Retail Technology as a field is heating up very much today. Here is a short note on the landscape and the pros and cons of the segments within the landscape.

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Retail
Technologies,
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The
Retail
Tech
Ecosystem

2. 2
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Compe55ve
Landscape
All
product
companies
that
are
in
the
space
that
Shopsense
is
in
ConsumerFacingRetailerFacing
Interactivity Data Analytics

3. 3
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Compe55ve
Landscape
All
product
companies
that
are
in
the
space
that
Shopsense
is
in
ConsumerFacingRetailerFacing
Interactivity Data Analytics
Ini5al
Euphoria;
Small
Sales
Cycles;
Customized
Offering
Communica5on
with
the
consumer;
Highly
commodi5zed;
Not
too
differen5ated
Only
observes
behavior;
Highly
commodi5zed;
Intense
compe55on;
Do
not
control
the
source
of
data

4. 4
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Consumer
Facing
-­‐
Interac5vity
Broadly
breaks
into
three
segments
that
provide
an
engaging
experience
to
the
shopper
Interactive Applications
High
Level
of
Customiza3on
High
Level
of
Customiza3on
Capture
Consumer
Experience
Deeply
Engage
Consumer
Difficult
to
Produc3ze
Augmented Reality
High
Level
of
Customiza3on
Very
High
“Sexiness”
factor
Can
Deeply
Engage
Consumers
High
Cost
of
Content
Crea3on
“Sexiness”
appeal
lost
very
fast
Current
Experience
not
Pixel
Perfect
High
Cost
of
Development
Location Based Notifications
Need
to
Switch
on
Something
on
the
phone
Addi3onal
Source
of
Data
High
Relevance
Fundamentally
Mobile
High
Adver3sing
Poten3al
Can
get
irrita3ng
High
level
of
False
Posi3ves
Capturing the consumer experience and engagement creates high entry barriers
and makes the solution relevant to retailers and is habit-changing

5. 5
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Retailer
Facing
-­‐
Interac5vity
This
segment
is
driven
by
enhancements
to
exis5ng
solu5ons
that
are
sold
as
modules
on
exis5ng
retail
infrastructure
POS Based MIS
Very
High
Entry
Barriers
Highly
Produc3zable
Very
well
Integrated
Piggyback
on
POS
for
distribu3on
channel
Not
Differen3ated
This segment is driven by making existing systems efficient (payments), beautiful
(ERP) and modern (POS)
New Age Payment and POS Companies
Very
High
Entry
Barriers
Addi3onal
Source
of
Data
Controls
Consumer
Transac3ons
Easy
to
Upsell
other
products
&
services
High
Compliances
Intense
Compe33on
ERP Visualization
Very
High
Entry
Barriers
Highly
Produc3zable
Not
Differen3ated
Piggyback
on
ERP
for
distribu3on
channel
Intense
Compe33on

6. 6
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Consumer
Facing
-­‐
Data
Analy5cs
Led
by
Loyalty
Programs,
this
segment
is
where
personaliza5on
happens
to
consumers
in
the
stores
Loyalty Programs
Loosing
its
sheen
with
high
street
and
bridge
to
luxury
brands
Capturing
the
consumer
Perfect
Customer
Iden3fier
Segmentation and Campaign Management
Not
Differen3ated
BeSer
Targe3ng
Piggyback
on
Loyalty
programs
for
Distribu3on
Do
not
own
the
source
of
data
Easy
for
Loyalty
Programs
to
provide
Difficult
to
showcase
results
3me
over
3me
where
low
wallet
share
of
consumer
with
one
retailer
Clienteling Solutions
Not
as
Consumer
Engaging
Integrated
across
various
data
sources
High
Relevance
Complements
Sales
Associate
Needs
Integra3on
Across
Various
Data
systems
Personalization, Omnichannel Enablement and Consumer Connect drives the
maximum ROI for a retailer
High
entry
barriers
Not
Differen3ated

7. 7
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Retailer
Facing
-­‐
Data
Analy5cs
To
beBer
understand
consumers
and
op5mizes
stores,
products
and
staff,
Retailer
Facing
Data
Analy5cs
solu5ons
have
been
the
poster
child
of
retail
tech
for
the
beBer
part
of
the
previous
decade
Purchase Behavior - POS, Loyalty, Inventory
Data
captured
at
the
end
of
shopping
journey,
post-­‐hoc
analysis
Understand
Buying
Behavior
Piggyback
on
POS,
Loyalty
and
Inventory
for
Distribu3on
Browsing Behavior - Video, BLE, WIFI
Leakage
in
capturing
data
Understand
Behavior
that
leads
to
Buying
Own
the
source
of
data
Need
to
install
addi3onal
hardware
Not
totally
passive;
Need
to
get
consumers
to
download
app
or
opt
into
services
Important to own sources of data and integrate across all. Major drawback is that
these are just observers, not interceptors to consumers to influence purchase

8. 8
©
2014
Shopsense
Retail
Technologies.
Confiden5al
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circula5on.
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reserved.
Our
Opportunity
Targeted
&
Personalized
Offers
&
Discounts
Browsing
the
website
while
in
the
store
Price
Comparison,
Product
Reviews
Virtual
Dressing
Rooms,
Sharing
via
Email/Whatsapp
SoLoMo
Based
Apps
Point
of
Sale,
Payment,
Loyalty
Value lies with one who controls consumer experience whilst owning and inventing
new sources of data in the store that understand shopping
- Drive Shopper Engagement and Enhance Experience
- Invent and own new sources of Data in the Retail Environment
- Understand Shopper Behavior to Sell More and Sell Better

9. 9
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Problem
Landscape
Data Devices
Context
Deployment across devices that invent and
own new sources of data in the store

10. 10
©
2014
Shopsense
Retail
Technologies.
Confiden5al
document,
not
for
circula5on.
All
rights
reserved.
Crea5ng
and
Understanding
Consumer
Ac5vity
Streams
1980s 1990s 2000s Today
Consumer
Ac3vity
Across
Touch-­‐points
Transac3ons
in
Store
&
Online
Browsing
Point-­‐of-­‐Sale
Data
in
Modern
Trade
stores
Proprietor
of
Mom-­‐&-­‐
Pop
Store
“Software will not replace all offline retail. Instead it will be used to transform
certain retail experiences”- Reid Hoffman

11. Mumbai
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