35:53
today's
35:54
program.
36:02
All
36:03
right.
36:03
Joining
36:03
us
36:03
now
36:04
for
36:04
five
36:04
insightful
36:05
minutes
36:05
is
36:06
Tom
36:06
Cooper.
36:06
Tom
36:07
Cooper
36:07
is
36:07
the
36:07
CEO
36:08
of
36:08
Duvo
36:09
and
36:09
he's
36:09
here
36:09
to
36:09
give
36:10
us
36:10
an
36:10
update
36:10
on
36:11
what
36:11
he's
36:11
seeing
36:11
in
36:12
the
36:12
market
36:12
in
36:12
terms
36:12
of
36:13
retailers
36:13
abilities
36:14
to
36:14
adapt
36:14
to
36:15
AI
36:15
solutions
36:16
to
36:16
their
36:16
organizational
36:17
benefit.
36:18
Tom,
36:18
how
36:19
are
36:19
you
36:19
doing?
36:19
Yeah,
36:19
I'm
36:20
good.
36:20
Good
36:20
to
36:20
see
36:20
you
36:20
again,
36:20
Chris.
36:21
How
36:21
are
36:21
you?
36:21
You
36:22
too.
36:22
Yeah,
36:22
no,
36:22
it's
36:23
been,
36:23
I'm
36:23
really
36:23
excited
36:24
about
36:24
this
36:24
interview.
36:24
And
36:25
we
36:25
are
36:25
also
36:25
live
36:26
from
36:26
Shop
36:26
Talk
36:27
Europe
36:27
too
36:27
in
36:27
the
36:27
speakers
36:28
lounge.
36:28
So
36:29
we're
36:29
coming
36:29
at
36:29
you
36:30
fresh
36:30
and
36:31
bright
36:31
eyed
36:31
and
36:31
bushy
36:32
tailed
36:32
on
36:32
this
36:33
Wednesday
36:33
morning.
36:33
All
36:34
right,
36:34
Tom,
36:34
let's
36:34
start
36:34
with
36:35
this.
36:36
I
36:36
haven't
36:36
stopped
36:37
talking
36:37
about
36:37
our
36:37
interview,
36:37
you
36:38
know,
36:38
the
36:38
one
36:38
we
36:38
did
36:38
back
36:39
in
36:39
March,
36:40
the
36:40
one
36:40
we
36:40
did
36:40
earlier
36:41
this
36:41
year.
36:41
So
36:41
remind
36:41
our
36:42
audience
36:42
of
36:42
who
36:42
you
36:42
are
36:43
and
36:43
what
36:43
it
36:43
is
36:44
that
36:44
Duvo
36:44
does
36:44
first.
36:45
Yeah.
36:46
So
36:46
I'm
36:46
Tom
36:47
and
36:48
I'm
36:49
the
36:49
founder
36:49
of
36:51
great
36:52
European
36:53
retailer
36:54
2
36:55
billion
36:56
and
36:57
delivering
36:57
amazing
36:58
food
36:58
to
36:58
people
36:59
where
37:00
I've
37:00
learned
37:01
that.
37:01
And
37:02
what
37:02
is
37:02
the
37:02
name
37:02
of
37:02
that
37:03
company?
37:03
It's
37:04
called
37:04
Rohli.
37:05
I
37:05
mean
37:05
like
37:06
nobody
37:06
knows
37:06
that.
37:07
Some
37:07
random
37:07
2
37:08
billion
37:08
dollar
37:08
Europe,
37:08
European
37:09
retailer.
37:10
But
37:10
I've
37:11
seen
37:11
so
37:11
much
37:12
pain
37:13
when
37:13
I
37:14
observed
37:15
people
37:15
working
37:16
for
37:16
that
37:16
company.
37:17
It's
37:17
essentially
37:18
endless
37:18
hell
37:19
of
37:20
emails
37:20
and
37:20
Excel
37:21
files
37:21
and
37:21
dashboards
37:22
and
37:22
Chrome
37:23
tabs
37:23
and
37:24
SAP.
37:25
So
37:26
I
37:26
also
37:27
co
37:27
founded
37:27
Duvo
37:29
and
37:29
Duvo
37:30
solves
37:30
all
37:30
of
37:31
that.
37:31
So
37:33
we
37:33
identify
37:34
processes
37:35
that
37:35
are
37:35
automatable
37:36
in
37:36
companies,
37:38
we
37:38
suggest
37:38
how
37:39
to
37:39
optimize
37:39
them
37:40
and
37:40
we
37:40
just
37:40
run
37:40
them
37:41
after
37:41
that.
37:41
Right.
37:42
That's
37:42
what
37:42
Duo
37:43
does.
37:43
Yeah.
37:43
And
37:44
AI
37:44
is
37:44
actually
37:44
making
37:45
that
37:45
possible.
37:45
And
37:45
that's
37:46
what
37:46
stood
37:47
out
37:47
to
37:47
me
37:47
when
37:47
we
37:48
spoke
37:48
back
37:49
in
37:49
March.
37:49
Because
37:49
my
37:49
big
37:50
takeaway
37:50
was
37:50
that
37:52
you
37:52
help
37:52
to
37:53
take
37:53
away
37:53
the
37:53
least
37:54
glamorous
37:55
tasks
37:55
in
37:56
retail.
37:56
The
37:57
things
37:57
that
37:57
people
37:57
either
37:58
forget
37:58
to
37:58
do
37:58
or
37:59
don't
37:59
want
37:59
to
37:59
do.
38:00
And
38:00
why
38:00
is
38:00
that
38:00
such
38:01
an
38:01
important
38:01
element
38:02
of
38:02
what
38:02
it
38:02
is
38:02
that
38:02
Duvo's
38:03
doing?
38:04
Because,
38:04
you
38:05
know,
38:05
ultimately
38:07
this
38:08
is
38:08
where
38:08
value
38:09
is
38:09
not
38:09
created.
38:11
Right.
38:11
And
38:13
I
38:13
would
38:14
assume
38:15
from
38:15
what
38:15
we
38:16
observed
38:16
that
38:16
at
38:16
least
38:16
half
38:17
of
38:17
the
38:17
time
38:18
super
38:18
smart
38:19
people
38:19
spend
38:19
time
38:20
on
38:20
is
38:20
this
38:21
is
38:22
being
38:22
human
38:22
API
38:23
between
38:23
systems
38:24
that
38:24
don't
38:24
talk
38:24
to
38:24
each
38:25
other.
38:25
Right.
38:26
And
38:27
I
38:27
think
38:27
that
38:27
that's
38:28
why,
38:28
because
38:28
if
38:28
you,
38:29
if
38:29
you
38:29
think
38:30
about
38:30
this,
38:30
if
38:30
you
38:31
double
38:31
the
38:31
capacity
38:32
of
38:32
your
38:32
smart
38:32
people,
38:34
how
38:34
much
38:34
better
38:35
retailer
38:35
you
38:36
could
38:36
actually
38:36
be.
38:37
Right?
38:38
And
38:38
then
38:38
if
38:38
you
38:38
think
38:39
about
38:39
it
38:39
further,
38:40
what
38:40
if
38:41
the
38:42
AI
38:42
could
38:42
do
38:42
things
38:43
that
38:43
you
38:43
don't
38:43
do
38:44
today
38:44
at
38:44
all?
38:45
Right.
38:46
So
38:46
many
38:46
processes
38:47
get
38:47
overlooked,
38:49
so
38:49
many
38:49
things
38:50
don't
38:51
do,
38:52
or
38:52
so
38:52
many
38:52
processes
38:53
are
38:53
not
38:53
run
38:54
at
38:54
scale.
38:55
You
38:55
know,
38:55
you
38:55
maybe
38:56
look
38:56
at
38:56
the
38:56
biggest
38:57
fires,
38:57
but
38:57
not
38:57
all
38:58
of
38:58
the
38:58
cases.
38:59
So
38:59
I
38:59
think
39:00
this
39:00
is
39:00
why
39:01
it's
39:01
important.
39:02
It
39:03
just
39:03
gives
39:03
you
39:03
the
39:04
unlimited
39:04
workforce
39:05
in
39:05
a
39:06
way,
39:07
both
39:07
on
39:07
a
39:07
human
39:08
side,
39:08
but
39:08
also
39:09
on
39:09
the
39:09
AI
39:09
side.
39:10
Yeah,
39:10
that's
39:10
interesting
39:11
too.
39:11
You
39:11
said
39:11
we
39:11
don't
39:12
create
39:12
value
39:12
from
39:13
that
39:13
work,
39:13
but
39:13
we
39:14
leak
39:14
a
39:14
lot
39:14
of
39:14
value
39:15
from
39:15
that
39:15
work,
39:15
I
39:16
imagine
39:16
too.
39:16
Right.
39:17
All
39:17
right,
39:17
so
39:17
I'm
39:18
curious
39:18
too.
39:18
I
39:18
wanted
39:18
to
39:18
ask
39:18
you
39:19
this.
39:19
So
39:20
founder,
39:21
retailer,
39:22
tech
39:22
company,
39:23
what
39:23
vantage
39:23
point
39:24
does
39:24
that
39:24
give
39:25
you
39:25
relative
39:26
to
39:26
how
39:26
the
39:26
marketplace
39:28
is
39:28
operating
39:29
in
39:29
terms
39:29
of
39:29
how
39:30
they're
39:30
thinking
39:30
about
39:30
AI?
39:33
I
39:33
think
39:33
ultimately,
39:35
I'm
39:35
not
39:35
naive,
39:36
right?
39:37
Like
39:37
running
39:38
AI
39:39
in
39:39
production
39:42
at
39:42
large
39:43
scale,
39:44
where
39:44
you
39:45
actually
39:45
rely
39:46
on
39:46
the
39:46
AI
39:47
not
39:47
to
39:47
break
39:48
the
39:48
company.
39:49
I
39:50
think
39:50
that's
39:51
something
39:52
you
39:52
have
39:52
to
39:53
understand
39:53
from
39:54
inside.
39:54
It's
39:55
not
39:55
like,
39:55
hey,
39:55
hey,
39:56
I
39:56
have
39:56
a
39:56
couple
39:57
agents,
39:57
do
39:57
you
39:57
want
39:57
to
39:58
kind
39:58
of
39:58
deploy
39:58
them
39:58
in
39:59
the
39:59
company?
39:59
It
40:00
may
40:00
work,
40:00
may
40:01
not,
40:01
let's
40:01
see.
40:02
Right?
40:03
That's
40:03
the
40:03
posture
40:04
that
40:04
I
40:04
see
40:04
a
40:04
lot
40:05
in
40:05
the
40:05
market.
40:06
Let's
40:06
do
40:06
a
40:06
poc.
40:07
Great.
40:08
But
40:08
ultimately,
40:09
if
40:10
you
40:10
deploy
40:11
AI
40:11
on
40:12
your
40:13
most
40:13
critical
40:13
business
40:14
processes,
40:15
it
40:15
needs
40:16
to
40:16
run
40:16
10,000
40:17
times
40:17
per
40:17
day,
40:18
super
40:18
reliably
40:20
for
40:21
years.
40:22
Right.
40:23
And
40:23
I
40:23
think
40:24
that's
40:24
just
40:25
very
40:25
unique
40:25
vantage
40:26
point.
40:27
Also.
40:29
There's
40:30
like
40:30
a
40:31
clear
40:32
advantage
40:35
of
40:36
understanding
40:36
that
40:37
nobody
40:37
wants
40:38
to
40:38
change
40:38
their
40:38
systems.
40:40
Right?
40:40
So
40:41
it's
40:41
kind
40:41
of.
40:41
I
40:41
see
40:41
a
40:42
lot
40:42
of
40:42
startups,
40:42
right?
40:43
Hey,
40:43
hey,
40:43
so
40:43
just
40:44
change
40:44
your
40:44
system.
40:44
I
40:45
have
40:45
this
40:45
new
40:45
erp
40:46
and
40:46
your
40:47
problems
40:47
will
40:48
be
40:48
gone.
40:48
And
40:49
I'm
40:49
like,
40:50
that's
40:50
gonna
40:50
cost
40:51
me
40:51
$50
40:51
million
40:53
and
40:53
two
40:53
years
40:53
of
40:54
time.
40:55
No,
40:55
thank
40:55
you.
40:55
Right.
40:56
So
40:56
it's
40:56
kind
40:56
of
40:57
just
40:57
like
40:57
we
40:57
always
40:58
knew
40:58
we
40:58
have
40:58
to
40:59
work
40:59
on
40:59
top
40:59
of
40:59
existing
41:00
systems.
41:00
Right?
41:02
Because
41:02
once
41:03
you
41:03
talk
41:03
about
41:04
some
41:04
SAP
41:05
integration
41:06
Change
41:07
of
41:07
erp.
41:10
No
41:10
sane
41:11
enterprise
41:11
will
41:12
touch
41:12
you.
41:12
So
41:13
I
41:13
think
41:13
that's
41:14
also
41:15
fairly
41:15
unique
41:15
understanding.
41:16
Yeah,
41:17
that's
41:17
funny
41:17
because
41:17
I
41:18
was
41:18
gonna
41:18
ask.
41:18
That
41:19
was
41:19
going
41:19
to
41:19
be.
41:19
My
41:19
next
41:19
question
41:20
is.
41:20
Okay.
41:20
You
41:20
know,
41:20
you
41:20
have
41:20
that
41:21
unique
41:21
kind
41:22
of
41:22
blend
41:22
of
41:23
roles
41:23
here.
41:23
And
41:23
I
41:23
was
41:23
curious
41:24
what
41:24
advantage
41:24
it
41:25
plays
41:25
on
41:25
the
41:25
software
41:25
side
41:26
too.
41:26
And
41:26
I
41:26
think
41:26
you
41:26
hit
41:26
it.
41:27
It's
41:27
like
41:27
you
41:27
have
41:27
an
41:28
understanding
41:28
of
41:28
what
41:28
you're
41:29
actually
41:29
going
41:29
to
41:29
be
41:29
able
41:30
to
41:30
sell
41:30
into
41:30
a
41:30
retailer.
41:31
I
41:32
think
41:32
there's
41:32
one
41:32
more.
41:33
Okay,.
41:36
I
41:36
understand
41:37
there's
41:38
still
41:38
gonna
41:38
be
41:39
people
41:39
in
41:39
the
41:39
company.
41:40
Right.
41:41
What
41:41
I
41:41
see
41:41
a
41:41
lot
41:41
of
41:42
tech
41:42
companies
41:43
kind
41:43
of
41:43
assuming
41:44
is,
41:45
hey,
41:45
you
41:46
know,
41:46
guess
41:46
what?
41:47
I'm
41:47
just
41:47
gonna
41:48
automate
41:48
your
41:48
20
41:49
billion
41:49
enterprise
41:50
with
41:50
agents
41:51
so
41:51
you
41:52
can
41:52
get
41:52
rid
41:52
of
41:52
everybody.
41:53
And
41:55
that's
41:55
funny.
41:56
Right.
41:57
And
41:58
if
41:58
you
41:58
have
41:58
that
41:58
understanding,
41:59
there's
41:59
still
42:00
gonna
42:00
be
42:00
people.
42:01
Right.
42:01
You
42:02
have
42:02
to
42:02
build
42:02
both
42:03
for
42:03
people
42:03
and
42:04
agents.
42:05
Right.
42:05
And
42:06
you're
42:06
trying
42:06
to
42:07
create
42:07
the
42:07
environment
42:08
where
42:08
people
42:09
are
42:10
becoming
42:10
almost
42:11
like
42:11
agent
42:11
operators.
42:12
Right.
42:13
As
42:13
opposed
42:13
to
42:13
replacing
42:14
everybody
42:15
or
42:16
automating
42:16
everything.
42:19
And
42:19
that's,
42:20
I
42:20
think,
42:20
also
42:20
driven
42:21
by
42:23
knowing
42:23
the
42:24
company,
42:24
like
42:25
retail
42:25
company
42:25
from
42:26
inside.
42:27
You
42:27
know
42:27
full
42:27
well
42:28
when
42:28
you're
42:28
inside,
42:29
you're
42:29
not
42:29
going
42:29
to
42:30
automate
42:30
everything.
42:30
Yeah.
42:31
And
42:31
that's
42:31
what
42:31
I
42:31
learned
42:32
from
42:32
you
42:32
the
42:32
first
42:32
time,
42:32
particularly
42:33
in
42:33
the
42:33
first
42:33
wave
42:34
of
42:34
AI.
42:34
Like,
42:34
the
42:35
first
42:35
wave
42:35
of
42:35
AI
42:36
to
42:36
do
42:36
this.
42:36
Right.
42:36
Is
42:37
going
42:37
to
42:37
make
42:37
your
42:38
people
42:38
better
42:38
at
42:39
what
42:39
they
42:39
do.
42:39
And
42:40
that's
42:40
the
42:40
right
42:40
way
42:40
to
42:41
think
42:41
about
42:41
it
42:41
first
42:41
versus
42:42
like
42:42
going
42:42
at
42:42
it
42:42
from
42:43
like,
42:43
how
42:43
do
42:43
I
42:43
just
42:44
reduce
42:44
headcount
42:44
with
42:45
deployment
42:45
of
42:45
AI?
42:46
It's
42:46
really
42:46
about
42:46
how
42:47
do
42:47
you
42:47
make
42:47
your
42:48
people
42:48
do
42:49
more
42:49
work
42:49
more
42:50
efficiently
42:50
to
42:51
find
42:51
the
42:51
value
42:52
that
42:52
they're
42:52
not
42:52
creating
42:52
and
42:53
also
42:53
the
42:53
value
42:53
that
42:53
they're
42:54
leaking.
42:54
Back
42:54
to
42:54
the
42:54
point
42:54
where
42:55
said
42:55
at
42:55
the
42:55
beginning.
42:55
Yeah.
42:56
And
42:56
maybe
42:56
deploy
42:57
AI
42:57
on
42:57
processes
42:58
you've
42:59
never
42:59
run
43:00
because
43:00
you
43:00
didn't
43:00
have
43:01
enough
43:01
people.
43:01
Right.
43:03
But
43:04
I
43:05
think
43:05
retail
43:05
is
43:05
inherently
43:06
quite
43:06
efficient.
43:07
I
43:07
mean,
43:07
we're
43:08
leaking
43:08
tons
43:08
of
43:09
value.
43:09
Right.
43:10
But
43:12
everybody
43:12
has
43:13
been
43:13
through
43:13
decades
43:14
of
43:14
optimizations.
43:15
Right.
43:15
Like,
43:16
the
43:16
margin
43:17
is
43:17
so
43:17
thin
43:18
that
43:18
there
43:18
is
43:19
super
43:19
hard
43:20
to
43:20
find.
43:21
I
43:21
mean,
43:21
if
43:21
you
43:21
go
43:21
to
43:21
banking.
43:22
Yeah.
43:22
I
43:23
mean,
43:23
if
43:23
you
43:23
have.
43:23
If
43:24
you
43:24
have
43:24
3,000
43:24
people
43:25
more
43:25
than
43:25
you
43:26
should
43:26
have,
43:26
like
43:27
the
43:27
PNL
43:27
is
43:27
not
43:28
going
43:28
to
43:28
really
43:28
know
43:28
that.
43:29
Right,
43:29
Right.
43:30
But
43:30
in
43:30
retail,
43:31
that's
43:31
the
43:31
difference
43:32
between
43:32
making
43:33
a
43:33
profit
43:33
and
43:33
making
43:34
a
43:34
loss.
43:35
So
43:35
I
43:35
think
43:35
inherently
43:37
trying
43:37
to
43:37
find
43:38
ROI
43:38
in
43:39
headcount
43:39
savings
43:40
is
43:40
a
43:40
false
43:41
pretense
43:41
here.
43:41
Well,
43:41
that's
43:42
really
43:42
interesting
43:42
too.
43:42
Yeah.
43:43
And
43:43
the
43:43
other
43:43
thing
43:43
about
43:43
retail
43:43
Too
43:44
that
43:44
I
43:44
think
43:44
is
43:44
fascinating
43:44
is
43:44
just
43:45
there's
43:45
so
43:45
many
43:45
touch
43:46
points
43:46
and
43:46
so
43:46
many
43:47
different
43:47
things
43:47
that
43:47
are
43:47
happening
43:48
because
43:48
people
43:48
that
43:48
are
43:49
new
43:49
to
43:49
retail
43:49
always
43:49
say
43:49
that
43:50
to
43:50
me.
43:50
Like
43:50
I
43:50
had
43:50
no
43:51
idea
43:51
how
43:51
complicated
43:52
it
43:52
is
43:52
because
43:52
it
43:53
is,
43:53
it's
43:53
probably
43:53
one
43:53
of
43:53
the
43:53
most
43:53
complicated
43:54
industries
43:54
when
43:54
you
43:54
think
43:55
about
43:55
production,
43:56
getting
43:56
things
43:56
to
43:56
the
43:56
shelf
43:57
and
43:57
getting
43:57
things
43:57
home
43:57
to
43:58
the
43:58
customer.
43:58
All
43:58
right,
43:59
so
43:59
on
43:59
that
43:59
note,
43:59
the
43:59
last
44:00
question
44:00
I'm
44:00
going
44:00
to
44:00
ask
44:01
you
44:01
and
44:02
because
44:02
we
44:02
were
44:02
talking
44:02
beforehand
44:03
and
44:03
I
44:03
know
44:03
you
44:03
guys
44:04
are
44:04
getting
44:04
some
44:04
good
44:04
traction,
44:05
you've
44:05
got
44:05
some
44:05
big
44:05
clients
44:06
already
44:06
underway.
44:08
What
44:08
is
44:09
the
44:09
biggest
44:09
mistake
44:10
you're
44:10
seeing
44:11
clients
44:12
make
44:12
in
44:12
regards
44:13
to
44:13
trying
44:13
to
44:15
implement
44:15
the
44:15
ideas
44:15
that
44:16
you're
44:16
putting
44:16
in
44:16
front
44:16
of
44:16
them?
44:19
I
44:19
think
44:19
ultimately
44:20
when
44:21
we're
44:21
involved,
44:22
the
44:23
mistakes
44:23
don't
44:23
happen.
44:24
Okay,
44:24
Right.
44:25
But
44:26
what
44:26
I
44:26
see
44:27
retailers
44:28
getting
44:28
wrong
44:29
is
44:30
doing
44:30
these
44:32
10
44:32
year
44:32
strategies,.
44:35
Two.
44:35
Year
44:36
data
44:36
cleanups
44:39
and
44:40
the
44:40
world
44:40
is
44:40
moving
44:40
too
44:41
fast.
44:41
Yeah.
44:42
Like,
44:42
you
44:42
know,
44:42
by
44:43
the
44:43
time
44:43
you're
44:43
done
44:43
with
44:44
your
44:44
10
44:44
year
44:44
old
44:44
strategy,
44:45
you're
44:46
gonna
44:46
be
44:46
left
44:46
behind.
44:47
And
44:49
I
44:49
think
44:49
that
44:49
that's
44:50
the
44:50
biggest
44:50
mistake.
44:51
Like
44:51
there
44:51
is
44:51
a
44:51
technology
44:52
very
44:53
capable
44:53
today
44:54
of
44:54
doing
44:54
what
44:54
you
44:55
need.
44:55
Right.
44:56
Yes,
44:56
it's
44:56
important
44:56
to
44:57
have
44:57
strategy,
44:58
but
44:58
like
44:59
the
44:59
technology
44:59
will
44:59
be
45:00
different
45:00
in
45:00
two
45:00
years.
45:01
Right.
45:01
So
45:01
you're
45:01
gonna
45:01
redo
45:02
that
45:02
anyway.
45:03
So
45:03
maybe
45:03
let's
45:04
do
45:04
one
45:04
year
45:04
strategy,
45:05
two
45:05
year
45:05
strategy.
45:06
You
45:06
know,
45:07
let's
45:07
figure
45:07
out
45:07
tools
45:08
for
45:08
the
45:08
job
45:09
right
45:09
now
45:11
and
45:11
then,
45:11
and
45:12
then
45:12
take
45:12
it
45:12
from
45:12
there.
45:13
I
45:13
think
45:13
that's
45:13
the
45:13
biggest
45:14
mistake
45:14
also
45:15
AI
45:16
because
45:17
it
45:17
can
45:17
reason
45:19
doesn't
45:19
need
45:20
such
45:20
a
45:20
clean
45:20
data
45:21
as
45:21
maybe
45:21
the
45:22
previous
45:22
wave
45:22
of
45:22
technology.
45:23
Right.
45:24
So
45:25
I
45:25
think
45:26
it's
45:27
kind
45:27
of
45:27
like
45:27
every
45:27
retailer
45:28
is
45:28
six
45:28
months
45:29
away
45:29
from
45:29
clean
45:29
database.
45:30
Right.
45:31
Like
45:31
never
45:31
going
45:31
to
45:31
happen.
45:32
I'm
45:32
tired
45:32
of
45:32
talking
45:33
about
45:33
it,
45:33
to
45:33
be
45:33
honest.
45:34
You
45:34
don't
45:34
need
45:34
that.
45:35
You
45:35
don't
45:35
need
45:35
that
45:36
with
45:36
current
45:36
technology.
45:37
And
45:37
it's
45:38
just
45:38
holding
45:38
you
45:38
back
45:38
if
45:39
you
45:39
think
45:39
about
45:39
it.
45:40
So
45:40
I
45:40
think
45:40
that's,
45:40
that's
45:41
also
45:42
a
45:42
huge
45:42
mistake
45:43
that
45:43
I'm
45:43
seeing.
45:43
So
45:44
when
45:44
you
45:44
go
45:44
into
45:44
those
45:44
meetings
45:45
and
45:45
you're
45:45
pitching
45:45
this
45:46
to
45:46
folks,
45:46
do
45:47
you
45:47
have
45:47
to
45:47
kind
45:47
of
45:47
rewire
45:48
their
45:48
brains
45:48
around
45:48
that,
45:49
particularly
45:49
around
45:49
the
45:49
time
45:50
horizon,
45:50
or
45:51
are
45:52
people
45:52
starting
45:52
to
45:52
get
45:52
that?
45:53
I
45:54
think
45:54
so.
45:55
I
45:55
think
45:55
our
45:56
pitch
45:56
is
45:56
in
45:56
a
45:56
way
45:57
fairly
45:57
unique
45:58
because
45:58
what
45:58
we
45:58
say
45:58
is
45:59
what's
46:00
your
46:01
messiest
46:02
process
46:02
you
46:02
really
46:03
hate?
46:05
And
46:05
then
46:06
we
46:06
show
46:06
them
46:06
how
46:07
to,
46:07
how
46:07
we
46:07
automate
46:08
it
46:08
in
46:08
like
46:09
three,
46:09
four
46:09
weeks.
46:10
Okay.
46:10
And
46:11
that
46:11
rewires
46:11
the
46:11
brain.
46:12
Like
46:12
talking
46:13
about
46:13
things.
46:13
Don't
46:14
rewire
46:14
the
46:14
brain.
46:14
Like
46:15
seeing
46:15
is
46:15
believing.
46:16
And
46:16
that's
46:17
what
46:17
we
46:17
are
46:17
trying
46:17
to
46:18
show
46:18
people
46:19
in
46:19
retail
46:20
management.
46:20
Okay,
46:21
awesome.
46:22
Well,
46:22
Tom,
46:22
thank
46:22
you.
46:23
Thanks
46:24
for
46:24
making
46:24
time
46:24
out
46:24
of
46:25
your
46:25
busy
46:25
schedule
46:26
at
46:26
Shop
46:26
Talk
46:26
Europe.
46:27
You're
46:27
gonna
46:27
be
46:27
on
46:27
stage
46:27
later,
46:28
right?
46:28
Yeah,
46:28
yeah,
46:29
yeah.
46:30
Excited
46:31
and
46:31
looking
46:31
forward.
46:32
Yeah,
46:32
I
46:32
can't
46:32
wait
46:32
to
46:32
see
46:32
what
46:33
they're
46:33
saying.
46:33
Thanks
46:33
for
46:33
having
46:33
me.
46:33
Yeah,
46:34
great.
46:34
Anytime.
46:35
Welcome
46:35
back.
46:35
Anytime,
46:36
man.
46:36
Best
46:36
of
46:36
luck.
46:37
Thank
46:37
you.
46:37
Thank
46:37
you.
46:41
All
46:41
right,
46:42
Chris.
46:42
Headline
46:43
number
46:43
four
46:43
is
46:43
brought
46:44
to
46:44
you
46:44
by
46:44
Ocampo
46:45
Capital.
46:46
Ocampo
46:47
Capital
46:47
is
46:47
a
46:48
venture
46:48
capital
46:48
firm
46:49
founded
46:49
by
46:49
retail
46:50
executives
46:50
with
46:51
the
46:51
aim
46:51
of
46:51
helping
46:52
early
46:52
stage
46:52
consumer
46:53
businesses
46:53
succeed
46:54
through
46:54
investment
46:55
and
46:55
operational
46:56
support.
46:57
Learn
46:57
more@ocampocapital.com
47:01
all
47:01
right,
47:01
our
47:02
fourth
47:02
headline
47:03
today
47:03
is
47:04
Amazon
47:05
has
47:05
expanded
47:05
its
47:06
AI
47:06
driven
47:07
visual
47:07
search
47:07
capabilities
47:08
in
47:08
its
47:09
shopping
47:09
app,
47:09
rolling
47:10
out
47:10
three
47:10
new
47:11
features
47:11
that
47:11
make
47:11
product
47:12
discovery
47:12
a
47:13
more
47:13
intuitive
47:14
image.
47:14
First
47:15
experience
47:16
According
47:17
to
47:17
Chainstore
47:17
age,
47:18
Amazon
47:19
offers
47:19
three
47:20
new
47:20
visual
47:20
search
47:21
functions
47:21
inside
47:22
its
47:22
app.
47:23
First,
47:23
real
47:24
time
47:24
AI
47:25
generated
47:25
images.
47:26
As
47:26
customers
47:27
search
47:27
using
47:27
descriptive
47:28
language,
47:30
AI
47:30
generated
47:30
images
47:31
take
47:31
shape
47:31
in
47:32
real
47:32
time
47:32
in
47:33
the
47:33
suggestions
47:33
below
47:34
the
47:34
search
47:34
bar.
47:35
Shifting
47:36
and
47:36
refining
47:36
with
47:37
each
47:37
word
47:37
typed.
47:38
Consumers
47:38
can
47:39
then
47:39
tap
47:39
a
47:39
generated
47:40
image
47:40
and
47:40
shop
47:41
for
47:41
visually
47:41
similar
47:42
products.
47:43
Second,
47:44
shop
47:44
by
47:44
style
47:45
curated
47:45
outfit
47:46
search.
47:46
Consumers
47:47
searching
47:47
for
47:47
apparel
47:48
and
47:48
accessories
47:48
will
47:49
now
47:49
see
47:50
AI
47:50
generated
47:51
shoppable
47:51
collages
47:52
in
47:52
search
47:52
results
47:53
labeled
47:53
shop
47:54
by
47:54
style,
47:55
organized
47:55
by
47:56
themes
47:56
like
47:56
urban
47:56
luxe,
47:57
soft
47:57
elegance.
47:58
Tapping
47:59
a
47:59
collage
47:59
leads
48:00
to
48:00
a
48:00
curated
48:01
page
48:01
where
48:01
consumers
48:01
could
48:02
shop
48:02
items,
48:02
explore
48:03
similar
48:03
products,
48:03
or
48:04
swipe
48:04
between
48:05
styles.
48:06
Third,
48:07
visual
48:07
selections
48:08
suggestions.
48:10
When
48:10
customers
48:11
type
48:11
a
48:11
broad
48:12
visual
48:12
description
48:13
like
48:13
flannel
48:14
shirt,
48:14
Amazon
48:15
surfaces
48:15
descriptive
48:16
visual
48:16
filters
48:17
while
48:17
they're
48:17
typing,
48:17
allowing
48:18
customers
48:18
to
48:19
select
48:19
an
48:19
image
48:19
that
48:19
reflects,
48:20
finds
48:20
their
48:20
search
48:21
and
48:21
browse
48:21
a
48:22
narrated
48:22
selection.
48:24
Chris,
48:24
it
48:25
has
48:25
been
48:25
a
48:25
while
48:26
since
48:26
I
48:26
remember
48:27
you
48:27
talking
48:28
about
48:28
visual
48:28
search
48:29
on
48:29
this
48:29
podcast.
48:31
So
48:31
are
48:31
you
48:32
buying
48:32
or
48:33
selling
48:33
Amazon's
48:34
new
48:34
visual
48:35
search
48:35
capabilities?
48:37
Wow.
48:38
Oh,
48:38
man.
48:39
Yeah,
48:39
this
48:39
is.
48:39
This
48:40
is
48:40
the
48:40
problem
48:40
I
48:40
have
48:40
when
48:40
I
48:41
bring
48:41
a
48:41
frequent
48:41
listener
48:42
on
48:42
as
48:42
guest
48:42
host.
48:42
He
48:43
knows
48:43
the
48:43
content.
48:44
The
48:44
content
48:44
and
48:44
what.
48:44
How
48:45
we've
48:45
been
48:45
talking
48:45
about
48:45
it
48:45
in
48:45
the
48:46
past.
48:47
You
48:47
know,
48:47
Chad,
48:47
I'm
48:47
still
48:47
buying
48:48
this.
48:48
I'm
48:48
still
48:48
buying
48:49
this.
48:50
And
48:50
I
48:50
say
48:51
that,
48:51
you
48:51
know,
48:52
bringing
48:52
people
48:52
way
48:53
back
48:53
in
48:53
history.
48:53
The
48:54
very
48:54
first
48:54
video
48:55
that
48:55
I
48:55
ever
48:56
shot
48:56
for
48:57
Omnitalk
48:57
was
48:57
of
48:58
Amazon's
48:58
visual
48:59
search
48:59
capabilities.
48:59
I
49:00
walked
49:00
into
49:00
a
49:00
Starbucks
49:01
store,
49:01
took
49:02
a
49:02
photo
49:02
of
49:02
the
49:02
Starbucks
49:03
coffee
49:03
bag,
49:03
and
49:04
showed
49:04
how
49:05
Amazon
49:05
could
49:06
deliver
49:06
the
49:06
product
49:07
for
49:07
less
49:07
than
49:07
it
49:08
would
49:08
cost
49:08
me
49:08
to
49:08
buy
49:08
it
49:08
directly
49:09
from
49:09
the
49:09
Starbucks
49:10
in
49:10
which
49:10
I
49:10
was
49:10
having
49:10
my
49:11
coffee.
49:12
Ouch.
49:13
Yeah,
49:13
right.
49:13
And
49:14
I
49:14
did
49:14
that
49:14
at
49:14
Macy's
49:15
too.
49:15
Macy's
49:15
hated
49:15
me
49:16
for
49:16
that.
49:16
So
49:16
now
49:17
when
49:17
I
49:17
think
49:17
about
49:17
the
49:18
more
49:18
robust
49:18
capabilities
49:19
that
49:19
are
49:19
at
49:19
play
49:20
here,
49:20
you
49:20
know,
49:20
in
49:21
the
49:21
long
49:21
run,
49:21
particularly
49:22
with
49:22
AI
49:22
Going
49:23
back
49:23
to
49:23
the
49:23
Universal
49:23
card
49:24
thing
49:24
we
49:24
talked
49:24
at
49:24
the
49:24
beginning,
49:25
and
49:25
particularly
49:26
with
49:26
Buy
49:26
for
49:27
me
49:27
from
49:28
Amazon
49:28
coming
49:29
into
49:29
play,
49:31
I
49:31
think
49:31
you
49:32
have
49:32
to
49:32
take
49:32
this
49:32
really
49:33
seriously,
49:33
especially
49:34
with
49:34
Amazon,
49:34
you
49:34
know,
49:35
essentially
49:35
ostensibly
49:36
making
49:36
its
49:37
own
49:37
play
49:37
to
49:37
be
49:38
kind
49:38
of
49:38
the
49:38
first
49:39
product
49:39
search
49:40
super
49:40
app,
49:40
you
49:41
know,
49:42
because,
49:42
you
49:42
know,
49:43
you're
49:43
buying
49:43
it
49:43
from
49:43
Amazon.
49:44
Back
49:44
to
49:44
what
49:44
you
49:44
said
49:44
at
49:45
the
49:45
outset,
49:45
Chap,
49:45
I
49:45
think
49:46
that
49:46
gives
49:46
Amazon
49:47
a
49:47
very,
49:47
you
49:48
know,
49:48
strong
49:48
position
49:48
here.
49:49
And
49:49
the
49:49
other
49:49
thing
49:49
I'll
49:49
say,
49:50
too,
49:50
producer
49:51
Ellen
49:51
and
49:51
I
49:51
were
49:51
talking
49:52
before
49:52
the
49:52
show,
49:53
and
49:54
so
49:54
I'm
49:54
curious
49:54
to
49:54
get
49:54
her
49:54
take
49:55
at
49:55
the
49:55
end
49:55
of
49:55
the
49:55
show,
49:55
too.
49:56
We
49:57
were
49:57
talking
49:57
at
49:57
the
49:57
end,
49:57
before
49:58
the
49:58
show
49:58
about,
49:58
like,
49:58
what
49:59
I
49:59
heard
49:59
at
49:59
Shop
49:59
Talk
49:59
Europe,
50:00
and
50:01
I
50:02
heard
50:02
Nadine
50:02
Graff
50:03
from
50:03
Estee
50:03
Lauder,
50:04
their
50:04
head
50:04
of
50:04
Europe
50:05
for
50:05
Estee
50:05
Lauder,
50:06
and
50:06
she's
50:06
like,
50:07
talking
50:07
about
50:07
how,
50:07
you
50:08
know,
50:08
it's
50:08
kind
50:09
of
50:09
the.
50:09
Kind
50:09
of
50:09
the
50:10
theme
50:10
or
50:10
the
50:10
takeaway
50:11
I
50:11
got
50:11
from
50:11
her
50:11
was
50:11
kind
50:11
of
50:12
like,
50:12
you
50:12
know,
50:13
we're
50:13
probably
50:13
gonna
50:13
have
50:13
a
50:14
day
50:14
where
50:14
every
50:14
retailer's
50:15
on
50:15
Amazon.
50:16
That's
50:16
where
50:16
this
50:17
is
50:17
headed,
50:17
because
50:18
there's
50:18
kind
50:18
of
50:18
no
50:18
reason
50:19
not
50:19
to
50:19
be
50:20
anymore
50:21
if
50:21
you
50:21
think.
50:22
If
50:22
you
50:22
start
50:22
really
50:23
going
50:23
down
50:23
that
50:24
rabbit
50:24
hole.
50:24
And
50:25
so
50:25
for
50:25
that
50:25
reason,
50:26
I
50:26
like
50:26
this
50:26
move
50:27
because,
50:27
you
50:27
know,
50:27
if
50:27
I
50:28
can
50:28
just
50:28
get.
50:29
If
50:29
I
50:29
can
50:29
just
50:29
know
50:30
that
50:30
Amazon
50:30
is
50:30
going
50:31
to
50:31
have
50:31
the
50:31
products
50:31
I
50:32
want,
50:32
they're
50:32
going
50:32
to
50:32
fulfill
50:32
it
50:33
the
50:33
way
50:33
I
50:33
want,
50:34
then
50:34
whether
50:35
it's
50:35
apparel,
50:36
beauty,
50:37
this
50:37
type
50:37
of
50:38
capability
50:39
will
50:39
help,
50:39
provided
50:40
that
50:40
they
50:40
can
50:41
do
50:41
it
50:41
in
50:41
a
50:41
way
50:42
that
50:42
people
50:42
want
50:42
to
50:43
interact
50:43
with
50:43
it.
50:43
That's
50:43
my
50:44
take.
50:44
But
50:44
what
50:44
do
50:44
you
50:44
think?
50:46
Yeah,
50:46
so
50:46
the
50:47
last
50:47
thing
50:47
you
50:47
said
50:47
there
50:48
was
50:48
probably
50:49
the
50:49
kicker
50:49
that
50:50
I'll
50:50
get
50:50
to
50:50
in
50:50
a
50:51
minute.
50:52
And
50:52
so
50:53
your
50:53
Starbucks
50:53
example,
50:55
you
50:55
love.
50:55
What
50:56
I
50:56
love
50:56
about
50:56
Amazon's
50:57
image
50:57
recognition.
50:58
You
50:58
take
50:58
a
50:58
picture
50:58
of
50:59
something,
50:59
you
50:59
find
50:59
it
51:00
on
51:00
Amazon.
51:01
Wicked
51:01
cool.
51:02
Yeah.
51:02
This
51:02
announcement
51:03
is
51:03
not
51:04
that.
51:04
Right.
51:04
This
51:05
is
51:05
AI
51:05
image
51:06
generation
51:07
that
51:07
helps
51:07
you
51:08
shop
51:08
visually
51:09
on
51:09
the
51:09
discovery
51:10
side.
51:10
So
51:11
you
51:11
have
51:11
a
51:12
picture
51:12
in
51:12
your
51:12
head,
51:13
but
51:13
you
51:13
don't
51:14
know
51:14
the
51:14
right
51:15
words
51:15
to
51:15
describe
51:16
it.
51:17
And
51:17
then.
51:17
So
51:17
this
51:18
helps
51:18
you
51:18
get
51:18
there.
51:19
It's
51:19
another
51:20
shopping
51:20
tool.
51:21
We
51:21
expect
51:21
this
51:22
from
51:22
Amazon.
51:23
It's
51:23
smart.
51:24
I
51:24
agree.
51:25
I'm
51:25
not
51:25
sure
51:26
it's
51:26
a
51:27
game
51:27
changer
51:27
that
51:27
drives
51:28
improved
51:28
conversion
51:29
and
51:29
retention,
51:30
but
51:31
it
51:31
is
51:31
another
51:32
reason
51:32
to
51:32
stay
51:33
in
51:33
Amazon's
51:34
ecosystem
51:34
because
51:35
they
51:35
make
51:36
discovery
51:36
and
51:36
shopping
51:37
easy.
51:37
Like,
51:37
it's
51:37
another
51:38
tool.
51:38
So
51:39
kudos
51:39
to
51:39
them
51:40
on
51:40
that
51:40
front.
51:40
Yeah,
51:41
I
51:41
think
51:41
one
51:41
interesting
51:42
angle
51:42
here
51:42
to
51:43
be
51:43
careful
51:43
about
51:43
if
51:44
I'm
51:44
Amazon.
51:45
So
51:46
at
51:46
Gartner,
51:46
I
51:47
cover
51:47
retail
51:47
returns
51:48
trends.
51:49
And
51:49
as
51:50
you
51:50
know,
51:50
Chris,
51:51
returns
51:51
are
51:52
not
51:52
retailers,
51:53
friends.
51:53
These
51:53
days
51:53
return
51:54
rates
51:54
are
51:55
near
51:55
20%
51:56
of
51:56
overall
51:57
E
51:57
commerce
51:57
returns.
51:58
Like,
51:58
it's
51:59
a
51:59
huge
51:59
number.
52:01
And
52:03
the
52:03
research
52:04
says
52:04
that
52:05
retailers
52:06
are
52:06
still
52:06
in
52:06
control
52:07
of
52:07
many
52:07
of
52:08
the
52:08
reasons
52:08
that
52:09
things
52:09
get
52:09
returned.
52:10
Okay,
52:11
and
52:11
here
52:12
we
52:12
are
52:13
adding
52:13
another
52:13
potential
52:14
reason
52:14
why
52:15
someone's
52:15
going
52:15
to
52:16
return
52:16
something.
52:16
Because
52:17
if
52:18
the
52:18
matching
52:18
product
52:19
inferred
52:19
from
52:20
the
52:20
generated
52:20
AI
52:21
image
52:21
isn't
52:22
exactly
52:22
right,
52:23
I
52:24
just
52:24
created
52:24
a
52:25
return.
52:25
Right.
52:26
So,
52:28
you
52:28
know,
52:28
Lord
52:28
knows
52:29
my
52:29
wife
52:29
would
52:29
never
52:30
let
52:30
me
52:30
use
52:30
a
52:30
feature
52:31
like
52:31
this
52:31
to
52:31
shop.
52:32
I
52:33
just
52:33
know
52:33
that.
52:34
Right.
52:34
So
52:35
it
52:36
cool
52:36
factor
52:37
100%.
52:38
But
52:38
let's
52:39
watch
52:39
the
52:39
execution
52:40
because
52:41
we
52:41
are
52:41
adding
52:42
an
52:42
AI
52:43
generated
52:43
image
52:44
that
52:45
is
52:45
based
52:45
off
52:45
of
52:46
some
52:46
consumer's
52:47
natural
52:48
language
52:48
to
52:48
like,
52:49
then
52:49
get
52:50
me
52:50
to
52:50
the
52:50
product.
52:51
Like
52:51
they're.
52:52
We're
52:53
adding
52:53
a
52:53
piece
52:54
of
52:54
technology
52:55
in
52:55
the
52:55
middle
52:55
on
52:56
that
52:56
discovery
52:56
process.
52:57
That
52:58
is
52:58
brand
52:58
new.
52:59
Right.
53:00
And
53:01
so
53:01
how
53:01
does
53:02
that,
53:02
how
53:02
does
53:03
that,
53:04
does
53:04
that
53:04
create
53:04
another
53:05
point
53:05
of
53:06
friction
53:06
or
53:07
a
53:07
possible
53:07
return?
53:08
That's
53:09
something
53:09
I'm
53:09
interested
53:09
in
53:10
seeing.
53:10
Yeah,
53:11
you're
53:11
adding
53:11
an
53:11
uncontrollable.
53:12
You
53:12
know,
53:14
that
53:14
is
53:14
something
53:15
that's
53:15
at
53:15
play.
53:16
All
53:16
right,
53:16
you
53:16
said
53:16
you're
53:17
going
53:17
to
53:17
come
53:17
back
53:17
to
53:17
it
53:17
at
53:17
the
53:18
end.
53:19
The
53:19
kicker,
53:20
what
53:20
was
53:20
the
53:20
kick,
53:20
what
53:21
was
53:21
your
53:21
take
53:21
on
53:21
the
53:21
kicker
53:22
there
53:22
chapter?
53:23
Well,
53:23
the
53:24
kicker
53:24
being,
53:25
yes,
53:25
we're
53:25
making
53:26
discovery
53:26
and
53:26
shopping
53:27
easy
53:27
to
53:27
stay
53:28
in
53:28
the
53:28
ecosystem.
53:29
But
53:29
if
53:30
we're
53:30
adding
53:32
confusion
53:32
to
53:33
that
53:33
process,
53:35
then
53:35
I
53:35
get
53:35
concerned.
53:36
And
53:36
we
53:36
already
53:37
know
53:37
there
53:37
is
53:38
friction.
53:38
Like
53:39
today
53:39
we
53:39
get
53:39
on
53:39
Amazon
53:40
and
53:40
we
53:40
have
53:41
to
53:41
sort
53:41
through
53:42
sponsored
53:42
products.
53:43
Right.
53:43
I
53:43
mean,
53:44
in
53:44
order
53:45
to
53:45
get
53:45
to
53:45
the
53:45
thing
53:46
that
53:46
we
53:46
want
53:46
to
53:46
buy.
53:48
Let's
53:48
be
53:49
fair,
53:49
Amazon
53:49
has
53:50
created
53:50
some
53:50
friction
53:51
in
53:51
that
53:51
process
53:51
because
53:52
we
53:52
have
53:53
lots
53:53
of
53:53
sponsored
53:54
products
53:54
that
53:54
we
53:54
have
53:54
to
53:54
get
53:55
through.
53:55
Now.
53:55
This
53:56
AI
53:56
image
53:56
generation
53:57
feature
53:58
should
53:58
be
53:59
an
53:59
additive
53:59
benefit
54:00
to
54:00
the
54:00
consumer.
54:01
Right.
54:02
But
54:02
it's
54:03
an
54:03
unknown.
54:03
Right?
54:04
How
54:04
is
54:04
it
54:04
actually
54:04
going
54:05
to
54:05
get
54:05
executed?
54:05
So.
54:06
Yeah,
54:06
yeah,
54:07
yeah.
54:07
And
54:07
that's,
54:07
that's
54:08
why
54:08
I'm
54:08
dying
54:09
to
54:09
get
54:09
producer
54:09
Ella
54:10
in
54:10
here
54:10
at
54:10
the
54:10
end
54:10
of
54:10
the
54:10
show
54:11
because
54:11
I'm
54:11
curious,
54:11
like,
54:11
how
54:12
much
54:12
do
54:12
you
54:12
look
54:12
for
54:13
apparel
54:13
on
54:13
Amazon
54:14
now?
54:14
And
54:15
would
54:15
you
54:15
think
54:15
this
54:15
type
54:16
of
54:16
feature
54:17
is
54:17
going
54:17
to
54:17
work?
54:18
But.
54:18
All
54:18
right,
54:19
let's
54:19
keep
54:19
rolling
54:19
though.
54:19
Headline
54:20
number
54:20
five,
54:20
Headline
54:21
number
54:21
five
54:21
is
54:21
brought
54:21
to
54:22
you
54:22
by
54:22
the
54:22
lockdown.
54:23
Valack
54:23
is
54:23
a
54:23
proven
54:24
E
54:24
grocery
54:24
technology
54:25
built
54:25
for
54:25
grocers
54:26
by
54:26
grocers.
54:27
Exactly
54:27
the
54:27
type
54:28
of
54:28
technology
54:28
we
54:28
like
54:29
here
54:29
at
54:29
omnitalk.
54:30
They
54:30
unite
54:30
proprietary
54:31
software
54:32
with
54:32
right
54:32
size
54:32
automation
54:33
to
54:33
make
54:33
same
54:33
day
54:34
delivery
54:34
profitable.
54:35
To
54:35
learn
54:36
more,
54:36
visit
54:36
Voloc.com
54:37
that's
54:38
V
54:38
E
54:38
L
54:38
O
54:38
Q.com
54:40
headline
54:40
number
54:40
five
54:40
a
54:41
new
54:41
startup
54:41
called
54:41
the
54:41
Mall
54:42
has
54:42
launched
54:42
an
54:42
app
54:42
that
54:43
aims
54:43
to
54:43
be
54:43
a
54:43
universal
54:44
shopping
54:44
feed,
54:45
a
54:45
kind
54:45
of
54:46
Spotify
54:46
for
54:47
online
54:47
retail,
54:47
if
54:48
you
54:48
will,
54:48
letting
54:49
users
54:49
follow
54:49
their
54:49
favorite
54:50
brands,
54:50
track
54:50
sales
54:51
and
54:51
product
54:51
drops,
54:51
and
54:52
discover
54:52
new
54:52
products
54:52
across
54:53
more
54:53
than
54:53
10,000
54:54
retailers
54:54
all
54:55
in
54:55
one
54:55
place.
54:56
According
54:56
to
54:56
TechCrunch,
54:57
the
54:57
mall
54:57
was
54:57
founded
54:58
in
54:58
October
54:58
25
54:59
by
55:00
co
55:00
founder
55:01
and
55:01
CEO
55:01
Shreya
55:02
Halder,
55:02
a
55:02
Stanford
55:03
computer
55:03
science
55:03
grad.
55:04
So
55:04
you
55:04
know,
55:04
it
55:04
just
55:05
must
55:05
be
55:05
awesome.
55:05
Chap
55:06
and
55:06
co
55:07
founder
55:07
and
55:07
CEO
55:08
Ellie
55:08
Konskar,
55:09
who
55:09
previously
55:10
worked
55:10
at
55:10
Tom
55:10
Ford
55:10
and
55:11
Carla
55:11
Otto.
55:12
The
55:12
two
55:12
connected
55:12
through
55:13
a
55:13
female
55:13
founder
55:14
circle
55:14
in
55:14
Los
55:14
Angeles.
55:16
Rather
55:16
than
55:16
using
55:16
brand
55:17
APIs
55:17
or
55:18
affiliate
55:18
partnerships.
55:19
The
55:19
Mall
55:19
scrapes
55:20
retail
55:20
websites
55:21
frequently
55:21
enough
55:21
to
55:22
track
55:22
sales
55:23
restocks,
55:23
drops
55:24
and
55:24
promotions,
55:25
and
55:25
alerts
55:26
users
55:26
via
55:26
push
55:27
notifications
55:28
when
55:28
users
55:28
are
55:29
ready
55:29
to
55:29
buy.
55:29
A
55:29
browser
55:30
opens
55:30
inside
55:31
the
55:31
app
55:31
and
55:31
takes
55:31
them
55:32
directly
55:32
to
55:32
the
55:32
brand's
55:33
site
55:33
to
55:33
check
55:33
out.
55:34
The
55:34
app
55:34
is
55:34
currently
55:35
live
55:35
only
55:36
in
55:36
beta
55:36
and
55:37
is
55:37
invite
55:37
only,
55:38
and
55:38
it's
55:39
currently
55:39
being
55:39
tested
55:40
with
55:40
4,500
55:41
early
55:41
users,
55:41
but
55:42
it's
55:42
expected
55:42
to
55:43
be
55:43
broadly
55:43
available
55:44
by
55:44
the
55:44
end
55:44
of
55:44
the
55:45
summer.
55:46
Chap
55:46
the
55:46
Mall
55:46
is
55:47
pitching
55:47
itself
55:47
as
55:48
a
55:48
solution
55:48
to
55:48
the
55:49
fragmented,
55:49
multi
55:50
tab
55:50
online
55:51
shopping
55:51
experience.
55:52
Does
55:52
this
55:53
startup
55:53
have
55:53
legs
55:54
to
55:54
reshape
55:54
how
55:55
people
55:55
discover
55:55
and
55:55
shop
55:56
online?
55:57
Or
55:57
is
55:57
it
55:57
too
55:58
easy
55:58
for
55:58
Amazon,
55:59
Google
55:59
or
56:00
even
56:00
TikTok
56:01
to
56:01
replicate
56:02
and
56:02
ultimately
56:03
destroy
56:03
it
56:03
in
56:04
one
56:04
fell
56:04
swoop?
56:05
So
56:05
Chris,
56:06
I
56:06
had
56:07
not
56:07
heard
56:07
of
56:07
this
56:08
prior
56:08
to
56:09
this
56:10
podcast.
56:11
I
56:11
went
56:12
checked
56:12
it
56:12
out.
56:12
I
56:12
guess
56:13
I'm
56:13
on
56:13
the
56:13
wait
56:13
list
56:14
now.
56:14
I
56:14
found
56:14
it
56:14
odd
56:15
that
56:15
I
56:15
have
56:15
to,
56:16
you
56:16
know,
56:16
get
56:16
on
56:16
a
56:17
wait
56:17
list
56:17
to
56:17
experience
56:18
this
56:18
product,
56:19
but
56:19
I'm
56:19
on
56:20
the
56:20
list.
56:20
I
56:20
guess.
56:21
I
56:21
am
56:22
not
56:22
optimistic
56:23
on
56:23
this
56:23
one.
56:23
On
56:23
its
56:24
surface
56:24
now
56:25
they
56:25
call
56:25
it
56:26
the
56:26
Spotify
56:27
for
56:27
shopping.
56:28
I
56:29
don't
56:29
love
56:29
that
56:30
analogy
56:30
because
56:31
we're
56:31
talking
56:31
content
56:32
versus
56:33
products.
56:34
Very
56:34
different,
56:34
right?
56:35
And
56:35
I'm
56:36
not
56:36
going
56:36
to
56:36
go
56:36
I'm
56:37
not
56:37
creating
56:37
a
56:37
playlist
56:38
of
56:38
pants
56:40
anytime
56:40
soon.
56:42
So
56:43
it's
56:43
broader
56:44
than
56:44
the
56:44
Google
56:45
Universal
56:45
cart
56:46
thing
56:46
though,
56:46
because
56:47
it
56:47
does
56:47
allow
56:48
you
56:48
to
56:48
build
56:48
a
56:49
mall
56:49
of
56:50
brands
56:50
versus
56:51
specific
56:51
products.
56:52
So
56:52
that's
56:53
interesting.
56:54
But
56:54
then
56:54
it
56:54
also
56:54
does
56:55
some
56:55
of
56:55
the
56:55
things
56:56
same
56:56
things
56:56
that
56:56
Universal
56:57
Google's
56:58
cart
56:58
does,
56:58
which
56:59
is
56:59
like
56:59
watch
56:59
for
57:00
price,
57:00
dropped
57:01
price
57:01
drops
57:01
and
57:01
in
57:01
stock
57:02
alerts.
57:03
So
57:04
I'm
57:05
Tom,
57:05
I'll
57:05
be
57:05
honest.
57:06
I'm
57:06
probably
57:06
not
57:06
the
57:07
target
57:07
demographic
57:08
of
57:08
this
57:08
product,
57:08
so
57:09
it's
57:09
tough
57:09
for
57:09
me
57:09
to
57:10
say
57:10
it's
57:10
a
57:10
good
57:11
bet.
57:11
But
57:12
right
57:12
now,
57:12
I
57:12
fall
57:13
on
57:13
the
57:13
skeptical
57:14
side
57:14
of
57:14
this
57:15
thing.
57:15
And
57:16
the
57:17
monetization
57:17
side
57:18
of
57:18
this
57:18
looks
57:18
to
57:19
be
57:19
a
57:19
B2B
57:20
model
57:20
where
57:21
they're
57:21
selling
57:21
data
57:22
to
57:23
retailers.
57:24
So
57:25
we
57:25
need
57:25
lots
57:25
of
57:26
scale
57:26
on
57:26
the
57:26
consumer
57:27
side
57:27
for
57:27
that
57:28
monetization
57:28
to
57:29
work.
57:30
And
57:30
right
57:30
now,
57:31
I
57:31
don't
57:31
see
57:32
the
57:32
compelling
57:33
consumer
57:33
angle
57:33
that
57:34
drives
57:34
that
57:34
kind
57:34
of
57:35
scale.
57:36
Yeah,
57:36
right.
57:36
Yeah,
57:37
I
57:37
think
57:37
I
57:37
agree.
57:37
I
57:38
think
57:38
I
57:38
agree
57:38
with
57:38
you.
57:38
The
57:38
one
57:38
point
57:38
I
57:39
don't
57:39
agree
57:39
with
57:39
you
57:39
necessarily
57:40
is
57:40
what
57:40
you
57:40
said
57:40
at
57:40
the
57:40
beginning.
57:41
Like,
57:41
I
57:41
think,
57:41
I
57:42
think
57:42
there
57:42
is
57:42
some
57:42
aspect
57:43
of
57:43
the
57:44
future
57:44
of
57:44
retail
57:45
online
57:45
merchandising
57:46
being
57:46
driven
57:47
by
57:47
content.
57:47
So
57:48
I
57:48
think
57:48
there
57:48
is
57:48
some
57:49
analogies
57:49
with
57:50
the
57:50
Spotify.
57:51
But
57:51
that's
57:51
also
57:51
why
57:51
I
57:51
want
57:52
to
57:52
hold
57:52
Ella's
57:53
commentary
57:53
on
57:53
the
57:53
last
57:54
headline
57:54
until
57:54
we
57:55
get
57:55
to
57:55
this
57:55
headline,
57:55
because
57:56
I
57:56
think
57:56
there's,
57:56
there's
57:57
some
57:57
overlaps
57:57
here
57:58
in
57:58
terms
57:58
of
57:58
how
57:59
this
57:59
all
57:59
could
57:59
play
58:00
out.
58:00
But
58:00
I,
58:00
I
58:01
think
58:01
fundamentally,
58:01
I
58:01
do
58:02
agree
58:02
with
58:02
you.
58:02
If
58:02
I
58:02
had
58:02
a
58:02
nickel
58:03
for
58:03
every
58:03
time
58:04
I
58:04
covered
58:04
a
58:04
story
58:04
like
58:05
this
58:05
on
58:05
the
58:05
podcast
58:06
in
58:06
the
58:06
Omnitalk
58:06
Fast
58:07
Five
58:07
history,
58:07
I'd
58:08
probably
58:08
have,
58:08
you
58:08
know,
58:09
truthfully,
58:09
20
58:09
cents
58:10
because
58:10
I
58:10
feel
58:10
like
58:11
it
58:11
happens
58:11
every
58:11
year
58:12
to
58:12
two
58:12
years,
58:13
but
58:13
it's
58:13
still
58:14
pretty
58:14
regularly,
58:15
when
58:15
you
58:15
think
58:15
about
58:15
it,
58:15
when
58:15
this
58:16
type
58:16
of
58:16
thing
58:16
is
58:16
breaking
58:17
and
58:17
getting
58:17
the
58:17
coverage
58:18
it
58:18
is.
58:18
The
58:18
last
58:19
one
58:19
I
58:19
remember
58:19
of
58:20
any
58:20
substance
58:21
was
58:21
Verishop,
58:22
but
58:22
I
58:22
haven't
58:23
heard
58:23
word
58:24
one
58:24
about
58:24
Verishop,
58:25
you
58:25
know,
58:25
since
58:26
it
58:26
came
58:26
on
58:26
the
58:26
scene
58:26
with
58:27
similar
58:27
fanfare.
58:28
So,
58:28
so
58:29
I'm,
58:29
I'm,
58:29
I'm
58:30
actually
58:30
going
58:30
to
58:30
be
58:30
pretty,
58:30
you
58:31
know,
58:31
pretty
58:31
blunt.
58:32
I
58:32
give
58:32
this
58:32
zero
58:33
chance.
58:34
But,
58:34
but
58:35
I
58:35
do
58:35
think
58:35
it's.
58:35
The
58:36
reason
58:36
that
58:36
I
58:36
want
58:36
to
58:36
include
58:37
it
58:37
this
58:37
week
58:39
is
58:39
even
58:39
though
58:39
I'm
58:39
not
58:40
long
58:40
on
58:40
it,
58:40
I
58:41
think
58:41
it
58:42
is
58:42
important
58:42
to
58:42
highlight
58:43
because
58:43
it,
58:43
the
58:44
story
58:44
itself
58:45
highlights
58:45
where
58:45
things
58:46
are
58:46
going
58:46
and
58:47
the
58:47
problems
58:48
that
58:48
the
58:48
bigger
58:49
platforms
58:49
are
58:49
going
58:50
to
58:50
set
58:50
out
58:50
to
58:51
try
58:51
and
58:51
solve,
58:51
which,
58:52
you
58:52
know,
58:52
it's
58:53
like
58:53
Amazon,
58:54
if
58:54
you
58:54
go
58:54
back
58:54
to
58:54
the
58:54
last
58:55
headline,
58:55
is
58:56
kind
58:56
of
58:56
trying
58:56
to
58:56
solve
58:56
this
58:57
in
58:57
some
58:57
ways,
58:57
or
58:58
you
58:58
could
58:58
see
58:58
how
58:58
they
58:58
will
58:59
pivot
58:59
in
58:59
a
59:00
similar
59:00
direction
59:00
over
59:01
time.
59:01
And,
59:02
and
59:02
so,
59:02
and
59:02
the
59:03
other
59:03
thing
59:03
too
59:03
is,
59:03
I
59:04
feel
59:04
like,
59:05
I
59:05
hate
59:05
to
59:05
say
59:05
it,
59:05
but
59:05
I
59:06
feel
59:06
like
59:06
a
59:07
smart
59:07
person,
59:07
probably
59:08
smarter
59:08
than
59:08
me,
59:08
could
59:08
probably
59:09
program
59:09
Claude
59:10
to
59:10
do
59:10
this
59:10
for
59:10
themselves,
59:11
just
59:11
as
59:12
a
59:12
personal
59:12
feature
59:13
for
59:13
them,
59:13
you
59:14
know,
59:14
you
59:14
know,
59:15
day
59:15
in
59:15
and
59:15
day
59:15
out
59:15
if
59:16
they're
59:16
really
59:16
inclined
59:17
to
59:17
want
59:17
to
59:17
shop
59:18
this
59:18
way.
59:19
Right.
59:20
But.
59:21
But
59:21
I
59:21
don't
59:21
know.
59:21
Producer
59:22
Ella,
59:22
what
59:22
do
59:22
you.
59:22
What
59:22
do
59:23
you
59:23
think
59:23
here?
59:23
You
59:23
know,
59:23
like,
59:24
what's
59:24
your.
59:24
What's
59:25
your
59:25
take
59:25
on.
59:26
On
59:26
shopping
59:27
apparel
59:27
on
59:27
Amazon
59:28
and
59:28
also
59:28
on
59:28
the
59:29
mall?
59:30
Yeah,
59:30
the
59:31
mall
59:31
headline
59:31
is
59:32
really
59:32
interesting
59:32
to
59:32
me.
59:33
I
59:33
think
59:34
it
59:34
might
59:34
honestly
59:35
come
59:35
down
59:35
to
59:35
the
59:35
marketing
59:36
of
59:36
this,
59:36
because
59:37
if.
59:37
If
59:37
they
59:37
can
59:37
get
59:38
me
59:38
to
59:38
the
59:38
platform,
59:40
maybe
59:40
it's
59:40
a
59:40
go.
59:41
However,
59:42
I.
59:42
I'm
59:43
interested
59:43
to
59:43
know
59:44
what.
59:44
What
59:44
the
59:44
difference
59:45
is.
59:45
Chap.
59:46
Kind
59:46
of
59:46
to
59:46
your
59:46
point.
59:47
It
59:47
just
59:47
feels
59:47
kind
59:47
of
59:48
like
59:48
I'm
59:48
already
59:48
doing
59:49
this,
59:50
you
59:50
know,
59:50
on
59:50
Google
59:50
or
59:51
Amazon
59:52
itself.
59:52
Yeah,
59:53
there's
59:54
some
59:54
pretty
59:54
big
59:54
discovery
59:55
ecosystems
59:56
that
59:56
we're
59:56
all
59:56
familiar
59:57
with
59:57
right
59:57
now.
59:58
So
59:58
how
59:58
is
59:58
this
59:59
one
59:59
going
59:59
to
59:59
create
1:00:00
really
1:00:00
differentiated
1:00:01
consumer
1:00:01
value?
1:00:02
That's.
1:00:03
That's
1:00:03
what's
1:00:03
not
1:00:04
clear
1:00:04
yet.
1:00:04
But,
1:00:05
you
1:00:05
know,
1:00:06
I'm
1:00:06
on
1:00:06
the
1:00:06
wait
1:00:07
list,
1:00:07
so
1:00:07
it's
1:00:08
hard
1:00:08
for
1:00:08
me
1:00:08
to
1:00:08
know
1:00:08
what's
1:00:09
out
1:00:09
there.
1:00:09
But
1:00:10
once
1:00:10
I
1:00:10
click
1:00:11
into
1:00:11
it,
1:00:11
I'd
1:00:11
love
1:00:11
to
1:00:11
see
1:00:11
what
1:00:12
they're
1:00:12
up
1:00:12
to.
1:00:13
Yeah,
1:00:13
good
1:00:14
to.
1:00:14
Good.
1:00:14
Good
1:00:14
to
1:00:15
Whoever's
1:00:15
running
1:00:15
their
1:00:15
PR
1:00:16
for
1:00:16
getting
1:00:16
them
1:00:16
in
1:00:16
TechCrunch,
1:00:17
too.
1:00:17
You
1:00:17
got
1:00:17
to
1:00:17
wonder
1:00:18
if
1:00:18
there's
1:00:18
some
1:00:18
connections
1:00:19
there
1:00:19
at
1:00:19
the
1:00:19
TechCrunch
1:00:20
media
1:00:20
staff.
1:00:21
But,
1:00:22
Ella,
1:00:22
I'm
1:00:22
curious,
1:00:22
too.
1:00:23
What
1:00:23
about
1:00:23
Amazon?
1:00:24
Like,
1:00:25
do
1:00:25
you.
1:00:26
Do
1:00:26
you
1:00:26
shop
1:00:26
for
1:00:26
apparel
1:00:27
now
1:00:27
on
1:00:27
Amazon?
1:00:29
I
1:00:29
do.
1:00:29
I
1:00:30
actually
1:00:30
have
1:00:30
this
1:00:30
as.
1:00:31
As
1:00:31
my.
1:00:31
My
1:00:31
favorite
1:00:32
headline
1:00:32
of
1:00:32
the
1:00:32
week,
1:00:32
so
1:00:32
I
1:00:33
might
1:00:33
as
1:00:33
well
1:00:33
just
1:00:33
jump
1:00:33
in.
1:00:33
Let's
1:00:34
just
1:00:34
do
1:00:34
it.
1:00:34
Let's
1:00:34
just
1:00:34
do
1:00:34
it
1:00:35
now.
1:00:35
Yeah,
1:00:35
let's
1:00:35
do
1:00:35
it
1:00:35
now.
1:00:36
I
1:00:37
feel
1:00:37
like
1:00:37
kind
1:00:37
of
1:00:37
a
1:00:37
broken
1:00:37
record
1:00:38
because
1:00:38
every
1:00:38
time
1:00:38
it's
1:00:39
the
1:00:39
favorite
1:00:39
headline,
1:00:40
it's
1:00:40
talking
1:00:40
about
1:00:40
this
1:00:41
type
1:00:41
of
1:00:41
search.
1:00:41
Right.
1:00:42
Like,
1:00:42
I'm
1:00:42
searching
1:00:43
for
1:00:43
a
1:00:44
coastal
1:00:44
grandma
1:00:45
rain
1:00:45
jacket,
1:00:45
but
1:00:45
cooler,
1:00:46
like,
1:00:46
the
1:00:46
most
1:00:46
specific
1:00:47
thing
1:00:47
ever.
1:00:48
Are
1:00:48
you
1:00:48
really.
1:00:48
Are
1:00:49
you
1:00:49
really
1:00:49
doing
1:00:49
that?
1:00:50
100%.
1:00:52
A
1:00:52
coastal
1:00:53
grandma
1:00:53
rain
1:00:54
jacket.
1:00:54
Coastal.
1:00:55
I
1:00:55
wanted
1:00:55
to
1:00:55
make
1:00:55
sure
1:00:55
I
1:00:56
heard
1:00:56
that
1:00:56
right.
1:00:56
Yeah,
1:00:56
I
1:00:57
was
1:00:57
glad
1:00:57
you
1:00:57
repeated
1:00:57
it
1:00:57
because
1:00:58
I
1:00:58
didn't
1:00:58
want
1:00:58
to
1:00:58
accidentally
1:00:59
say
1:00:59
it
1:00:59
wrong.
1:01:00
Right.
1:01:00
But
1:01:00
it's
1:01:00
using
1:01:01
that
1:01:01
third
1:01:01
party
1:01:02
of
1:01:02
getting
1:01:02
to
1:01:02
someone's
1:01:03
Amazon
1:01:03
storefront
1:01:04
who
1:01:04
maybe
1:01:04
already
1:01:04
shopped,
1:01:05
sort
1:01:05
of
1:01:05
like
1:01:05
a
1:01:06
merchant
1:01:06
that
1:01:06
I
1:01:07
can,
1:01:07
you
1:01:07
know,
1:01:07
shop
1:01:08
what
1:01:08
that
1:01:08
person
1:01:09
likes
1:01:09
that's
1:01:09
maybe
1:01:10
labeled
1:01:10
as
1:01:10
cool
1:01:11
girl
1:01:11
grandma
1:01:11
jackets.
1:01:12
Right,
1:01:12
Right.
1:01:13
So
1:01:14
I
1:01:14
think
1:01:14
if
1:01:14
they
1:01:14
can
1:01:15
figure
1:01:15
out
1:01:15
this
1:01:15
search
1:01:16
function,
1:01:16
it
1:01:16
might.
1:01:17
It
1:01:17
might
1:01:18
become
1:01:18
the
1:01:18
creator
1:01:19
I'm
1:01:19
looking
1:01:19
for
1:01:19
just
1:01:20
within
1:01:20
their
1:01:20
own
1:01:20
sight.
1:01:21
So
1:01:21
I
1:01:21
think
1:01:22
they're
1:01:22
onto
1:01:22
something.
1:01:23
And
1:01:23
this
1:01:23
honestly
1:01:23
reminds
1:01:24
me,
1:01:24
too.
1:01:25
I'm
1:01:25
gonna
1:01:25
plug
1:01:25
one
1:01:26
of
1:01:26
My
1:01:26
good
1:01:26
friends
1:01:26
here,
1:01:26
but
1:01:27
Mary
1:01:27
Corland
1:01:28
Downs,
1:01:28
she
1:01:29
just
1:01:29
hosted
1:01:29
the
1:01:29
Fashionology
1:01:30
Summit
1:01:30
in
1:01:31
New
1:01:31
York.
1:01:31
And
1:01:32
this
1:01:32
was.
1:01:32
The
1:01:32
whole
1:01:33
entire
1:01:33
thing
1:01:33
was
1:01:33
about
1:01:34
modern
1:01:34
commerce
1:01:34
and
1:01:35
agentic
1:01:35
search
1:01:36
and
1:01:36
how
1:01:37
search
1:01:37
is
1:01:37
just
1:01:37
changing
1:01:38
and
1:01:38
the
1:01:38
consumer
1:01:39
intent
1:01:39
is
1:01:39
changing.
1:01:40
So
1:01:41
she
1:01:41
got
1:01:41
a
1:01:41
ton
1:01:41
of
1:01:41
people
1:01:42
together
1:01:42
to
1:01:42
talk
1:01:42
about
1:01:42
this.
1:01:43
But.
1:01:43
But
1:01:43
definitely
1:01:44
look
1:01:44
into
1:01:44
all
1:01:45
things
1:01:45
fashion
1:01:45
tech,
1:01:46
if
1:01:46
you're
1:01:46
interested.
1:01:47
Okay.
1:01:48
Wow.
1:01:48
All
1:01:48
right.
1:01:48
Yeah,
1:01:49
well,
1:01:49
that's.
1:01:49
I
1:01:50
did
1:01:50
not
1:01:50
expect
1:01:50
you
1:01:50
to
1:01:50
tell
1:01:50
me
1:01:51
that.
1:01:51
I
1:01:51
really
1:01:51
didn't.
1:01:51
I
1:01:51
didn't
1:01:52
expect.
1:01:52
But
1:01:52
so
1:01:53
that's.
1:01:53
That's
1:01:54
really
1:01:54
good
1:01:54
news
1:01:54
for
1:01:55
Amazon.
1:01:55
You
1:01:55
know,
1:01:55
if.
1:01:56
If
1:01:56
someone
1:01:56
like
1:01:56
yourself
1:01:57
who's
1:01:57
big
1:01:57
into
1:01:58
fashion,
1:01:58
as
1:01:59
followers
1:01:59
of
1:01:59
this
1:01:59
show
1:02:00
will
1:02:00
know,
1:02:01
is
1:02:01
already
1:02:01
going
1:02:02
there
1:02:02
to.
1:02:02
To
1:02:02
do
1:02:03
their
1:02:03
searches.
1:02:04
You
1:02:04
know,
1:02:04
particularly
1:02:04
as,
1:02:05
you
1:02:05
know,
1:02:05
we
1:02:05
think
1:02:05
about
1:02:06
the
1:02:06
evolution
1:02:06
of
1:02:06
content,
1:02:07
too,
1:02:07
and
1:02:07
how
1:02:07
that
1:02:07
plays
1:02:08
into
1:02:08
it.
1:02:08
And
1:02:08
I
1:02:08
know
1:02:08
that
1:02:08
that's.
1:02:09
That's
1:02:09
a
1:02:09
key
1:02:09
way
1:02:09
that
1:02:09
you
1:02:10
think
1:02:10
about
1:02:10
things
1:02:10
and
1:02:10
search
1:02:10
things
1:02:11
too,
1:02:11
so.
1:02:11
All
1:02:12
right.
1:02:12
Wow.
1:02:12
Great
1:02:13
show,
1:02:13
chap.
1:02:13
Great
1:02:14
show.
1:02:14
All
1:02:14
right,
1:02:14
let's
1:02:15
do
1:02:15
the
1:02:15
lightning
1:02:15
round.
1:02:16
You
1:02:16
ready?
1:02:17
I'm
1:02:18
ready.
1:02:18
All
1:02:19
right.
1:02:19
The
1:02:19
Pope.
1:02:20
The
1:02:20
Pope,
1:02:20
yes.
1:02:20
The
1:02:21
Pope
1:02:21
is
1:02:21
in
1:02:21
Barcelona
1:02:22
the
1:02:23
very
1:02:23
same
1:02:23
week
1:02:24
as
1:02:24
I
1:02:24
am
1:02:25
to
1:02:25
bless
1:02:25
the
1:02:25
Sagrada
1:02:26
Familia.
1:02:27
What
1:02:27
is
1:02:27
the
1:02:27
one
1:02:28
word
1:02:28
you
1:02:28
would
1:02:28
use
1:02:29
to
1:02:29
describe
1:02:29
the
1:02:29
architecture
1:02:30
of
1:02:30
Antoni
1:02:31
Gaudi?
1:02:32
One
1:02:32
word
1:02:33
would
1:02:33
be
1:02:34
provocative.
1:02:36
That
1:02:36
would
1:02:37
be
1:02:37
my
1:02:37
one
1:02:37
word.
1:02:37
So
1:02:38
I
1:02:38
was
1:02:38
just
1:02:38
in
1:02:38
Barcelona
1:02:39
two
1:02:39
weeks
1:02:39
ago
1:02:40
myself
1:02:40
for
1:02:40
the
1:02:40
supply
1:02:41
chain
1:02:41
Gartner
1:02:42
supply
1:02:42
chain
1:02:42
symposium.
1:02:43
I
1:02:43
could
1:02:44
see
1:02:44
the
1:02:44
Basilica
1:02:45
from
1:02:45
my
1:02:45
hotel
1:02:46
window,
1:02:46
but
1:02:46
sadly,
1:02:47
I
1:02:47
did
1:02:47
not
1:02:47
make
1:02:47
it
1:02:48
there.
1:02:48
Yeah,
1:02:49
yeah.
1:02:49
I've
1:02:49
never
1:02:49
been
1:02:50
inside
1:02:50
it.
1:02:50
I've
1:02:50
never
1:02:51
been
1:02:51
inside
1:02:51
it.
1:02:51
I've
1:02:51
only
1:02:52
seen
1:02:52
Gotti
1:02:52
from
1:02:52
the
1:02:53
outside,
1:02:53
but,
1:02:53
yes,
1:02:54
that's
1:02:55
a
1:02:55
good
1:02:55
word.
1:02:57
All
1:02:57
right,
1:02:57
quit
1:02:58
question
1:02:58
to
1:02:59
you,
1:02:59
Chris.
1:03:00
You
1:03:00
chose
1:03:01
to
1:03:01
spend
1:03:01
three
1:03:02
weeks
1:03:02
in
1:03:03
Europe
1:03:03
during
1:03:03
one
1:03:04
of
1:03:04
the
1:03:04
best
1:03:04
months
1:03:05
of
1:03:05
the
1:03:05
year
1:03:05
in
1:03:06
Minnesota.
1:03:07
Name
1:03:07
two
1:03:08
things
1:03:08
you
1:03:08
will
1:03:09
do
1:03:09
there
1:03:09
to
1:03:09
make
1:03:09
that
1:03:10
sacrifice
1:03:11
worth
1:03:11
it.
1:03:12
Oh,
1:03:12
man,
1:03:12
that's
1:03:12
a
1:03:13
really
1:03:13
good
1:03:13
question.
1:03:13
Yes.
1:03:13
I
1:03:14
don't
1:03:14
know
1:03:14
that
1:03:14
I
1:03:14
chose
1:03:14
to
1:03:15
either.
1:03:15
I
1:03:15
think
1:03:17
circumstances
1:03:18
dictated
1:03:18
that
1:03:19
I
1:03:19
had
1:03:19
to
1:03:19
give
1:03:19
that
1:03:19
up.
1:03:20
Yeah.
1:03:20
Why
1:03:20
can't
1:03:20
conferences
1:03:21
be
1:03:21
in
1:03:21
December?
1:03:23
Two
1:03:23
things
1:03:23
I'll
1:03:24
do.
1:03:24
Well,
1:03:24
one,
1:03:24
I'm
1:03:25
going
1:03:25
to
1:03:25
San
1:03:25
Sebastian.
1:03:27
Mrs.
1:03:28
Omnitok
1:03:28
and
1:03:28
I
1:03:28
are
1:03:28
going
1:03:29
to
1:03:29
San
1:03:29
Sebastian,
1:03:29
which
1:03:30
is
1:03:30
kind
1:03:30
of
1:03:30
on
1:03:30
our
1:03:30
bucket
1:03:31
list
1:03:31
of
1:03:32
world
1:03:32
stops,
1:03:33
particularly
1:03:34
for
1:03:34
the
1:03:34
food.
1:03:34
And
1:03:35
then
1:03:35
the
1:03:35
other
1:03:35
thing
1:03:35
in
1:03:36
Vienna
1:03:36
that
1:03:36
I'd
1:03:36
like
1:03:37
to
1:03:37
do
1:03:37
because
1:03:37
I'm
1:03:37
going
1:03:38
to
1:03:38
Vienna
1:03:38
in
1:03:38
the
1:03:38
third
1:03:38
week,
1:03:39
I'd
1:03:39
like
1:03:39
to
1:03:40
take
1:03:40
in,
1:03:40
like,
1:03:40
a
1:03:41
opera
1:03:41
or
1:03:42
some
1:03:42
type
1:03:42
of
1:03:42
concerto,
1:03:43
because
1:03:43
I
1:03:43
don't
1:03:44
know
1:03:44
why,
1:03:44
but
1:03:45
Vienna
1:03:45
is
1:03:45
probably
1:03:46
because
1:03:46
of
1:03:46
Mozart.
1:03:47
But
1:03:47
Vienna
1:03:48
is
1:03:48
synonymous
1:03:49
with
1:03:49
that
1:03:49
for
1:03:49
me.
1:03:49
Oh,
1:03:50
definitely
1:03:50
do
1:03:50
that.
1:03:51
Yeah.
1:03:51
I
1:03:51
went
1:03:51
to
1:03:51
Vienna
1:03:52
many
1:03:52
years
1:03:52
ago,
1:03:53
saw
1:03:55
an
1:03:55
orchestra
1:03:56
and
1:03:56
yeah,
1:03:56
it
1:03:56
is
1:03:57
definitely.
1:03:57
Yeah,
1:03:58
fantastic.
1:03:58
Definitely
1:03:59
worth
1:03:59
doing.
1:04:00
All
1:04:00
right.
1:04:00
I
1:04:00
was
1:04:00
looking,
1:04:00
I
1:04:00
was
1:04:01
looking
1:04:01
through
1:04:01
things
1:04:01
today
1:04:02
when
1:04:02
I
1:04:02
had
1:04:02
some
1:04:02
downtime
1:04:03
to
1:04:03
try
1:04:03
to,
1:04:03
to
1:04:04
make
1:04:04
that.
1:04:04
Make
1:04:04
that
1:04:05
happen.
1:04:05
So
1:04:05
hopefully
1:04:05
I
1:04:05
can
1:04:05
do
1:04:06
that.
1:04:06
All
1:04:06
right,
1:04:07
next
1:04:07
one.
1:04:08
Since
1:04:09
you're
1:04:09
a
1:04:09
Minnesota
1:04:09
guy
1:04:10
who
1:04:10
starts
1:04:10
this
1:04:10
season
1:04:11
for
1:04:11
the
1:04:11
Vikings
1:04:11
at
1:04:11
quarterback
1:04:12
KYLER
1:04:12
Murray
1:04:13
or
1:04:13
J.J.
1:04:13
mcCarthy?
1:04:16
Well,
1:04:16
Chris,
1:04:17
as
1:04:17
you
1:04:18
can
1:04:18
see
1:04:18
in
1:04:18
the
1:04:18
background,
1:04:18
being
1:04:19
a
1:04:19
Michigan
1:04:19
man
1:04:20
myself,
1:04:21
I
1:04:21
gotta
1:04:22
put
1:04:22
my
1:04:22
chips
1:04:22
down
1:04:22
on
1:04:23
jj.
1:04:24
He
1:04:24
is
1:04:25
a
1:04:25
warrior.
1:04:26
I
1:04:26
have
1:04:27
no
1:04:27
doubt
1:04:27
he
1:04:28
will
1:04:28
be
1:04:28
the
1:04:28
starter.
1:04:29
And
1:04:30
then
1:04:30
of
1:04:30
course,
1:04:30
he
1:04:30
will
1:04:31
proceed
1:04:31
to
1:04:31
take
1:04:31
the
1:04:31
Vikings
1:04:32
fans
1:04:33
on
1:04:33
a
1:04:33
roller
1:04:33
coaster
1:04:34
ride
1:04:34
the
1:04:34
rest
1:04:34
of
1:04:35
the
1:04:35
year
1:04:36
like
1:04:36
he
1:04:36
did
1:04:36
last
1:04:37
year.
1:04:37
Oh,
1:04:38
my
1:04:38
God,
1:04:38
I
1:04:39
forgot
1:04:39
you're
1:04:39
a
1:04:39
Michigan
1:04:40
guy
1:04:40
too.
1:04:40
I
1:04:40
was
1:04:40
gonna.
1:04:41
Oh,
1:04:41
my.
1:04:41
You
1:04:42
guys
1:04:42
are
1:04:42
such
1:04:42
homers.
1:04:43
Oh,
1:04:43
my
1:04:43
God.
1:04:45
Oh,
1:04:45
God.
1:04:46
No
1:04:46
way.
1:04:46
I
1:04:46
think
1:04:46
there's
1:04:47
no
1:04:47
way
1:04:47
that
1:04:47
happens.
1:04:47
But.
1:04:48
Okay,
1:04:48
I'll
1:04:48
give
1:04:49
it
1:04:49
to
1:04:49
you.
1:04:49
All
1:04:49
right.
1:04:50
All
1:04:50
right,
1:04:50
next
1:04:51
question
1:04:51
to
1:04:51
you,
1:04:51
Chris.
1:04:52
Name
1:04:52
your
1:04:53
favorite
1:04:53
European
1:04:54
retailer
1:04:55
and
1:04:55
why
1:04:55
they
1:04:56
are
1:04:56
your
1:04:56
favorite.
1:04:58
Oh,
1:04:59
wow.
1:05:00
My
1:05:01
favorite
1:05:01
European
1:05:02
retailer.
1:05:03
Oh,
1:05:03
God,
1:05:03
that's
1:05:04
a
1:05:04
good
1:05:04
question.
1:05:05
So
1:05:06
right
1:05:06
now
1:05:06
what's
1:05:06
jumping
1:05:07
into
1:05:07
my
1:05:07
head
1:05:07
is
1:05:08
Zara
1:05:09
is
1:05:09
a
1:05:09
great
1:05:09
retailer
1:05:11
and
1:05:11
also
1:05:12
Tesco
1:05:13
is
1:05:13
a
1:05:13
great
1:05:13
retailer,
1:05:13
but
1:05:14
I
1:05:14
think
1:05:14
right
1:05:14
now
1:05:14
I'd
1:05:14
have
1:05:15
to
1:05:15
say
1:05:15
Marks
1:05:16
and
1:05:16
Spencer.
1:05:16
They're
1:05:16
killing
1:05:17
it
1:05:17
right
1:05:17
now.
1:05:17
Like
1:05:18
they
1:05:18
have.
1:05:19
They
1:05:19
are
1:05:19
just
1:05:19
hitting
1:05:19
on
1:05:19
all
1:05:20
cylinders
1:05:20
and,
1:05:21
you
1:05:21
know,
1:05:21
despite
1:05:22
some
1:05:22
of
1:05:22
the
1:05:22
things
1:05:22
that
1:05:22
happen.
1:05:22
But
1:05:23
they're,
1:05:23
you
1:05:24
know,
1:05:24
I
1:05:24
talked
1:05:24
to,
1:05:24
I
1:05:24
talked
1:05:25
to
1:05:25
some
1:05:25
friends
1:05:25
of
1:05:25
mine
1:05:25
and
1:05:25
they
1:05:25
say
1:05:26
that
1:05:26
they've
1:05:26
got
1:05:26
the
1:05:26
best,
1:05:27
best
1:05:28
grocery
1:05:28
experience
1:05:28
in
1:05:28
the
1:05:29
business
1:05:29
in
1:05:29
the
1:05:29
uk.
1:05:29
So
1:05:30
that's,
1:05:30
that's,
1:05:30
that's
1:05:31
who
1:05:31
I'd
1:05:31
go
1:05:31
with
1:05:31
right
1:05:31
now.
1:05:32
Yeah.
1:05:33
Okay.
1:05:34
Yeah.
1:05:34
Another
1:05:34
my,
1:05:35
you
1:05:35
know,
1:05:35
just
1:05:35
to.
1:05:36
Yeah,
1:05:36
I'm
1:05:36
curious
1:05:37
too.
1:05:37
Like,
1:05:38
it's
1:05:38
just
1:05:39
a
1:05:39
super
1:05:39
interesting
1:05:42
capability.
1:05:43
I
1:05:43
met
1:05:43
with,
1:05:44
I
1:05:44
think,
1:05:44
the
1:05:44
CTO
1:05:45
of
1:05:45
Rayway
1:05:45
Groceries
1:05:46
Railway
1:05:48
at
1:05:48
NRF
1:05:49
in
1:05:49
this,
1:05:49
in.
1:05:50
In
1:05:50
this
1:05:51
past
1:05:51
January.
1:05:52
And
1:05:52
you
1:05:53
know,
1:05:53
we
1:05:53
were
1:05:53
talking
1:05:53
about
1:05:53
drones.
1:05:55
They
1:05:55
execute
1:05:56
a
1:05:56
drone
1:05:57
delivery
1:05:58
that
1:05:58
is
1:05:58
synchronized
1:05:59
with
1:05:59
a
1:06:00
bike
1:06:00
pickup
1:06:01
to
1:06:01
deliver
1:06:02
to
1:06:02
the
1:06:02
actual
1:06:03
house.
1:06:03
Like
1:06:03
it
1:06:04
is
1:06:04
a
1:06:04
two
1:06:04
step
1:06:05
grocery
1:06:05
delivery
1:06:06
from
1:06:06
drone
1:06:07
to
1:06:07
bike.
1:06:08
Like
1:06:08
that
1:06:08
is
1:06:09
happening
1:06:09
right
1:06:09
now.
1:06:10
It's
1:06:10
crazy.
1:06:11
That's
1:06:11
nuts.
1:06:12
Yeah,
1:06:12
there's
1:06:13
so
1:06:13
many
1:06:13
good
1:06:13
retailers
1:06:14
over
1:06:14
here
1:06:14
too.
1:06:14
Like,
1:06:14
even
1:06:15
as
1:06:15
you
1:06:15
were
1:06:15
talking
1:06:15
about,
1:06:15
it's
1:06:16
like
1:06:16
Roller
1:06:16
Group's
1:06:16
a
1:06:16
good
1:06:17
retailer.
1:06:18
Migros
1:06:18
down
1:06:18
in
1:06:18
Turkey
1:06:19
is
1:06:19
a
1:06:19
good
1:06:19
retailer.
1:06:20
You
1:06:20
know,
1:06:20
there's
1:06:20
just
1:06:21
so
1:06:21
much
1:06:21
good
1:06:21
reads.
1:06:22
I
1:06:22
love
1:06:22
that.
1:06:22
That's
1:06:22
why
1:06:23
I
1:06:23
love
1:06:23
coming
1:06:23
over
1:06:23
here.
1:06:23
So
1:06:24
actually,
1:06:24
maybe
1:06:24
I
1:06:24
did
1:06:24
choose
1:06:25
to
1:06:25
come
1:06:25
over
1:06:25
here.
1:06:25
Now
1:06:25
that
1:06:26
you're
1:06:26
saying
1:06:26
that,
1:06:26
Chap,
1:06:27
I
1:06:27
don't
1:06:27
know.
1:06:28
All
1:06:28
right,
1:06:29
well,
1:06:29
today's
1:06:29
podcast
1:06:30
was
1:06:30
produced
1:06:30
with
1:06:30
the
1:06:30
help
1:06:31
and
1:06:31
support
1:06:31
of
1:06:31
Ella
1:06:32
Siriord.
1:06:33
Ella,
1:06:33
thanks
1:06:34
for
1:06:34
all
1:06:34
that
1:06:34
you
1:06:34
do
1:06:34
to
1:06:35
make
1:06:35
this
1:06:35
podcast
1:06:36
what
1:06:36
it
1:06:36
is
1:06:36
each
1:06:37
and
1:06:37
every
1:06:37
week.
1:06:37
Thanks
1:06:38
for
1:06:38
adding
1:06:38
your
1:06:38
commentary,
1:06:39
as
1:06:39
you
1:06:39
just
1:06:39
did
1:06:40
as
1:06:40
well.
1:06:40
Happy
1:06:41
birthday
1:06:41
today
1:06:42
to
1:06:42
Gina
1:06:42
Gershon,
1:06:43
Bill
1:06:43
Burr,
1:06:43
and
1:06:44
to
1:06:44
Vanessa
1:06:44
Kensington
1:06:45
herself,
1:06:46
Elizabeth
1:06:47
Hurley.
1:06:48
Oh,
1:06:48
behave.
1:06:49
Ella
1:06:50
has
1:06:50
no
1:06:50
idea
1:06:50
what
1:06:51
that
1:06:51
reference
1:06:51
is.
1:06:51
And
1:06:51
remember,
1:06:52
if
1:06:52
you
1:06:52
can
1:06:52
only
1:06:52
read
1:06:52
or
1:06:53
listen
1:06:53
to
1:06:53
one
1:06:53
retail
1:06:54
blog
1:06:54
in
1:06:54
the
1:06:54
business,
1:06:54
make
1:06:55
it
1:06:55
Omnitok.
1:06:56
Our
1:06:56
Fast
1:06:56
Five
1:06:56
podcast
1:06:57
is
1:06:57
the
1:06:57
quickest,
1:06:57
fastest
1:06:58
rundown
1:06:59
of
1:06:59
all
1:06:59
the
1:06:59
week's
1:06:59
top
1:07:00
news.
1:07:00
And
1:07:00
our
1:07:00
daily
1:07:00
newsletter,
1:07:01
the
1:07:01
Retail
1:07:01
Daily
1:07:02
Minute,
1:07:02
tells
1:07:02
you
1:07:02
all
1:07:02
you
1:07:03
need
1:07:03
to
1:07:03
know
1:07:03
each
1:07:03
day
1:07:04
to
1:07:04
stay
1:07:04
on
1:07:04
top
1:07:05
of
1:07:05
your
1:07:05
game
1:07:05
as
1:07:05
a
1:07:05
retail
1:07:06
executive.
1:07:06
And
1:07:06
also
1:07:07
regularly
1:07:07
features
1:07:07
special
1:07:08
content
1:07:08
that
1:07:08
is
1:07:08
exclusive
1:07:09
to
1:07:09
us
1:07:09
and
1:07:10
we
1:07:10
take
1:07:10
a
1:07:10
ton
1:07:10
of
1:07:10
pride
1:07:10
in
1:07:11
doing
1:07:11
just
1:07:11
for
1:07:11
you.
1:07:12
Thanks
1:07:12
as
1:07:12
always
1:07:12
for
1:07:12
listening
1:07:12
in.
1:07:13
Please
1:07:13
remember,
1:07:13
like
1:07:13
and
1:07:13
leave
1:07:14
us
1:07:14
a
1:07:14
review
1:07:14
wherever
1:07:14
you
1:07:15
happen
1:07:15
to
1:07:15
listen
1:07:15
to
1:07:15
your
1:07:15
podcast
1:07:16
or
1:07:16
on
1:07:16
YouTube.
1:07:17
You
1:07:17
can
1:07:17
follow
1:07:17
us
1:07:17
today
1:07:17
by
1:07:17
simply
1:07:18
going
1:07:18
to
1:07:18
YouTube.com
1:07:20
omnitalkretail
1:07:21
Chap,
1:07:22
if
1:07:22
people
1:07:22
want
1:07:22
to
1:07:22
get
1:07:22
in
1:07:22
touch
1:07:23
with
1:07:23
you,
1:07:23
which
1:07:23
I'm
1:07:23
guessing
1:07:23
a
1:07:24
lot
1:07:24
of
1:07:24
people
1:07:24
are
1:07:24
going
1:07:24
to
1:07:25
want
1:07:25
to,
1:07:25
after
1:07:25
listening
1:07:25
to
1:07:26
this
1:07:26
podcast,
1:07:26
what's
1:07:26
the
1:07:26
best
1:07:27
way
1:07:27
for
1:07:27
them
1:07:27
to
1:07:27
do
1:07:27
that?
1:07:29
Yeah,
1:07:29
just
1:07:30
hit
1:07:30
me
1:07:30
up
1:07:30
on
1:07:31
LinkedIn.
1:07:32
My
1:07:32
contact
1:07:32
info
1:07:33
is
1:07:33
out
1:07:33
there.
1:07:33
Be
1:07:34
happy
1:07:34
to
1:07:34
connect.
1:07:35
Yeah,
1:07:35
that's
1:07:35
how
1:07:35
you
1:07:35
and
1:07:36
I've
1:07:36
been
1:07:36
chatting
1:07:36
for
1:07:36
years
1:07:37
now
1:07:37
at
1:07:37
this.
1:07:37
So,
1:07:38
yes,
1:07:38
yes,
1:07:38
I
1:07:39
highly,
1:07:39
highly
1:07:40
recommend
1:07:40
it,
1:07:40
everybody.
1:07:41
All
1:07:41
right,
1:07:42
well,
1:07:42
on
1:07:42
behalf
1:07:42
of
1:07:43
Chap,
1:07:43
myself,
1:07:44
producer
1:07:45
Ella,
1:07:45
and
1:07:46
all
1:07:46
of
1:07:46
us
1:07:46
here
1:07:47
at
1:07:47
Omnitalk
1:07:48
Retail,
1:07:48
as
1:07:48
always,
1:07:49
be
1:07:49
careful
1:07:49
out
1:07:50
there.