ArticlePDF Available
Journal
of
Retailing
93
(1,
2017)
7–28
Shopper-Facing
Retail
Technology:
A
Retailer
Adoption
Decision
Framework
Incorporating
Shopper
Attitudes
and
Privacy
Concerns
J.
Jeffrey
Inman a,,
Hristina
Nikolova b
aKatz
Graduate
School
of
Business,
University
of
Pittsburgh,
United
States
bCarroll
School
of
Management,
Boston
College,
United
States
Available
online
13
February
2017
Abstract
Continual
innovation
and
new
technology
are
critical
in
helping
retailers’
create
a
sustainable
competitive
advantage.
In
particular,
shopper-
facing
technology
plays
an
important
role
in
increasing
revenues
and
decreasing
costs.
In
this
article,
we
briefly
discuss
some
of
the
salient
retail
technologies
over
the
recent
past
as
well
as
technologies
that
are
only
beginning
to
gain
traction.
Additionally,
we
present
a
shopper-centric
decision
calculus
that
retailers
can
use
when
considering
a
new
shopper-facing
technology.
We
argue
that
new
technologies
provide
value
by
either
increasing
revenue
through
(a)
attracting
new
shoppers,
(b)
increasing
share
of
volume
from
existing
shoppers,
or
(c)
extracting
greater
consumer
surplus,
or
decreasing
costs
through
offloading
labor
to
shoppers.
Importantly,
our
framework
incorporates
shoppers
by
considering
their
perceptions
of
the
new
technology
and
their
resulting
behavioral
reactions.
Specifically,
we
argue
that
shoppers
update
their
perceptions
of
fairness,
value,
satisfaction,
trust,
commitment,
and
attitudinal
loyalty
and
evaluate
the
potential
intrusiveness
of
the
technology
on
their
personal
privacy.
These
perceptions
then
mediate
the
effect
of
the
technology
on
shopper
behavioral
reactions
such
as
retail
patronage
intentions
and
WOM
communication.
We
present
preliminary
support
for
our
framework
by
examining
consumers’
perceptions
of
several
new
retail
technologies,
as
well
as
their
behavioral
intentions.
The
findings
support
our
thesis
that
shopper
perceptions
of
the
retailer
are
affected
by
new
shopper-facing
technologies
and
that
these
reactions
mediate
behavioral
intentions,
which
in
turn
drives
the
ROI
of
the
new
technology.
©
2017
New
York
University.
Published
by
Elsevier
Inc.
All
rights
reserved.
Keywords:
Retail
technology;
Shopper
privacy
concerns;
Proximity
marketing;
Self-scanning;
iBeacons;
Self-checkout
Introduction
Retail
technology
capabilities
have
never
been
greater;
retail-
ers
are
faced
with
an
increasing
array
of
potential
technologies
that
is
expanding
in
its
complexity
and
cost.
Overall
spending
on
retail
IT
was
projected
to
exceed
$190
billion
worldwide
in
2015
(Wilson
2014).
Retailers
are
faced
with
a
dizzying
array
of
technologies
and
terminology,
including
iBeacons,
mobile
POS,
Near
Field
Communications,
and
the
Internet
of
Things.
Retail-
ers
are
understandably
overwhelmed
by
the
options
and
may
adopt
technologies
without
a
clear
picture
of
both
how
they
fit
into
their
strategy
and,
potentially
more
important,
how
shoppers
will
react.
Corresponding
author.
E-mail
addresses:
jinman@katz.pitt.edu
(J.J.
Inman),
hristina.nikolova@bc.edu
(H.
Nikolova).
When
considering
adoption
of
a
new
shopper-facing
tech-
nology,
more
sophisticated
retailers’
decision
calculus
includes
financial
factors
such
as
ROI,
payback
period,
net
present
value,
internal
rate
of
return,
and
impact
on
profits.
Projects
that
gen-
erate
a
sufficiently
high
ROI
are
then
adopted
and
implemented.
However,
critical
assumptions
regarding
the
reaction
of
shop-
pers
to
the
new
technology
are
embedded
in
such
calculations.
These
assumptions
can
either
be
explicit
in
terms
of
shopper
met-
rics
such
as
basket
size
and
conversion
or
are
simply
implicitly
assumed
to
be
positive.
In
this
article,
we
argue
that
retailers’
decision
calculus
for
evaluating
the
adoption
of
shopper-facing
technology
needs
to
be
expanded
beyond
what
the
technology
can
potentially
deliver
to
consider
shopper
reactions
and
assess
what
the
technology
will
deliver.
Managers
are
often
excited
by
their
own
side
of
the
value
equation,
forgetting
that
shoppers
may
not
share
their
enthusiasm.
While
the
hope
for
positive
effects
from
retail
tech-
nology
in
terms
of
increased
basket
size,
share
of
wallet,
and
http://dx.doi.org/10.1016/j.jretai.2016.12.006
0022-4359/©
2017
New
York
University.
Published
by
Elsevier
Inc.
All
rights
reserved.
8
J.J.
Inman,
H.
Nikolova
/
Journal
of
Retailing
93
(1,
2017)
7–28
profit
are
the
motivating
forces
underlying
the
adoption
of
new
technology,
there
are
many
examples
of
unexpected
negative
outcomes.
For
example,
the
recent
data
breaches
at
major
retail-
ers
have
increased
shopper
wariness
of
technology.
Over
70
million
households
had
at
least
some
of
their
personal
informa-
tion
stolen
in
the
2013
data
breach
at
Target,
including
credit
card
information
for
40
million
households.
Target
also
was
embar-
rassed
by
a
report
in
the
New
York
Times
that
its
data
mining
efforts
used
to
target
shoppers
had
led
to
a
father
discovering
that
his
teenage
daughter
was
pregnant
(Duhigg
2012).
Unfor-
tunately,
Target
is
not
an
isolated
case.
Other
major
retailers
have
suffered
data
breaches
as
well,
including
Home
Depot
in
2013
(56
million
credit
card
accounts)
and
Nieman
Marcus
in
2013
(1
million
credit
card
accounts).
On
top
of
the
costs
of
replacing
shoppers’
credit
cards
and
the
effect
on
the
firm’s
stock
price,
a
recent
survey
by
CNBC
suggests
that
such
instances
result
in
a
shopper
backlash
fueled
by
lost
trust
(Conradt
2014).
Specifically,
they
find
that
almost
70%
of
respondents
correctly
identified
companies
that
had
been
breached
and
fifteen
percent
reported
that
they
stopped
shopping
at
breached
retailers.
Further,
30%
reported
planning
to
pay
by
cash
in
the
future
when
shopping
at
a
breached
retailer
instead
of
via
credit
or
debit
card—and
spending
tends
to
be
lower
when
paying
with
cash
(e.g.,
Inman
and
Winer
1998).
In
the
face
of
such
potential
consequences,
how
should
a
retailer
evaluate
a
new
shopper-facing
technology
and
decide
whether
to
adopt
it?
Surprisingly,
an
interested
retailer
will
find
little
guidance
in
the
academic
literature.
While
the
literature
has
examined
the
effect
of
specific
technologies
such
as
self-
service
technologies
(e.g.,
Meuter
et
al.
2000,
2005),
payment
systems
(e.g.,
Giebelhausen
et
al.
2014),
and
mobile
coupons
(e.g.,
Hui
et
al.
2013),
there
is
currently
no
framework
to
which
a
retailer
can
avail
to
guide
consideration
of
a
new
shopper-facing
technology.
That
is
the
focus
of
this
article.
We
begin
by
briefly
reviewing
some
of
the
major
techno-
logical
innovations
in
retail
during
the
past
50
years.
We
then
present
our
equity
theory-based
framework
that
examines
retail
technologies
through
the
lens
of
its
potential
positive
and
neg-
ative
consequences
for
the
retailer
versus
its
potential
positive
and
negative
consequences
for
shoppers.
We
close
with
a
series
of
examples
and
a
survey
of
shopper
reactions
to
potential
retail
technologies
that
are
looming
on
the
horizon.
A
Brief
Overview
Of
Retail
Technology:
Past,
Present,
And
Future
Many
major
technological
innovations
have
revolutionized
retailing
over
the
past
several
decades.
In
this
section,
we
describe
some
of
them
and
then
discuss
more
recent
innova-
tions
as
well
as
technologies
that
are
beginning
to
be
broadly
introduced
by
retailers.
We
do
not
claim
to
capture
every
inno-
vation,
but
rather
focus
on
some
of
the
most
disruptive
retail
technologies
that
we
identified
from
conversations
with
several
industry
experts.
Past
Retail
Technology
Highlights
Barcode
Scanning
Arguably
the
most
important
retail
technology
innovation
in
the
twentieth
century
was
the
adoption
of
the
UPC
barcode
scanning
after
developments
of
the
1960s
such
as
inexpensive
lasers
and
semiconductors
finally
made
scanners
simple
and
cost
effective
(Seideman
1993).
In
1974
at
a
Marsh
supermarket
in
Troy,
Ohio,
the
first
retail
product
was
sold
via
a
scanner—a
pack
of
chewing
gum.
Once
85%
of
all
products
carried
UPC
codes
a
few
years
later,
adoption
of
barcode
scanners
took
off.
For
example,
less
than
one
percent
of
grocery
stores
nationwide
had
scanners
in
1978,
but
by
1981
the
figure
was
ten
percent
and
a
third
of
U.S.
grocery
stores
were
using
scanners
by
1984.
Today,
virtually
every
retailer
is
equipped
with
a
barcode
scanner.
The
capability
to
scan
items
using
a
standardized
code
pro-
vided
retailers
with
real-time
transaction
data
and
enabled
them
to
identify
fast-moving
items.
Retailers
could
also
couple
these
data
with
information
on
shelf
space
allocation
to
create
met-
rics
to
assess
product-level
ROI.
Importantly,
transaction
data
were
collected
more
accurately
and
objectively
and
could
be
combined
with
data
on
causal
factors
such
as
price,
feature
advertising,
and
display
to
estimate
the
drivers
of
sales.
The
availability
of
scanner
data
also
spurred
a
sizable
body
of
research
on
quantitative
models
of
consumer
buying
behavior
(e.g.,
Guadagni
and
Little
1983).
Today,
scanner
data
are
a
main-
stay
of
performance
tracking
and
strategic
decision
making
for
retailers
and
CPG
firms
alike.
In
fact,
it
would
not
be
an
exagger-
ation
to
say
that
the
advent
of
scanning
technology
made
possible
much
of
the
retail
technology
innovation
that
has
followed.
Videocart
While
much
is
made
today
of
location-based
marketing,
this
is
not
a
new
concept.
In
fact,
Malec
and
Moser
(1994)
filed
a
patent
in
1988
for
an
“Intelligent
Shopping
Cart
System
Having
Cart
Position
Determining
and
Service
Queue
Position
Securing
Capability.”
The
abstract
describes
the
system
as
a
“shopping
cart
display
system
that
includes
a
cart
mounted
display
that
is
responsive
to
unique
trigger
signals
provided
by
respective
transmitters
associated
with
respective
fixed
locations.
When
the
display
receives
a
unique
trigger
signal,
it
displays
advertising
associated
with
the
respective
location.”
For
example,
the
system
would
know
when
the
shopper
is
in
the
dairy
section
and
could
show
an
ad
for
Dannon
yogurt.
The
ads
would
be
funded
by
the
CPG
firms.
A
sketch
of
the
cart
from
the
patent
is
shown
in
Fig.
1.
Moser
formed
Videocart,
Inc.
which
was
touted
by
the
New
York
Times
(Henriques
1991)
as,
“Videocart
Inc.,
whose
computer-equipped
shopping
cart
provides
point-of-purchase
advertising
as
the
shopper
moves
through
the
store.”
The
first
author
recalls
using
a
Videocart
at
a
Vons
supermarket
in
Her-
mosa
Beach
in
1991.
At
Videocart’s
peak
in
1992,
46,000
shopping
carts
were
fitted
with
the
displays
in
retailers
such
as
Schnucks
in
St.
Louis
and
Dominicks
in
Chicago.
However,
in
1993
Videocart
filed
for
bankruptcy.
Several
reasons
underlie
the
failure
of
Videocart.
First,
most
of
the
pop-up
ads
were
reminder
ads
and
did
not
offer
a
financial
J.J.
Inman,
H.
Nikolova
/
Journal
of
Retailing
93
(1,
2017)
7–28
9
Fig.
1.
Videocart
sketch
from
original
patent
5,295,064.
incentive
for
shoppers,
making
the
effect
on
incremental
sales
difficult
to
measure.
Second,
shoppers
did
not
like
the
screen
location
mounted
on
the
handle,
which
blocked
a
view
of
the
cart
and
took
up
a
large
part
of
the
cart’s
seat,
which
many
shoppers
used
to
place
valuables,
fragile
items,
or
a
small
child.
Finally,
retailers
had
difficulty
keeping
the
batteries
on
the
device
charged,
so
oftentimes
shoppers
were
pushing
a
nonfunctioning
cart.
Additionally,
the
costs
and
installation
of
the
on-cart
display
units
and
the
in-store
infrared
system
were
prohibitive.
In-Store
Coupon
Dispensers
In
1992,
Patent
5,083,765
was
awarded
to
George
Kringel
for
a
device
described
as
a
“stand-alone
dispenser
including
an
integral
electrical
power
supply
provided
for
reliably
dispensing
individual
sheets,
such
as
coupons,
from
a
stack”
(Kringel
1992).
The
assignee
was
ActMedia,
which
rolled
them
out
in
retailers
across
the
country.
Stores
would
install
the
coupon
dispensers
next
to
the
product
for
which
the
coupon
was
offered.
A
limited
number
of
dispensers
were
installed
in
each
store
because
CPG
firms
that
paid
to
have
the
dispenser
for
their
product
would
be
given
category
exclusivity.
By
1996,
ActMedia
had
annual
sales
of
approximately
$500
M
and
was
in
40,000
supermarkets,
drugstores,
and
mass
merchandisers.
News
America
entered
the
at-shelf
couponing
business
in
1996
and
acquired
the
parent
company
of
ActMedia
in
1997.
News
America
subsequently
renamed
the
dispenser
the
SmartSource
Coupon
Machine.
In
2007,
a
patent
was
granted
for
a
“process
for
distributing
product
entitlements
to
members
of
a
retail
store’s
frequent
shop-
per
program”
(Muldoon
2007).
This
patent
describes
a
process
for
distributing
coupons
at
a
retailer
to
members
of
the
retailer’s
frequent
shopper
program
(FSP).
The
process
focuses
on
com-
paring
the
shoppers’
purchase
history
to
available
coupons.
The
shopper
enters
their
FSP
number
into
the
dispenser
in
the
store,
which
triggers
coupons
that
fit
the
shopper’s
purchase
history.
The
coupons
are
then
redeemed
at
checkout.
Today,
CVS
is
an
example
of
a
retailer
that
uses
in-store
coupon
dispensers
to
offer
loyalty
card
members
coupons
based
on
their
previous
purchase
history.
Kiosks
A
kiosk
consists
of
a
touchscreen,
a
computer,
and
perhaps
a
printer
and
credit
card
reader—all
enclosed
in
a
secure
cabi-
net.
Kiosks
can
deliver
information
or
they
can
promote
and
sell
products
and
services.
Most
kiosks
are
located
in
public
places,
such
as
stores,
airports,
malls,
and
hotel
and
corporate
lobbies.
The
landmark
implementation
of
kiosks
is
credited
to
Florshiem
Shoes,
which
installed
them
in
1985
in
over
600
locations.
The
kiosks
provided
images
and
video
promotion
for
shoppers
who
wished
to
purchase
shoes
that
were
unavailable
in
the
particular
store.
The
purchase
could
be
paid
for
at
the
kiosk
and
the
trans-
action
was
then
transmitted
to
Florsheim
for
delivery
to
either
the
shopper’s
home
or
to
the
store.
While
the
Florsheim
system
became
the
benchmark
for
inno-
vative
self-service
shopping
technology,
it
is
only
in
the
past
few
years
that
the
kiosk
market
has
taken
off.
Kiosks
have
now
spread
across
a
wide
array
of
venues,
such
as
airports,
hotels,
banks,
grocery
stores,
and
clothing
stores.
Kiosks
are
used
to
dispense
money
(ATMs),
boarding
passes,
tickets
for
movies,
trains,
and
theaters,
and
DVDs.
The
firm
Outerwall
alone
operates
over
40,000
Redbox
DVD
dispensing
kiosks
and
over
20,000
self-service
Coinstar
coin-counting
kiosks
throughout
the
U.S.
(Outerwall
Company
2016).
Total
revenue
for
Outerwall
was
$2.2
B
in
2015.
Walmart
Smart
Network
Walmart
pioneered
the
idea
of
shopper
media
through
its
Wal-
mart
Smart
Network.
The
goal
of
the
Walmart
Smart
Network
was
to
increase
category
sales
by
communicating
to
shoppers
at
or
near
the
point
of
purchase.
The
sales
pitch
seemed
com-
pelling:
in
a
very
fragmented
media
landscape,
advertisers
could
reach
almost
8
million
Walmart
shoppers
each
week
via
27,000
screens
across
2,700
stores.
Video
monitors
would
be
positioned
10
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Inman,
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/
Journal
of
Retailing
93
(1,
2017)
7–28
throughout
the
store
in
strategic
locations
to
communicate
infor-
mation
to
shoppers
about
topics
such
as
new
products
and
limited
time
offers.
The
Walmart
TV
network
was
rolled
out
in
1998
and
consisted
of
CRT
monitors
mounted
high
above
the
retail
floor
showing
ads.
The
Walmart
TV
Network
provided
another
source
of
revenue
for
Walmart,
as
vendors
could
fund
the
ads
from
their
advertising
budget
rather
than
from
their
trade
promotion
bud-
get.
Across
its
stores
in
the
U.S.,
Walmart
TV
was
projected
to
capture
130
million
viewers
monthly
(Hays
2005).
The
ads
initially
included
audio,
which
inadvertently
led
to
employee
fatigue,
shopper
irritation,
and
lower
ad
effectiveness.
The
Walmart
Smart
Network
replaced
the
aging
Walmart
TV
network
and
debuted
in
2008.
It
was
touted
as
a
“next
gen-
eration
retail
media
network
that
is
supported
by
a
flexible,
open
enterprise
platform
powered
by
Internet
Protocol
Televi-
sion
(IPTV)—technology
that
will
allow
the
retailer
to
monitor
and
control
more
than
27,000
screens
in
more
than
2,700
stores
across
the
country”
(Sharma
2008).
The
new
Walmart
Smart
Network
deploys
response
measurement
and
message
optimiza-
tion
technologies
that
enable
delivery
of
relevant
content
to
shoppers
and
that
can
be
varied
by
store,
by
screen,
and
by
time-of-day.
Present
Day
Retail
Technology—Already
Here
and
On
the
Horizon
Mobile
Apps
It
seems
that
almost
every
retailer
these
days
has
a
mobile
app.
However,
the
capabilities
of
these
apps
vary
quite
a
bit
across
retailers.
Some
have
relatively
limited
options
such
as
a
store
finder,
ability
to
download
coupons,
or
view
the
weekly
circular
electronically,
while
others
offer
an
omnichannel
experience.
For
example,
Target’s
app
offers
shoppers
the
ability
to
scan
products
to
see
if
there
are
special
offers,
receive
mobile
coupons
as
they
move
through
the
store,
download
a
store
map
to
find
products
within
each
Target
store,
and
buy
items
online
through
the
app.
A
recent
study
of
mobile
app
users
by
mobile
consulting
firm
Applause
sifted
through
user
reviews
for
retailer
mobile
apps
(Gray
2015).
Among
apps
that
received
the
highest
app
quality
scores,
the
common
themes
were
the
capability
to
see
deals,
easily
switch
between
PC
and
mobile,
and
receive
in-store
alerts.
A
recent
study
performed
by
Forrester
(2015)
reinforced
the
sense
that
U.S.
consumers
are
using
smartphones
to
shop
in
an
anytime,
anywhere
fashion
and
retailers
are
striving
to
engage
with
shoppers
at
these
critical
touch
points.
However,
retailers
are
struggling
with
convincing
shoppers
that
they
need
more
retailer
apps
as
consumers
strive
to
pare
back
on
the
apps
they
have
on
their
phone.
Consumers
would
prefer
more
integration
in
a
retailing
app;
that
is,
they
would
like
a
single
app
via
which
they
can
search
across
retailers
and
buy
whenever
the
need
arises.
Self
Scanning
Self-scanning
checkout,
also
called
“self-checkout,”
is
an
automated
process
that
enables
shoppers
to
scan,
bag,
and
pay
for
their
purchases
without
the
need
for
a
cashier.
In
most
stores,
a
self-scanning
checkout
lane
looks
like
a
traditional
checkout
lane
except
that
the
shopper
interacts
with
a
computer’s
user
interface
instead
of
a
store
employee.
Once
the
shopper
begins
the
checkout
process,
the
self-scan
interface
guides
the
shopper
through
the
process
of
scanning
each
item
and
where
to
place
it
after
it
has
been
scanned.
The
patent
(see
Fig.
2)
for
an
‘auto-
mated
point-of-sale
machine’
was
awarded
in
1992
(Schneider
1992)
and
the
first
self-checkout
system
was
installed
in
1992
by
Price
Chopper
Supermarkets.
When
the
shopper
scans
an
item,
the
item’s
barcode
provides
the
computer
with
the
information
it
needs
to
determine
what
item
is
being
scanned,
as
well
as
the
item’s
weight
and
current
price.
To
mitigate
pilfering,
when
the
computer’s
animated
voice
directs
the
shopper
to
place
the
scanned
item
in
a
shopping
bag,
the
item
is
also
being
placed
on
a
security
scale
that
compares
the
weight
against
the
last
scanned
item.
This
prevents
shoppers
from
scanning
one
item
and
placing
two
into
the
bag.
Typically,
there
is
a
cashier
supervisor
for
every
four
to
six
self-scanning
stations.
In
a
global
study
(NCR
2014)
of
2,800
shoppers
con-
ducted
across
nine
countries,
90%
of
respondents
reported
that
they
use
self-checkout.
Respondents
report
that
they
like
the
convenience
of
self-checkout,
find
it
simple
to
use,
and
think
it
is
faster
than
a
cashier
assisted
lane.
QueVision
Kroger
has
embraced
the
use
of
technology
to
reduce
shopper
wait
time
at
checkout,
rolling
out
a
new
system
called
QueVi-
sion
across
its
2,400
grocery
stores
in
2010.
The
system
couples
infrared
sensors
that
count
shoppers
with
analytics
based
on
“Little’s
Law”
(Little
1961)
to
quickly
open
more
checkout
lanes
when
shopper
waiting
time
has
exceeded
a
preset
thresh-
old.
Little’s
Law
from
queueing
theory,
represented
in
the
simple
equation
L
=
*w,
asserts
that
L,
the
average
number
of
shoppers
in
a
queueing
system,
is
equal
to
,
the
rate
at
which
shoppers
arrive
and
enter
the
system,
times
w,
the
average
trip
time
of
a
shopper.
QueVision
reportedly
has
reduced
the
average
shopper
wait
time
at
checkout
from
more
than
four
minutes
to
less
than
30
s
(McLaughlin
2014).
Since
over
7
million
shoppers
visit
Kroger
each
day,
this
reduction
in
wait
time
equates
to
over
400,000
h
of
saved
time
each
day.
Shopper
satisfaction
with
the
speed
of
checkout
has
increased
by
42%
and
revenue
has
increased
as
well,
which
Kroger
attributes
to
shoppers
allocating
less
time
to
waiting
in
line
and
more
time
to
shopping.
QueVision
has
also
helped
Kroger
free
up
parking
space
at
urban
stores
where
parking
is
limited
(Coolidge
2013).
There
are
several
other
companies
that
offer
similar
sensors
to
count
store
traffic.
For
example,
ShopperTrak
(Chicago)
and
Traf-Sys
(Pittsburgh)
claim
a
large
installed
base
of
counters.
The
traffic
counts
are
combined
with
POS
data
to
help
retailers
track
conversion
rates
(the
proportion
of
people
entering
the
store
that
actually
make
a
purchase),
assess
the
effectiveness
of
out-of-store
advertising
on
traffic,
and
optimize
their
staffing
to
ensure
coverage
during
peak
times.
Smart
Shelves
Retailers
have
recently
been
experimenting
with
“smart
shelves”
that
offer
the
promise
to
reduce
out-of-stocks
through
J.J.
Inman,
H.
Nikolova
/
Journal
of
Retailing
93
(1,
2017)
7–28
11
Fig.
2.
Self-checkout
sketch
from
original
patent
5,083,638.
weight
sensors
on
the
shelves.
A
weight-sensitive
mat
is
placed
on
the
shelf
and
a
notification
is
sent
to
store
personnel
when
the
last
item
is
removed.
If
reserve
stock
is
on
hand,
this
system
mitigates
lost
sales
and
shopper
irritation
from
out-of-stocks.
The
shelves
also
include
beacon-activated
mobile
advertis-
ing.
A
beacon
works
via
Bluetooth
technology
and
beacon
communications
consist
of
small
packets
of
data.
Beacons
trans-
mit
packets
of
data
at
set
intervals
to
be
accepted
by
shoppers’
smartphones,
where
they
can
be
used
to
trigger
marketing
mes-
sages
such
as
push
messages
and
app
actions.
When
one
of
the
apps
receives
a
beacon
broadcast,
it
communicates
the
data
to
its
server,
which
triggers
an
action
such
as
a
promotion,
a
targeted
advertisement,
or
even
a
helpful
reminder
(e.g.,
Got
Milk?).
The
premise
is
that
beacons
will
help
increase
the
connection
between
retailers
and
shoppers
and
improve
the
shopping
expe-
rience
because
the
proximity-based
communication
and
at-shelf
advertising
allow
retailers
to
ensure
that
shoppers
only
receive
relevant
information
and
discounts.
Finally,
many
smart
shelf
systems
incorporate
digital
price
tags,
which
allow
retailers
to
change
prices
remotely.
The
poten-
tial
benefits
of
digital
price
tags
are
twofold.
First,
the
potential
labor
savings
are
substantial,
since
many
retailers
sell
thousands
of
SKUs
and
changing
paper
price
tags
requires
many
hours
of
employee
time.
At
present,
new
pricing
is
sent
to
stores
on
a
weekly
basis,
with
about
twenty
percent
of
products
needing
to
be
updated
per
week.
This
is
typically
done
manually;
store
associates
go
through
the
store
and
replace
the
price
tags
on
the
affected
items.
Second,
digital
price
tags
allow
retailers
to
change
prices
dynamically.
For
example,
a
grocery
store
could
slowly
lower
the
price
on
its
baked
goods
throughout
the
day
in
order
to
avoid
having
excess
inventory
at
the
end
of
the
day.
Leveraging
this
capability
requires
analytics
that
enable
the
retailer
to
estimate
demand
on
an
hourly
basis,
coupled
with
real-time
inventory
information.
Shoppers
and
retailers
will
both
benefit
from
such
a
use;
shoppers
who
shop
later
in
the
day
would
pay
less
and
the
retailer
will
avoid
spoilage.
However,
digital
price
tags
also
offer
the
potential
for
retailers
to
engage
in
“surge
pricing,”
raising
prices
to
extract
greater
sur-
plus
from
shoppers
with
a
higher
willingness
to
pay.
Using
POS
data,
a
retailer
could
estimate
the
variation
in
shopper
price
elas-
ticity
for
product
categories
across
time
of
year,
day
of
week,
and
even
time
of
day
and
“optimize”
the
price
to
maximize
revenues.
For
example,
a
retailer
could
increase
the
price
of
its
prepared
foods
at
dinner
time
when
shoppers
are
in
a
hurry
and
are
will-
ing
to
pay
a
higher
price
for
the
convenience
of
purchasing
a
heat-and-serve
meal
solution.
Clearly,
such
an
opportunity
will
be
quite
attractive
to
retailers,
probably
less
so
to
those
shoppers
who
have
to
pay
a
higher
price.
Gravity
Feed
Shelving
Systems
Gravity
feed
shelving
systems
such
as
that
introduced
by
Campbell
Soup
in
2002
has
revolutionized
the
canned
soup
cat-
egory.
Campbell’s
gravity
feed
system
is
installed
in
over
20,000
stores.
A
gravity
feed
shelving
system
consists
of
a
sloped
shelf
for
supporting
merchandise
and
a
front
wall
that
hold
the
mer-
chandise
in
place
until
a
shopper
takes
the
front
product.
When
the
front
item
is
removed,
gravity
pushes
the
remaining
merchan-
dise
down
the
sloped
shelf
to
the
front.
This
system
automatically
maintains
the
appearance
of
the
shelf,
requires
less
attention
from
store
personnel
and
lowers
labor
costs.
Shoppers
find
such
displays
easier
to
navigate
and
more
pleasant
to
shop.
Accord-
ing
to
McCormick
and
Company,
the
gravity
feed
fixtures
for
its
spices
increased
sales
in
installed
retailers
by
over
five
percent
and
cut
labor
costs
in
half
(Karolefski
2008).
Personalized
Promotions/Pricing
Pioneered
by
dunnhumby
at
Tesco
and
Kroger,
retailers
are
using
datamining
of
their
loyalty
card
data
with
the
help
of
consulting
firms
such
as
EYC,
Catalina,
and
Aimia
to
identify
12
J.J.
Inman,
H.
Nikolova
/
Journal
of
Retailing
93
(1,
2017)
7–28
their
best
customers
and
develop
offers
that
increase
retention.
A
focal
objective
in
this
domain
is
“relevance”—offering
pro-
motions
on
products
that
are
of
interest
to
the
shopper
and
address
their
specific
needs.
For
example,
Catalina
Market-
ing
provides
personalized
coupons
for
retailers
and
brands
by
tracking
the
behavior
of
more
than
230
million
U.S.
shoppers
monthly.
Kroger
sends
out
over
12
million
mailers
to
their
best
customers
each
quarter.
Catalina
Marketing
claims
that
every
$1
in
promotional
offers
generates
$8
in
extra
sales
(Kharif
2013).
Taking
the
idea
of
personalized
promotions
one
step
further,
retailers
are
now
experimenting
with
“proximity
marketing”
dur-
ing
the
shopping
trip.
Coupling
smart
phone
technology
and
loyalty
card
data,
retailers
are
attempting
to
reach
shoppers
with
personalized
offers
in
real
time.
The
platform
collects
con-
tinuous
feedback
during
each
shopper’s
trip
and
uses
sensor
readings
from
shoppers’
smartphones
to
calculate
position
and
movement.
This
is
coupled
with
loyalty
data
to
deliver
relevant
messages
and
offers
at
key
moments.
Messages
can
be
triggered
based
on
a
shopper’s
location,
movement,
and
dwell
time.
For
example,
a
shopper
dwelling
in
the
ice-cream
section
could
be
sent
an
ice-cream
coupon.
Alternatively,
the
platform
can
pro-
vide
coupons
that
urge
the
shopper
to
visit
more
of
the
store
to
spur
unplanned
purchases
(Hui
et
al.
2013).
Finally,
shopper
movement
data
can
be
used
to
gauge
shopper
momentum
in
the
store
to
prevent
shoppers
having
to
backtrack.
Scan
and
Go
Several
retailers
have
begun
to
test
or
introduce
technology
that
allows
shoppers
to
use
their
smartphone
to
scan
items
as
they
put
them
in
their
basket.
Shoppers
can
then
use
the
scanned
data
via
the
retailer’s
app
to
pay
without
having
to
scan
the
items
again
at
the
checkout
line.
The
technology
offers
the
poten-
tial
for
improved
shopper
satisfaction
from
the
convenience
and
reduced
wait
time,
along
with
labor
savings
to
the
retailer
(anal-
ogous
to
self-checkout
discussed
above).
However,
retailers
are
still
struggling
with
leveraging
this
value.
Walmart
tested
this
technology
in
200
of
its
stores
in
2013–2014
and
found
that
shoppers
had
difficulty
in
learning
how
to
use
the
app.
The
per-
ceived
complexity
of
using
an
app
may
explain
the
result
that
41%
of
consumers
prefer
the
retailer
to
provide
them
a
device
to
use
while
shopping
(CFI
Group
2014).
In-Store
CRM
In
the
film
Minority
Report,
Tom
Cruise’s
character
is
bom-
barded
by
ads
as
he
walks
through
a
mall.
In
the
film,
shoppers
are
recognized
by
a
retinal
scan
of
their
eyes,
which
is
then
used
to
serve
up
personalized
content.
The
ads
mention
him
by
name,
implying
that
they
are
targeted
specifically
to
him.
While
this
type
of
technology
has
been
commonplace
for
online
retailers
for
years
(e.g.,
Amazon
customizes
the
products
that
people
see
based
on
their
previous
purchases
or
items
they
have
put
in
their
cart),
this
rarely
happens
in
the
physical
world.
When
shoppers
enter
a
store,
the
company
does
not
know
their
identity.
This,
too,
is
beginning
to
change.
Retailers
are
beginning
to
experiment
with
facial
recognition
software
that
will
help
them
identify
shoppers
unobtrusively.
Furthermore,
companies
like
RetailNext
are
able
to
recognize
customers
based
on
their
smart-
phones
and
track
key
metrics
such
as
shopping
path
and
dwell
time.
Pushing
the
envelope
further,
Realeyes,
which
analyzes
facial
cues
to
monitor
response
to
video
advertising,
can
poten-
tially
track
shoppers’
emotions
as
they
shop
the
store.
Powered
Analytics,
a
start-up
in
Pittsburgh
that
was
recently
acquired
by
Target,
focuses
on
personalizing
in-store
shopping
through
mobile
technology,
location
data,
and
predictive
analytics.
The
goal
is
to
bring
an
online
shopping
experience
into
physical
stores.
Finally,
in
2013,
Nordstrom
began
testing
technology
that
allowed
it
to
track
customers’
movements
by
following
the
Wi-
Fi
signals
from
their
smartphones.
However,
when
Nordstrom
posted
a
sign
telling
shoppers
that
it
was
tracking
their
behav-
ior,
shopper
reaction
was
so
negative
that
Nordstrom
ended
the
experiment.
Shopper-Facing
Retail
Technology
Adoption
Decision
Framework
When
considering
adoption
of
new
shopper-facing
tech-
nology,
sophisticated
retailers
consider
the
profit
implications.
That
is,
the
retailer
evaluates
whether
the
benefits
of
the
tech-
nology
will
outweigh
the
costs
of
purchase,
installation,
and
maintenance.
These
benefits
tend
to
result
from
the
technol-
ogy
increasing
revenues,
decreasing
costs,
or
both.
As
shown
in
Fig.
3,
revenue
increases
and
cost
decreases
can
derive
from
var-
ious
sources.
Revenues
can
be
increased
by
extracting
greater
consumer
surplus
(e.g.,
charging
higher
prices
to
shoppers
who
are
willing
to
pay
more),
increasing
the
amount
purchased
at
the
retailer
by
current
shoppers,
attracting
new
shoppers
to
the
retailer,
and
increasing
payments
from
suppliers,
while
costs
can
be
decreased
by
offloading
labor
to
shoppers
(e.g.,
self-scan)
or
by
automating
processes
(e.g.,
digital
shelves).
Each
of
these
is
discussed
in
more
detail
below.
Extracting
Surplus
Consumer
surplus
is
the
net
gain
that
a
buyer
receives
from
purchasing
a
good
or
service.
Specifically,
it
is
the
difference
between
what
the
buyer
would
have
been
willing
to
pay
and
the
actual
price.
In
terms
of
the
demand
curve,
it
is
the
area
below
the
demand
curve,
above
the
price,
and
left
of
the
quantity
bought
(set
at
the
market
clearing
price
where
supply
equals
demand).
For
example,
the
buyer
might
be
willing
to
purchase
a
certain
brand
of
coffee
maker
for
$50.
If
the
retailer’s
price
is
$39,
then
the
buyer
enjoys
a
consumer
surplus
of
$11.
It
is
tempting
for
retailers
to
seek
ways
to
extract
this
surplus
by
charging
differential
prices
based
on
shoppers’
differences
in
willingness
to
pay,
analogous
to
revenue
management
in
the
airline
industry.
Technology
offers
this
promise.
For
example,
if
the
retailer
can
identify
the
shopper
via
NFC
or
facial
recognition
technology,
the
retailer
can
offer
mobile
coupons
that
are
tailored
to
the
shopper’s
price
sensitivity.
Alternatively,
digital
shelves
provide
the
opportunity
for
analytics
to
estimate
how
shoppers’
price
sensitivity
varies
throughout
the
day
and
raise
price
at
times
of
day
or
days
of
the
week
when
price
sensitivity
for
the
product
category
is
lower.
For
example,
analysis
might
reveal
that
prices
for
prepared
food
could
be
dynamically
increased
J.J.
Inman,
H.
Nikolova
/
Journal
of
Retailing
93
(1,
2017)
7–28
13
Fig.
3.
Current
decision
calculus.
at
lunchtime,
or
RFID
monitoring
of
stock
levels
might
lead
to
retailers
increasing
price
as
stock
levels
decline
in
order
to
leverage
scarcity
effects.
Increasing
Quantity
Purchased
by
Current
Buyers
Most
shoppers
satisfy
their
household
requirements
for
goods
across
several
retailers.
That
is,
they
engage
in
channel
blurring
in
most
product
categories
(e.g.,
Luchs,
Inman,
and
Shankar
2016).
A
potential
benefit
of
new
technology
is
encouraging
shoppers
to
(a)
purchase
a
greater
share
of
their
requirements
for
any
given
category
from
that
retailer,
and
(b)
purchase
cate-
gories
from
the
focal
retailer
that
they
currently
do
not
purchase
at
all
from
that
retailer
but
from
other
retailers.
This
implies
that
retailers
can
profile
their
shoppers
using
geodemographic
purchase
patterns
and
compare
the
potential
purchases
against
shoppers’
loyalty
card
data
to
identify
opportunities
to
increase
share
of
wallet.
Attracting
New
or
Lapsed
Buyers
One
of
the
largest
potential
sources
of
increased
revenues
is
new
buyers.
These
shoppers
have
yet
to
form
an
impression
of
the
retailer
or
to
establish
buying
habits.
Unfortunately,
this
is
arguably
the
most
difficult
opportunity
to
leverage,
since
retail-
ers
are
presently
unable
to
identify
new
buyers
as
they
enter
the
store.
However,
lapsed
buyers
can
potentially
be
targeted
via
e-mail
with
“we
haven’t
seen
you
in
a
while”
coupons
or
incentives
to
persuade
them
to
return
to
the
store.
Increasing
Payments
from
Suppliers
As
retail
technologies
improve
retailers’
ability
to
target
shoppers
more
accurately,
the
value
of
this
capability
to
suppliers
increases.
This
allows
retailers
to
extract
more
funds
from
sup-
pliers
who
wish
to
have
access
to
targeted
buyers.
For
example,
a
retailer
with
scan
and
go
technology
that
has
a
large
shopper
base
will
be
potentially
attractive
to
suppliers
who
wish
to
con-
tact
shoppers
at
the
point
of
purchase
and
offer
an
incentive
to
purchase
their
brand.
Alternatively,
a
retailer
with
a
shopping
app
that
enables
shoppers
to
use
their
purchase
history
to
create
a
shopping
list
could
offer
suppliers
the
opportunity
to
bid
for
the
right
to
have
their
brand
listed
earlier
in
the
list
of
prior
purchases.
For
exam-
ple,
Costco
could
add
an
option
to
its
app
or
website
where
shoppers
could
access
their
shopping
history
in
creating
a
shop-
ping
list.
Suppliers
such
as
Pepsi
or
Unilever
could
be
offered
the
opportunity
to
bid
for
the
option
of
having
their
products
receive
more
prominent
display
such
as
being
listed
at
the
top
of
the
list
or
in
bold
font.
Suppliers
could
also
bid
for
the
option
of
being
listed
as
a
substitute
for
a
competitor
brand.
Some
shopping
apps
now
offer
the
utility
to
shoppers
to
create
a
shopping
list.
The
shopper
uses
the
list
to
guide
her
purchases,
then
this
data
can
be
linked
to
the
shopper’s
loyalty
account.
This
enables
the
retailer
to
assess
which
items
tend
to
be
planned
in
advance
(i.e.,
was
included
on
the
shopping
list)
versus
which
items
tend
to
be
chosen
at
the
point
of
pur-
chase
(unplanned
purchases).
Suppliers
whose
brands
tend
to
be
purchased
on
an
unplanned
basis
are
likely
more
susceptible
to
disruption
by
competitors’
promotions
than
brands
that
are
planned
in
advance.
Thus,
retailers
can
potentially
offer
suppli-
ers
the
opportunity
to
utilize
this
information
to
send
coupons
to
shoppers
who
tend
to
purchase
items
on
an
unplanned
basis.
Suppliers
would
be
willing
to
pay
a
great
amount
for
such
a
targeted
opportunity.
Offload
Labor
to
Shoppers
Retailers
occasionally
offload
labor
to
shoppers
which