Today’s retail industry is far more
fragmented and competitive than
ever. Multiple store formats and an
arsenal of digital tools are making
shoppers more educated about
choices.
Digital channels also
continue growing.
The landscape has also become more
diverse, with a variety of household types and
lifestyles having very different needs than the
mom-dad-with- kids target that dominated
generations past. This is compounded by
a
burgeoning
e t h n i c
population,
with
each
group having a
distinct
profile
in every area
from language
and
food
to
shopping style
and economic
status.
Add
to
this
revitalized inner
cities, which are
attracting young
Millennials
in
droves,
and
the result is a
seismic melting
pot that never
stands still.
Retailers
and
their
suppliers
need
real-
time,
in-depth
knowledge
to
attract diverse
shoppers.
And
with this, many
successful high
volume retailers
and consumer
p a c k a g e d
goods
(CPG)
organizations
have
turned
to artificial intelligence (AI) to navigate the
muddle. At the simplest level, AI machines
or systems imitate human behavior in
intelligent ways that can augment productivity
and optimize business performance. AI
applications include machine learning, natural
language processing (NLP) and robotics.
Machine learning first became a scientific
discipline in the late 1990s. But it did not
seriously take off until the 2000s. Growth was
fueled by access to huge amounts of real time
Big Data and the emergence of algorithms
that make sense of that data for productive
output. AI is continuing to grow, touching
more industries and functions every day.
To date, much AI retail activity has revolved
around machine learning in e-commerce,
particularly for search analysis, product
recommendations, promotions and analyzing
consumer sentiments. Amazon is regarded
as a pioneer here, and it is widely estimated
that 25% of its sales are generated through
recommendation-based product views and
previous purchases. Other e-commerce
companies
have
used
search
and
recommendation tools for some time. But in
recent years, e-commerce has reached new
heights by using machine learning to make
functions more comprehensive and specific.
Top AI Applications in Retail Personalized
Marketing
Advances in Big Data and AI are giving rise
to highly personalized campaigns and other
initiatives without major human intervention.
These engagement tools factor in customer
purchase history, browsing behavior, social
media activity and
overall
channel
engagement.
The
biggest difference is
that today’s initiatives
target people on an
individualized
basis,
and with AI, retailers
can do this at scale.
Trade
Promotions
Management
AI and analytics can
provide
promotion-
related insights and
guidance to channel
managers, category/
brand managers and
financial teams to
help allocate trade
fund dollars more
wisely and alleviate
margin erosion.
Supply Chain
Machine
learning
helps
forecast
inventory,
demand
and supply in that
predictions are not
based
solely
on
historic data. Rather,
the
technology
predicts what will sell, driving enhanced
forecasts based on real-time data using
demographics, weather, performance of
similar items and even online reviews and
social media. Predictions can be made by
store, SKU, size, color and other criteria.
brands, then comparing those products to the
demographics and shopping history of that
retailer’s customers—in realtime.
Some tools can even predict the ebb and
flow for each particular product over the next
30 days, including demand changes by both
percentage and item count.
Machine learning can also be used to
“read” customer reviews on social media
or e-commerce sites. A machine learning
algorithm can be taught to categorize posts or
look for text patterns, and AI can even detect
foul language and fraudulent reviews.
AI is still in its infancy. By 2020, however, 85%
of customer interactions will be managed by
AI. Thanks to Amazon and other cutting-edge
retailers, AI has already made major inroads
in e-commerce, particularly when it comes to
more pinpointed product recommendations.
This online personalization trend will only
intensify as e-commerce continues growing,
customers become even smarter and more
demanding and AI applications like visual
search and NLP digital assistants become
more widely understood and applied.
Types of Artificial Intelligence
Machine learning. Machines automatically
analyze large amounts of data and “learn” using
rule-based algorithms that identify patterns
and trends. As an example, this could mean
combining 100,000+ data points from 75 million
customers regarding shopping patterns and
other habits.
Natural language processing (NLP).
NLP is a machine’s ability to understand, analyze
and generate human speech. A computer listens
to a natural language spoken (or written) by a
person, understands its meaning and responds
by generating natural language to communicate
back (as opposed to a computer language like
Java or SQL). NLP can allow retailers to request
detailed information about a specific store,
product, shipping method or other topic without
touching a PC.
Robotics. Involves full-scale automation
of tasks traditionally performed by humans.
Warehouse picking and packing, for example,
can be performed by robots.
Machine learning even helps identify and
correct data errors and risks in the supply
chain, elevates insights from the Internet of
Things devices in the field and plans logistics.
This optimizes delivery of merchandise while
balancing supply and demand, making
human analysis unnecessary.
Assortment Planning
AI-influenced algorithms can predict the
most relevant items to add to a retailer’s
inventory
by
analyzing
the
product
assortments of competing retailers and
Symphony RetailAI is the leading global provider of Artifi
cial Intelligence-enabled decision platforms, solutions and
customercentric insights that drive validated growth for
retailers and CPG manufacturers, from customer intelligence
to personalized marketing, and merchandising and category
management, to supply chain and retail operations.
More at www.symphonyretailai.com
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