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Retail Automation at Scale: What It Takes to Deploy AI, Robotics & Data-Driven Tasks Successfully | 5IM · Omni Talk Retail

• 0:00
  Foreigning
• 0:10
  us
• 0:10
  now
• 0:10
  for
• 0:10
  five
• 0:10
  insightful
• 0:11
  minutes
• 0:11
  is
• 0:11
  Caitlin
• 0:12
  Allen.
• 0:12
  Caitlin
• 0:13
  is
• 0:13
  the
• 0:13
  SVP
• 0:14
  of
• 0:14
  market
• 0:14
  at
• 0:14
  Simbee.
• 0:15
  And
• 0:15
  Caitlin
• 0:16
  is
• 0:16
  here
• 0:16
  to
• 0:16
  share
• 0:16
  her
• 0:16
  thoughts
• 0:17
  on
• 0:17
  the
• 0:17
  puts
• 0:17
  and
• 0:17
  takes
• 0:18
  of
• 0:18
  deploying
• 0:18
  AI
• 0:19
  and
• 0:19
  robotic
• 0:19
  automation
• 0:20
  at
• 0:20
  scale
• 0:21
  inside
• 0:21
  retail
• 0:22
  organizations.
• 0:23
  Caitlin,
• 0:23
  let's
• 0:24
  start
• 0:24
  off
• 0:24
  with
• 0:25
  this.
• 0:25
  What,
• 0:25
  what
• 0:26
  do
• 0:26
  you
• 0:26
  think
• 0:26
  are
• 0:26
  retail
• 0:27
  executive
• 0:27
  buyers
• 0:28
  largest
• 0:28
  misconceptions
• 0:30
  when
• 0:30
  evaluating
• 0:31
  retail
• 0:31
  automation?
• 0:32
  It's
• 0:33
  no
• 0:33
  longer
• 0:33
  about
• 0:34
  if
• 0:34
  automation
• 0:34
  is
• 0:35
  driving
• 0:35
  value,
• 0:35
  but
• 0:36
  how.
• 0:37
  And
• 0:37
  so
• 0:37
  following
• 0:37
  where
• 0:38
  industry
• 0:38
  leaders
• 0:39
  invest
• 0:39
  is
• 0:40
  important.
• 0:40
  And
• 0:41
  we
• 0:41
  see
• 0:41
  the
• 0:41
  top
• 0:41
  CEOs
• 0:42
  and
• 0:42
  CFOs
• 0:43
  prioritizing
• 0:44
  three
• 0:44
  things.
• 0:46
  The
• 0:46
  first
• 0:46
  is
• 0:47
  data
• 0:48
  quality,
• 0:48
  then
• 0:49
  scalability
• 0:49
  and
• 0:50
  then
• 0:50
  store
• 0:50
  coverage.
• 0:51
  Yes.
• 0:51
  Know
• 0:52
  with
• 0:52
  the
• 0:53
  onset
• 0:53
  of
• 0:53
  AI,
• 0:54
  modern
• 0:54
  data
• 0:55
  models
• 0:55
  are
• 0:55
  needed.
• 0:56
  And
• 0:56
  yet
• 0:56
  poor
• 0:56
  data
• 0:57
  is
• 0:57
  the
• 0:57
  Achilles
• 0:58
  heel
• 0:58
  of
• 0:58
  most
• 0:59
  automation
• 0:59
  solutions.
• 1:00
  And
• 1:00
  so
• 1:00
  we
• 1:00
  see
• 1:01
  vendors,
• 1:02
  we
• 1:02
  see
• 1:02
  retailers
• 1:03
  rather
• 1:03
  working
• 1:04
  with
• 1:04
  vendors
• 1:04
  that,
• 1:06
  that
• 1:06
  really
• 1:07
  work
• 1:07
  with
• 1:07
  high
• 1:07
  data
• 1:08
  standards,
• 1:08
  defining
• 1:09
  those
• 1:09
  necessary
• 1:09
  elements
• 1:10
  in
• 1:10
  history
• 1:10
  and
• 1:11
  quality
• 1:12
  related
• 1:12
  to
• 1:13
  scalability
• 1:14
  as
• 1:15
  they're
• 1:15
  reinventing
• 1:16
  retail
• 1:16
  operations.
• 1:17
  Retail,
• 1:18
  retail's
• 1:19
  best
• 1:19
  are
• 1:19
  testing
• 1:20
  automation
• 1:20
  in
• 1:21
  areas
• 1:21
  like
• 1:21
  on
• 1:21
  shelf
• 1:22
  availability
• 1:22
  and
• 1:22
  price
• 1:23
  integrity
• 1:23
  to
• 1:23
  get
• 1:23
  started.
• 1:24
  And
• 1:24
  then
• 1:25
  they're
• 1:25
  looking
• 1:25
  at
• 1:25
  scaling
• 1:26
  other
• 1:26
  use
• 1:26
  cases
• 1:27
  be
• 1:27
  it
• 1:27
  across
• 1:27
  allocation
• 1:28
  planning,
• 1:28
  forecasting,
• 1:29
  planogram
• 1:30
  compliance
• 1:30
  or
• 1:31
  what
• 1:31
  have
• 1:31
  you.
• 1:32
  And
• 1:32
  then
• 1:32
  the
• 1:32
  final
• 1:33
  piece
• 1:33
  is
• 1:33
  store
• 1:34
  coverage.
• 1:35
  And
• 1:35
  you
• 1:35
  know,
• 1:35
  today
• 1:36
  retailers
• 1:36
  are
• 1:36
  tracking
• 1:37
  when
• 1:37
  products
• 1:37
  arrive
• 1:37
  and
• 1:38
  when
• 1:38
  they
• 1:38
  leave,
• 1:38
  but
• 1:39
  they
• 1:39
  have,
• 1:40
  they
• 1:40
  lack
• 1:40
  visibility
• 1:41
  into
• 1:41
  their
• 1:42
  store
• 1:42
  of
• 1:42
  what
• 1:42
  happens
• 1:42
  in
• 1:43
  between
• 1:43
  that
• 1:44
  that
• 1:44
  point
• 1:44
  in
• 1:45
  time.
• 1:45
  And
• 1:45
  so
• 1:46
  top
• 1:46
  CEOs
• 1:46
  and
• 1:46
  CFOs
• 1:47
  are
• 1:47
  prioritizing
• 1:48
  autom
• 1:49
  that
• 1:50
  surface
• 1:51
  the
• 1:51
  actions
• 1:51
  that
• 1:51
  matter
• 1:52
  most
• 1:52
  that
• 1:52
  they
• 1:52
  can
• 1:52
  start
• 1:53
  to
• 1:53
  understand
• 1:53
  what
• 1:53
  true
• 1:54
  execution
• 1:55
  looks
• 1:55
  like
• 1:55
  in
• 1:55
  the
• 1:55
  store.
• 1:56
  And
• 1:56
  I
• 1:56
  would
• 1:56
  say
• 1:57
  in
• 1:57
  closing
• 1:57
  that
• 1:58
  all
• 1:58
  of
• 1:59
  those
• 1:59
  three
• 1:59
  priorities
• 2:00
  really
• 2:00
  expose
• 2:01
  the
• 2:01
  misconception
• 2:02
  that
• 2:02
  leads
• 2:02
  when
• 2:03
  evaluating
• 2:03
  retail
• 2:04
  automation
• 2:04
  which
• 2:05
  is
• 2:05
  over
• 2:05
  rotating
• 2:06
  on
• 2:06
  one
• 2:06
  device
• 2:06
  type.
• 2:07
  This
• 2:07
  is
• 2:07
  really
• 2:08
  a
• 2:08
  conversation
• 2:08
  that
• 2:08
  needs
• 2:09
  to
• 2:09
  be
• 2:09
  about
• 2:10
  combining
• 2:10
  sensors
• 2:11
  for
• 2:11
  optimal
• 2:11
  coverage
• 2:12
  and
• 2:12
  data
• 2:12
  quality.
• 2:13
  Okay,
• 2:13
  well
• 2:14
  Caitlin,
• 2:14
  we
• 2:14
  know
• 2:14
  that
• 2:15
  Simbi
• 2:15
  uses
• 2:16
  computer
• 2:16
  vision
• 2:17
  AI.
• 2:17
  It's
• 2:18
  still
• 2:18
  new
• 2:18
  to
• 2:18
  some
• 2:19
  of
• 2:19
  the
• 2:19
  retailers
• 2:19
  listening
• 2:20
  to
• 2:20
  our
• 2:21
  program.
• 2:21
  So
• 2:22
  can
• 2:22
  you
• 2:22
  help
• 2:22
  us
• 2:22
  identify
• 2:23
  what
• 2:24
  differentiates
• 2:25
  good
• 2:25
  computer
• 2:26
  vision
• 2:26
  AI
• 2:26
  from
• 2:27
  bad?
• 2:28
  Sure.
• 2:28
  So
• 2:29
  computer
• 2:29
  vision
• 2:29
  is
• 2:30
  what's
• 2:30
  used
• 2:30
  by
• 2:31
  things
• 2:31
  like
• 2:31
  fixed
• 2:32
  cameras
• 2:32
  on
• 2:32
  the
• 2:32
  shelf
• 2:33
  or
• 2:33
  autonomous,
• 2:34
  autonomous
• 2:35
  mobile
• 2:35
  robots,
• 2:35
  et
• 2:36
  cetera,
• 2:36
  just
• 2:36
  to
• 2:36
  kind
• 2:36
  of
• 2:36
  ground
• 2:37
  that
• 2:37
  in
• 2:37
  something
• 2:37
  that
• 2:37
  we
• 2:38
  can
• 2:38
  all
• 2:38
  see.
• 2:38
  And
• 2:39
  I
• 2:39
  would
• 2:39
  say
• 2:39
  one
• 2:40
  factor
• 2:40
  really
• 2:40
  separates
• 2:41
  high
• 2:41
  value
• 2:41
  computer
• 2:42
  vision
• 2:42
  from
• 2:42
  the
• 2:42
  rest
• 2:42
  with
• 2:43
  two
• 2:43
  key
• 2:43
  supporting
• 2:44
  elements.
• 2:44
  So
• 2:45
  the
• 2:45
  main
• 2:45
  thing
• 2:45
  that's
• 2:45
  important
• 2:46
  is
• 2:46
  value.
• 2:46
  That's
• 2:47
  been
• 2:47
  proven
• 2:48
  at
• 2:48
  scale
• 2:49
  across
• 2:49
  multiple
• 2:50
  chain
• 2:50
  wide
• 2:50
  deployments,
• 2:51
  in
• 2:51
  multiple
• 2:51
  retail
• 2:52
  subsectors,
• 2:53
  in
• 2:53
  geographies
• 2:54
  and
• 2:54
  use
• 2:54
  cases
• 2:55
  where
• 2:55
  there's
• 2:55
  many
• 2:55
  applications.
• 2:56
  It's
• 2:56
  easy
• 2:57
  to
• 2:57
  claim
• 2:57
  that
• 2:57
  you
• 2:58
  have
• 2:58
  a
• 2:58
  product
• 2:58
  that
• 2:58
  does
• 2:59
  certain
• 2:59
  things.
• 2:59
  But
• 2:59
  then
• 2:59
  when,
• 3:00
  when
• 3:00
  vendors
• 3:01
  or
• 3:01
  when
• 3:01
  retailers
• 3:02
  dig
• 3:02
  in
• 3:02
  to
• 3:03
  verify
• 3:03
  vendor
• 3:04
  claims,
• 3:04
  they
• 3:04
  often
• 3:04
  find
• 3:05
  out
• 3:05
  that,
• 3:05
  you
• 3:05
  know,
• 3:05
  claims
• 3:06
  might
• 3:06
  be
• 3:06
  a
• 3:06
  little
• 3:06
  hand
• 3:06
  wavy
• 3:08
  and
• 3:08
  really
• 3:08
  like.
• 3:09
  The
• 3:09
  reason
• 3:09
  I
• 3:09
  start
• 3:10
  with
• 3:10
  that
• 3:10
  non
• 3:10
  technical
• 3:11
  answer
• 3:11
  is
• 3:11
  this.
• 3:11
  This
• 3:11
  is
• 3:12
  about
• 3:12
  the
• 3:12
  business
• 3:12
  outcome,
• 3:13
  right?
• 3:13
  That's
• 3:14
  how
• 3:14
  to,
• 3:14
  to
• 3:14
  really
• 3:15
  take
• 3:16
  a
• 3:16
  sense
• 3:17
  for
• 3:17
  whether
• 3:18
  computer
• 3:18
  vision
• 3:18
  is
• 3:18
  good
• 3:19
  or
• 3:19
  bad.
• 3:20
  And
• 3:20
  then
• 3:20
  the
• 3:21
  supporting
• 3:21
  points
• 3:21
  for
• 3:21
  that
• 3:22
  are
• 3:22
  really
• 3:22
  around
• 3:22
  depth
• 3:23
  perception
• 3:23
  and
• 3:23
  total
• 3:23
  cost
• 3:24
  of
• 3:24
  ownership.
• 3:25
  So
• 3:25
  depth
• 3:26
  perception
• 3:26
  is
• 3:26
  basically
• 3:27
  another
• 3:27
  way
• 3:27
  of
• 3:27
  saying
• 3:28
  that
• 3:28
  good
• 3:29
  computer
• 3:29
  vision
• 3:29
  sees
• 3:30
  in
• 3:30
  3D
• 3:31
  mobile
• 3:32
  robots
• 3:32
  have
• 3:32
  become
• 3:33
  known
• 3:33
  as
• 3:34
  the
• 3:34
  most
• 3:35
  accurate
• 3:35
  and
• 3:35
  scalable
• 3:36
  and
• 3:36
  cost
• 3:37
  effective
• 3:37
  retail
• 3:37
  solution
• 3:38
  because
• 3:39
  they
• 3:39
  can
• 3:39
  move
• 3:39
  around
• 3:40
  and
• 3:40
  that
• 3:40
  eliminates
• 3:41
  data
• 3:41
  coverage
• 3:41
  gaps.
• 3:42
  And
• 3:42
  that
• 3:43
  also
• 3:43
  relates
• 3:43
  to
• 3:43
  the
• 3:44
  topic
• 3:44
  of
• 3:44
  total
• 3:45
  cost
• 3:45
  of
• 3:45
  ownership.
• 3:46
  When
• 3:47
  you
• 3:47
  have
• 3:47
  just
• 3:48
  fixed
• 3:49
  cameras,
• 3:49
  for
• 3:49
  instance,
• 3:50
  you
• 3:50
  have
• 3:50
  hundreds
• 3:50
  of
• 3:50
  them
• 3:50
  per
• 3:51
  store.
• 3:51
  That
• 3:52
  really
• 3:52
  drives
• 3:52
  up
• 3:52
  your
• 3:52
  costs
• 3:53
  and
• 3:53
  your
• 3:53
  maintenance
• 3:54
  as
• 3:54
  well
• 3:54
  as
• 3:55
  your
• 3:55
  risk
• 3:55
  of
• 3:55
  damage.
• 3:56
  Whereas
• 3:57
  a
• 3:57
  robot
• 3:58
  really
• 3:58
  requires
• 3:59
  minimal
• 3:59
  infrastructure
• 4:00
  and
• 4:00
  it's
• 4:00
  kind
• 4:01
  of
• 4:01
  the
• 4:01
  difference
• 4:01
  of
• 4:01
  managing
• 4:02
  just
• 4:02
  one
• 4:02
  device
• 4:03
  versus
• 4:03
  hundreds.
• 4:04
  So
• 4:04
  I
• 4:04
  would
• 4:04
  say
• 4:05
  bottom
• 4:05
  line,
• 4:05
  computer
• 4:06
  vision
• 4:06
  is
• 4:06
  really
• 4:06
  about
• 4:07
  having
• 4:08
  proven
• 4:09
  results
• 4:09
  at
• 4:10
  scale
• 4:10
  in
• 4:10
  prior
• 4:11
  applications.
• 4:12
  And
• 4:12
  that's
• 4:13
  especially
• 4:13
  the
• 4:13
  case
• 4:14
  when
• 4:14
  it
• 4:14
  is
• 4:14
  backed
• 4:15
  by
• 4:15
  a
• 4:16
  solid
• 4:16
  business
• 4:16
  case
• 4:16
  that
• 4:17
  spans
• 4:17
  depth
• 4:17
  perception
• 4:18
  and
• 4:18
  cost
• 4:19
  efficiency.
• 4:20
  Caitlin
• 4:20
  Oftentimes,
• 4:21
  and
• 4:21
  we've
• 4:21
  lived
• 4:22
  this,
• 4:22
  we
• 4:22
  see
• 4:22
  a
• 4:22
  disconnect
• 4:23
  between
• 4:23
  the
• 4:24
  stores,
• 4:24
  organizations
• 4:25
  and
• 4:25
  the
• 4:25
  HQ
• 4:25
  side
• 4:26
  of
• 4:26
  a
• 4:26
  retail
• 4:26
  operation.
• 4:27
  So
• 4:28
  what
• 4:28
  do
• 4:28
  you
• 4:29
  think
• 4:29
  are
• 4:29
  the
• 4:29
  most
• 4:29
  significant
• 4:30
  disconnection
• 4:31
  points
• 4:32
  between
• 4:32
  those
• 4:33
  two
• 4:33
  sides
• 4:33
  of
• 4:33
  the
• 4:33
  operation
• 4:34
  when
• 4:34
  it
• 4:34
  comes
• 4:34
  to
• 4:34
  retail
• 4:35
  tech
• 4:35
  deployments?
• 4:35
  And,
• 4:36
  and
• 4:36
  what,
• 4:36
  if
• 4:36
  anything,
• 4:37
  can
• 4:37
  both
• 4:38
  sides
• 4:38
  do
• 4:38
  about
• 4:38
  it?
• 4:39
  Successful
• 4:39
  rollouts?
• 4:40
  Chris
• 4:40
  they
• 4:40
  don't
• 4:40
  just
• 4:41
  test
• 4:41
  technology.
• 4:42
  They're
• 4:42
  really
• 4:43
  more
• 4:43
  dedicated
• 4:43
  about
• 4:43
  building
• 4:44
  momentum
• 4:45
  across
• 4:45
  the
• 4:45
  organization.
• 4:46
  And
• 4:46
  so
• 4:46
  we
• 4:46
  see
• 4:47
  the
• 4:47
  best
• 4:47
  retailers
• 4:48
  bridge
• 4:49
  that
• 4:49
  gap
• 4:50
  by
• 4:50
  doing
• 4:51
  three
• 4:51
  things.
• 4:52
  Picking
• 4:52
  representative
• 4:53
  pilot
• 4:53
  stores
• 4:54
  that
• 4:54
  reflect
• 4:55
  real
• 4:55
  business
• 4:56
  realities
• 4:56
  like
• 4:56
  store
• 4:57
  size,
• 4:57
  sales
• 4:57
  volume,
• 4:58
  you
• 4:58
  know,
• 4:58
  operational
• 4:59
  readiness,
• 4:59
  tech
• 4:59
  savviness.
• 5:00
  When
• 5:01
  they
• 5:01
  select
• 5:01
  their
• 5:01
  pilot
• 5:02
  stores,
• 5:03
  the
• 5:03
  second
• 5:03
  thing
• 5:04
  they
• 5:04
  do
• 5:04
  is
• 5:04
  stack
• 5:04
  rank
• 5:04
  their
• 5:05
  KPIs.
• 5:05
  And
• 5:05
  that's
• 5:06
  really
• 5:06
  about
• 5:06
  prioritizing
• 5:07
  the
• 5:07
  one
• 5:07
  or
• 5:07
  two
• 5:07
  that
• 5:08
  matter
• 5:08
  most.
• 5:09
  And
• 5:09
  that's
• 5:09
  usually
• 5:09
  something
• 5:10
  like
• 5:10
  profitability
• 5:11
  and
• 5:11
  on
• 5:11
  shelf
• 5:11
  availability
• 5:12
  and
• 5:12
  sometimes
• 5:13
  price
• 5:13
  accuracy.
• 5:14
  And
• 5:14
  then
• 5:14
  the
• 5:14
  third
• 5:15
  piece
• 5:15
  is
• 5:15
  around
• 5:15
  engaging
• 5:16
  store
• 5:16
  teams
• 5:16
  early,
• 5:17
  right?
• 5:17
  No
• 5:17
  one
• 5:17
  wants
• 5:18
  something
• 5:18
  to
• 5:18
  be
• 5:19
  thrown
• 5:19
  over
• 5:19
  the
• 5:19
  fence
• 5:19
  at
• 5:19
  them.
• 5:20
  So
• 5:20
  engaging
• 5:21
  store
• 5:21
  teams
• 5:22
  in
• 5:23
  decision
• 5:23
  making,
• 5:24
  thorough
• 5:24
  training,
• 5:24
  development,
• 5:25
  and
• 5:25
  emphasizing
• 5:26
  automation's
• 5:26
  role
• 5:27
  and
• 5:27
  being
• 5:27
  a
• 5:27
  power
• 5:27
  tool
• 5:28
  for
• 5:28
  them,
• 5:28
  not
• 5:28
  as
• 5:29
  a
• 5:29
  replacement
• 5:29
  of
• 5:29
  labor,
• 5:31
  really
• 5:31
  brings
• 5:32
  things
• 5:32
  over
• 5:32
  the
• 5:32
  line
• 5:33
  for
• 5:33
  all
• 5:33
  parties
• 5:33
  involved.
• 5:34
  The
• 5:34
  best
• 5:34
  thing
• 5:34
  about
• 5:35
  what
• 5:35
  happens
• 5:36
  when
• 5:36
  what
• 5:36
  you
• 5:36
  just
• 5:37
  were
• 5:37
  talking
• 5:37
  about
• 5:38
  takes
• 5:38
  place
• 5:38
  in
• 5:38
  a
• 5:38
  store
• 5:39
  is
• 5:39
  that
• 5:39
  there's
• 5:39
  some
• 5:39
  really
• 5:40
  big
• 5:40
  aha
• 5:40
  moments
• 5:41
  for
• 5:41
  those
• 5:42
  customers
• 5:42
  who
• 5:42
  are
• 5:42
  deploying
• 5:43
  robotics.
• 5:43
  Do
• 5:43
  you
• 5:43
  have
• 5:44
  any
• 5:44
  good
• 5:44
  examples
• 5:44
  that
• 5:44
  you
• 5:45
  could
• 5:45
  share
• 5:45
  with
• 5:45
  us
• 5:45
  quickly?
• 5:46
  Two
• 5:46
  come
• 5:46
  to
• 5:47
  mind.
• 5:47
  So
• 5:47
  most
• 5:48
  of
• 5:48
  those
• 5:48
  top
• 5:48
  performing
• 5:49
  stores
• 5:49
  that
• 5:49
  we're
• 5:49
  talking
• 5:50
  about,
• 5:50
  they
• 5:51
  find
• 5:51
  that
• 5:52
  60%
• 5:52
  of
• 5:53
  the
• 5:53
  items
• 5:53
  that
• 5:54
  they
• 5:54
  believe
• 5:54
  to
• 5:54
  be
• 5:54
  out
• 5:54
  of
• 5:55
  stock
• 5:55
  actually
• 5:55
  to
• 5:55
  be
• 5:56
  in
• 5:56
  store.
• 5:56
  So
• 5:56
  over
• 5:56
  half
• 5:57
  of
• 5:57
  the
• 5:57
  items
• 5:57
  they
• 5:57
  think
• 5:57
  they
• 5:58
  can't
• 5:58
  sell
• 5:58
  are
• 5:58
  there
• 5:59
  to
• 5:59
  be
• 5:59
  sold
• 5:59
  which
• 6:00
  is
• 6:00
  an
• 6:00
  amazing
• 6:00
  aha.
• 6:01
  And
• 6:01
  then
• 6:02
  I
• 6:02
  think
• 6:02
  the
• 6:02
  second
• 6:02
  Ann
• 6:03
  is
• 6:03
  really
• 6:03
  around
• 6:05
  kind
• 6:05
  of
• 6:05
  longer
• 6:06
  term
• 6:06
  use
• 6:06
  cases
• 6:06
  of
• 6:07
  understanding
• 6:07
  what
• 6:07
  real
• 6:08
  time
• 6:08
  shelf
• 6:08
  conditions
• 6:09
  and
• 6:09
  precise
• 6:10
  item
• 6:10
  location
• 6:11
  can
• 6:11
  do
• 6:11
  to
• 6:12
  inform
• 6:12
  things
• 6:12
  like
• 6:13
  better
• 6:13
  e
• 6:13
  commerce
• 6:13
  accuracy,
• 6:14
  automated
• 6:15
  reordering
• 6:15
  and
• 6:16
  demand
• 6:16
  forecasting
• 6:17
  and
• 6:17
  then
• 6:17
  merchandising
• 6:18
  at
• 6:18
  scale
• 6:18
  to
• 6:19
  the
• 6:19
  effect
• 6:19
  of
• 6:19
  things
• 6:19
  like
• 6:20
  retail
• 6:20
  media
• 6:21
  and
• 6:21
  working
• 6:21
  with
• 6:22
  suppliers
• 6:22
  and
• 6:22
  vendors.
• 6:23
  And
• 6:23
  that's
• 6:24
  such
• 6:24
  a
• 6:24
  privilege
• 6:25
  to
• 6:25
  see
• 6:25
  those
• 6:25
  kind
• 6:26
  of
• 6:26
  ahas
• 6:26
  go
• 6:26
  off
• 6:27
  because
• 6:27
  it's
• 6:27
  kind
• 6:27
  of
• 6:27
  rare
• 6:28
  to
• 6:28
  see
• 6:28
  your
• 6:28
  business
• 6:28
  in
• 6:28
  a
• 6:29
  new
• 6:29
  light.
• 6:29
  So
• 6:29
  that's
• 6:30
  one
• 6:30
  of
• 6:30
  the
• 6:30
  things
• 6:30
  I
• 6:30
  love
• 6:30
  about
• 6:31
  my
• 6:31
  job.
• 6:31
  Great
• 6:31
  stuff,
• 6:32
  Caitlin.
• 6:32
  Thank
• 6:32
  you.