The document discusses how artificial intelligence is increasingly being used in the retail industry to address challenges posed by a fragmented marketplace with diverse consumer needs. AI allows retailers to gather customer insights and predict behaviors through automated analysis of large datasets. Key applications of AI discussed include personalized marketing, trade promotions management, supply chain management, assortment planning, and demand forecasting. The use of AI is expected to grow significantly in the retail industry in coming years to improve business performance and customer experience.

1. 1
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. This is
particularly true in grocery, where heavy hitters
like Amazon and Walmart continue to eat into
the market share of traditional chains.
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
ethnic 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.
Artificial
Intelligence
Opportunities in Retail
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2. 2
Retailers and their suppliers need real-time,
in-depth knowledge to attract diverse shoppers.
But the advent of distinct devices, sensors, and
machine-to-machine communications has made
data sets so large that timely manipulation,
management and analysis present significant
logistical challenges for companies using on-
hand data management tools or traditional
data processing applications. Other entities
have incorrect or outdated legacy data. This
is compounded by the difficulties many
companies face in recruiting the talent needed
to implement complex technology tools, analyze
the data and make effective recommendations.
Many successful high volume retailers and
consumer packaged 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.
AI allows retailers and manufacturers to gather
customer insights in an automated fashion and
predict next actions based on previous patterns
or images. AI uses predictive patterns to help
understand desires, motivations and actions
across both physical and digital channels. This
lets retailers and suppliers enhance many
functions, such as executing more targeted and
personalized marketing campaigns and improve
trade promotion efforts. AI can also automate
forecasting of inventory needs, more accurately
predict out-of-stock incidences and ultimately
help optimize supply chains.
Types of Artificial Intelligence
With artificial intelligence (AI), machines mimic or replace intelligent human behavior, like problem
solving or learning. They “sense,” “comprehend” and “act” in accordance with the real world. In essence,
machines learn from experience and make recommendations, learning and improving over time.
AI applications fall under three key areas
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.
Robotics. Involves full-scale automation of tasks traditionally performed by humans.
Warehouse picking and packing, for example, can be performed by robots.
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.

3. 3
1
PWC 2017 data
2
Statista
3
Deloitte’s 2016 Global CIO survey
4
PWC 2017 data
AI’s Growth
Retail and consumer goods
are among the top five
industries in which AI is
being applied1
.
In 2017, the global AI
market was estimated at
$2.4 billion. It is expected to
grow at a CAGR of 50% to
over $59 billion by 20252
.
64% of CIOs plan to invest
significantly in cognitive or
AI technologies over the
next two years3
.
By 2030, AI will drive
Global GDP gains of
$15.7 trillion (14% higher)
through productivity
and personalization
improvements4
.
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. Today, Amazon is even
marketing its easy-to-use, highly scalable search
and other machine learning technologies to
outside parties.
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.
User choices and information can be cross-
referenced in numerous ways. Customers can
locate merchandise faster, more products
are sold per transaction and there are fewer
abandoned carts.
Now, retailers and suppliers are applying AI to
areas outside e-commerce. Demand forecasting
that incorporates machine learning, for
example, allows online and offline retailers to
generate more precise forecasts than traditional
time series approaches. Machine learning also
facilitates warehouse management by helping to
alleviate the over- and understocking scenarios
that can erode a retailer’s bottom line. When
applied to trade promotions management,
AI could help suppliers improve timing,
tracking and other aspects of retail marketing
investments.
NLP is also making inroads by providing
conversational answers in areas including
category management through deep analytics,
data mining and visualization at department,
planogram and product levels.

4. 4
Top AI Applications in Retail
AI is gaining an important place in retail with
growth expected to reach $40 billion by 2025,
up from an estimated $6.46 billion today. It is
being driven by an increase in customer-centric
initiatives, more social media advertising and
heightened demand for virtual assistants5
.
Among retailers, 16% already use some form
of AI, while 20% plan to add it over the next
12 months; another 18% hope to implement it
more than a year from now6
. Following are the
leading AI, machine learning and NLP application
areas in retail.
AI in Personalized Marketing
Retailers typically use marketing automation
software or campaign management solutions
to target customers. Applied to CRM data, tools
divide customers into groups according to
shopping behavior, demographics, preferences
or other criteria.
The problem is that rules are chosen based on
the marketer’s human assumptions and leave
significant room for error. The process also
leaves out potentially useful criteria, and it can
be hard to segment customers who do not
correspond to pre-designated buckets. Since
information is historic, shopping behavior,
income and other factors are prone to change.
Machine learning examines a full set of data,
identifies patterns and organizes it into
“clusters” of similar data. Assumptions and
stereotypes about what is important are
bypassed. Rather, information is determined
by the analysis. Trends and connections are
established that might have been overlooked
by analyzing individual pieces of data
simultaneously, and information can be used to
send highly personalized offers to customers.
But retailers believe personalization still
has a way to go, even though 39% say it is
extremely important7
. Most retailers (54%)
gave themselves a low rating for executing
personalization strategies at an omnichannel
level, with just 4% rating themselves as high
overall. The biggest hurdle, said 69%, is lack of
appropriate technologies, followed by managing
across channels (47%).
Analyzing store point-of-sale and e-commerce
transaction history became the standard for
classifying and targeting consumer groups.
Now, 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.
5
ReportsnReports
6
mediapost.com, October 9, 2017
7
RIS News, “Closing Big Gaps in Personalization,” October 2017
8
Harvard Business Review
Usage of AI in personalized marketing
Personalization can
grow revenue 5% to
15% and increase
efficiency of marketing
spending by up to 30%8
.
Social sentiment analysis
35%
25%
Elastic personalized search
28%
18%
Source: 2018 EIQ Retail Innovation Survey
Plan to use in next
12 to 24 months
Currently use

5. 5
Improve promotion precision with NLP
AI in Trade Promotions Management
In promoting products, CPG companies have
historically made substantial investments with
retailers to boost revenue and/or increase
market share. Today, trade spending represents
more than 15% of CPG companies’ total
revenue9
and continues to grow. Consequently,
spending volume has increased dramatically,
and trade promotions have become more
complex and harder to manage.
What is more, it currently takes the average
business user four weeks to understand if a
trade promotion was effective10
. Yet 72% of
promotions fail to break even11
, and many new
products fail.
According to the Trade Promotions Management
Association’s website, the industry has been
reluctant to adopt new technologies. Roughly
60% of companies still use manual processes
and spreadsheet applications or proprietary
software. Thus they lack real-time, accurate and
meaningful insights for planning, managing and
optimizing trade promotions.
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.
Usage of AI in trade promotions management
9
Trade Promotions
Management
Association, 2017
10
Consumer Goods Technology,
“Tech Trends 2017: Redefining
Trade Promotion”
11
Nielsen price/
promotion
survey, 2017
NLP also recognizes natural, written language.
Using sentiment analysis, it can determine
whether consumer reactions to products are
positive, negative or neutral. Given the hordes
of posts consumers make daily on social
media, blogs, e-commerce sites
and other platforms (including CPG
companies’ own social media sites),
manufacturers have a wealth of
information to draw from.
By layering on NLP, consumer goods
companies can facilitate experimentation
with trade promotions criteria. They can
verbally ask, for example, how results
could differ if a promotion is run in
July versus August. The answer is
immediate and does not require
using a PC or running massive
calculations. And they avoid
expensive risks.
MarketsandMarkets.com predicts
the NLP market will roughly double,
reaching $16 billion by 2021 at a
CAGR of 16.1%12
.
Given the time and money invested in Research
Development, this feedback can go a long
way when it comes to trade funds marketing,
determining actionable price points,
tweaking items and recognizing
market voids that could be filled
by new products. And it can yield
results faster and at a lower cost.
12
“Natural Language Processing Market by Type
Technologies by Deployment Type, Vertical
by Region - Global Forecast to 2021,” July 2017
Source: 2018 EIQ Retail Innovation Survey
Plan to use in next
12 to 24 months
Currently use
Promotion optimizationPricing optimization
27%
37%
32%
30%

6. 6
AI in the Supply Chain
Machine learning has an important place in
the supply chain, particularly when it comes to
demand forecasting. With traditional planning
methods, demand forecasts are not always
accurate. This leads to out of stocks, overstocks
and products being returned to vendors. It also
creates unhappy customers and makes retailers
unable to attain financial goals.
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.
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.
Issues stemming from inventory management
or, more broadly, supply chain planning can
have a profound effect on logistics operations.
For example, a retail company that is developing
an omnichannel strategy struggles to manage
the complex trade-offs between demand
forecasting, inventory orders, channel allocation
and logistics costs and capacities as it tries to
respond to customer demand. Retailers may
also be facing a multitude of challenges related
to warehouse and DC management. In order
to ensure successful execution, companies
need to take an end-to-end view of the supply
chain, managing the relevant trade-offs and
synchronizing planning and logistics to drive
value. Adoption of machine learning in mapping
varied demand patterns and scenarios for more
effective inventory optimization and channel
allocation could become an important step in
timely replenishment and efficient logistics.
Source: 2018 EIQ Retail Innovation Survey
Plan to use in next
12 to 24 months
Currently use
Replenishment
24%
41%
Inventory planningDemand forecasting
22%
52%
Usage of AI in supply chain management
31%
33%

7. 7
AI in Assortment Planning
Historically, planning a retail assortment
involved looking at the previous year’s sales data
to see what performed well and what did not,
then factoring in new fads and trends to come
up with the right mix. The result would be a
combination of brand new items that followed
the latest fad, a few timeless perennials and
some of last year’s mix for those customers who
were not quite ready for a change.
The problem is, the consumer population and
its tastes and habits are a constantly moving
target. The teens who shopped this retailer last
year may have gone off to college. Or, if the
target was working professionals, changes in the
job market may have impacted their spending
habits. Whatever the reason, historic data talks
about yesterday’s customers.
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 brands, then comparing those
products to the demographics and shopping
history of that retailer’s customers—in real-
time. 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.
13
Psychological Science study,
August 21, 2017
15
Deloitte14
Fan Fuel Digital
Marketing Group
This is particularly valuable for retailers. They
can determine how the same or similar items
are performing elsewhere, give them a ranking
and decide if they want to order them, how
many to order, how long to feature certain
items, what stores to offer them in and other
criteria.
As a measurement tool, online reviews are
particularly valuable. Unlike a focus group
or other study, they voluntarily come from
individuals who have actually purchased
a product. And consumers take them very
seriously:
If two similar products
have the same rating,
shoppers will purchase the
one with more reviews13
.
97% of shoppers say
reviews influence their
buying decisions; 92%
hesitate to buy anything
if no customer has
reviewed it14
.
73% of shoppers say
written reviews impress
them more than star or
number ratings15
.
Source: 2018 EIQ Retail Innovation Survey
Plan to use in next
12 to 24 months
Currently use
Customer/consumer insightsMerchandise management
21%
47%
26%
46%
Usage of AI in assortment planning

8. 8
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decision platforms, solutions and customer-
centric 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
EnsembleIQ is a premier business
intelligence resource that exists to help
people and their organizations succeed.
We empower retailers, consumer goods
manufacturers, technology vendors,
marketing agencies and a vast ecosystem of
service providers by leveraging an integrated
network of media and information resources
that inform, connect and provide actionable
marketplace intelligence.
Conclusion
AI is still in its infancy. By 2020, however, 85% of
customer interactions will be managed by AI16
.
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 to grow, customers
become even smarter and more demanding and
AI applications like visual search and NLP digital
assistants become more widely understood and
applied.
In some other areas mentioned in this report,
AI has a long way to go in terms of uniform
and consistent adoption. But with the cost
of bringing products to market and the high
failure rates, retail and CPG companies in
particular have much to gain by applying the
technology to trade promotions management
and personalized marketing. AI is also gaining
ground in assortment planning, supply chain
management and product development where
an endless loop of forecasting continually
adjusts inventory levels. This alleviates
inconsistent inventory buys, overstocking,
understocking and consequent margin erosion.
It also creates happy, loyal customers who keep
returning due to more relevant assortments and
new products, thus ultimately driving overall
shopper satisfaction.
16
Gartner