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RETAIL
HOW ARTIFICIAL
INTELLIGENCE IS
REVOLUTIONIZING
DEMAND PLANNING
SPONSORED BY
PRESENTED BY
RIS RETAIL
MATURITY LADDER
Maturity Ladder:
Machine Learning
The RIS News Retail IQ Report Maturity Ladder is
a diagnostic measurement tool for a retailer’s state
of technology advancement in a specific category.
There are four key phases: 1. Basic – minimal
capabilities, 2. Intermediate – mostly basic with
some advanced capabilities, 3. Advanced – mostly
advanced capabilities with some limitations, and
4. State-of-the-Art – comprehensive capabilities
are fully integrated and up to date. Note that it is
possible to be on more than one step of the ladder
simultaneously as specific technology components
and processes are upgraded in phases.
4. STATE-OF-THE-ART
• Retailers leverage technology to help acquire customers whose profiles
match those of lucrative, existing ones. This involves combining
customer profiling with inbound marketing, predictive analysis and
targeting demographics.
• Implementation of “propensity modeling,” which calculates consumers’
proclivity to purchase through lead scoring. This figures out how much
money the shopper can potentially spend and how likely they are to
convert or unsubscribe.
• Machine learning is used to power image recognition tools. On cosmetics
websites in particular, this allows customers to “try on” makeup, test
colors and formulations and better match products.
3. ADVANCED
• Online and offline retailers implement demand forecasting systems that involve machine
learning. They can now link assortment, space, price and fulfillment into a single plan, factoring
in time of year, weather and information on competitive products, sell through, customer traffic
and demographics.
• Demand forecasting generates more precise, granular forecasts than time series approaches. This
results in more accurate inventory levels in stores, online and in warehouses. In-stock positioning
improves, markdowns decrease, and there is greater ROI.
• Management of planned, regular, promotional and markdown pricing becomes tighter. Retailers
can test different promotional price points under a variety of scenarios. They can also identify
items whose movement is not heavily impacted by price changes.
• Machine learning system factors in supply constraints and can generate order proposals every 24
hours. Machine learning can help determine which supplier(s) to use under certain circumstances.
2. INTERMEDIATE
• Following the lead of Amazon, e-commerce retailers use machine learning to further enhance and personalize product
search and recommendations.
• Retailers can model shoppers’ activities and affinities in real time. Suggested items are based on consumer history or other
shoppers’ purchasing activities.
• Retailers can identify e-commerce navigation patterns, learning the customer’s purpose and intent and analyze product
attributes to personalize what is displayed.
• Online shoppers find what they want faster. With their attention spans undiminished, they are less likely to grow weary of
searching. As a result, there are fewer abandoned shopping carts.
1. BASIC
• Store level and e-commerce retailers use single algorithm, time series forecasting, which relies largely on historical data to predict future demand.
• With the time series concept, demand is analyzed for a single product, SKU, category, demographic market or channel. The strategy can only
process and cross-reference a finite number of demand factors, minimizing opportunities for comparison and accurate prediction and forecasting.
• Retailers must manually cleanse and separate time series data, making the forecasting process longer, costlier and more labor intensive.
• Stock levels do not always match consumer demand, leading to out of stocks or overstocks, which erode margins and profits.
• Fresh food retailers faced with forecasting limitations are often left with high levels of waste in fresh categories. Hard and soft goods retailers must
endure markdowns or wholesale unwanted merchandise.
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
In his book, “March of the Machines: The Breakthrough in Artificial Intelligence,” Kevin Warwick envisions future scenarios in which artificial intelligence (AI) can replace humans. Warwick, a British engineer and deputy
vice-chancellor (research) at Coventry University in the U.K., is also famous
for conducting cyborg experiments using his own nervous system.
For many retailers, Warwick’s predictions have become reality — sans
$31.1
BILLION
Global spending on
cognitive systems by
2019, with a five-year
compound annual
growth rate of 55%.
Source: International Data Corp., “New IDC Spending
Guide”
bionic men. Rather, chains and e-commerce operators are using machine
learning to better identity what shoppers want, when and where they
want it, in what quantity, and at what price.
Machine learning is a 21st century technology that grew out of the
centuries-old AI concept. Artificial intelligence involves the ability of machines to carry out “smart” tasks. Machine learning (also referred to
by the IBM-coined term “cognitive computing”) takes this automation
concept further. Machines automatically analyze large amounts of data
and “learn” using rule-based algorithms that identify patterns and trends
Machine learning involves little “guess work.” It marks a huge change
from the 1990s when retailers, particularly grocers, based assortment
decisions on Nielsen and IRI data, internal sales data and vendor sales
feedback. Insights were narrow and resources did not specifically measure store-level or chain-wide data.
Today, at both the e-commerce and store levels, machine learning is
being used in demand forecasting and related functions like demand
pricing and markdown optimizations. Many e-commerce companies also
use machine learning in product search and recommendations, applications that continues to grow more sophisticated.
Machine Learning Comes of Age
Machine learning first became a scientific discipline in the late 1990s.
But it was not until the 2000s that the advent of cloud computing and
the ability to apply mathematical calculations to big data over and over
and faster and faster prompted more companies to embrace it.
It is predicted that by 2019 global spending on cognitive systems
will be close to $31.3 billion, with a five-year compound annual growth
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
rate of 55%, according to International Data Corp.’s “New IDC Spending
Guide.” Across business sectors, banking spends the most on cognitive
systems worldwide at 20%, largely for fraud analysis, followed by retail
and healthcare. Combined spending of the latter two is expected to reach
more than $10 billion by 2019. Globally, the U.S. and Canada are the
largest users of cognitive systems; their spending represents 80% of the
worldwide total, followed by Europe, the Middle East and Africa. In addition, the report identified machine learning as one of the top six innovation accelerators that will drive digital transformation.
It is no
longer sufficient
to achieve
forecast accuracy at the
aggregate level
unless it is
accompanied
by the anticipated point of
sale and corresponding sales
quantities.
Source: Retail Industry Leaders Association, “State
of the Supply Chain”
Best-in-class companies, including retailers, are 43% more likely to
use automated practices like machine learning to analyze data across
multiple systems and provide recommendations based on resulting insights, according to Aberdeen Group’s “CEM Executives Agenda 2016:
Aligning the Business Around the Customer” report.
Buoying E-Commerce Capabilities
Most machine learning applications fall into three main categories: enhancing products or services, automating processes, and uncovering insights that can inform operational and strategic decisions. Retailers use
the technology in all three ways.
To date, much activity in machine learning has been in e-commerce,
particularly in search analysis, product recommendations, promotions,
and consumer sentiments.
Amazon is regarded as a pioneer in these areas. According to one
industry executive, 25% of Amazon’s sales come through recommendations based on products viewed and previous purchases. Today, Amazon
is even marketing its easy-to-use, highly scalable search and other machine learning technologies to outside companies.
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 these functions more comprehensive
and specific. User choices and information can be cross-referenced in
a myriad of ways. Shoppers find products more quickly, more items are
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RETAIL
RONALD MENICH
Chief Data Scientist, Infor
There are many
different patterns to be
found in the manner
in which customers
purchase product, and
machine learning can
find those.
Infor Retail is changing the way
people work and the way they
think about work, by crafting a
new generation of enterprise-level user experiences that disrupt
preconceived notions of retail
software. Our passionate team of
developers, designers, scientists,
and visionaries are reimagining
the retail experience across every channel — through beautiful
design, engaging experiences,
the unprecedented power of science, smart data, and predictive
analytics. This is retail the way
it should be. Visit our website:
www.infor.com/retail.
INSIGHTS
The New Age
of Machine Learning
Q: Briefly describe machine learning. What can/can’t it do?
RONALD MENICH: Machine learning is a field of computation in which a
program can learn patterns from data that were not explicitly programmed.
Machine learning can be used to forecast demand for retailers, estimate the
impact of promotions, pricing, and display changes and more. Additionally,
machine learning is used to recommend products to a customer to purchase
based on the ensemble of customers and products purchased historically.
Machine learning is used in Google Translate. Machine learning is used to
categorize/tag images on the Internet. Machine learning can even recommend
optimal actions. An example of this is the Google DeepMind-developed machine
learning-driven game player AlphaGo, which made headlines last year for being
the first machine to beat the best human at the game of Go, a game that is
significantly more challenging than chess. The applications of machine learning
grow by the day.
Machine learning has typically been applied to a single task such as labeling
images for which many examples are available. General-purpose intelligent
machines which can perform multiple tasks and transition learnings from one
problem domain to another — this is still very much in its infancy.
Q: Machine learning has been discussed since the 1990s. But up until a
few years ago, there were roadblocks. What were those challenges and
how were they overcome or circumvented?
MENICH: From the start of the Internet era, product recommenders appeared
(e.g., Amazon’s initial book recommender, Netflix’s movie recommender)
and became standard during the first decade of this century. Search engine
technology, based on machine learning, also continued to evolve during that
first decade. So there was steady progress in machine learning from the 1990s
onwards.
Now, it is true that one particular type of machine learning — neural networks
— was used in the 1990s and experienced some technical roadblocks such as
vanishing gradients. In the last eight years or so, deep learning — the new
revision of neural networks — has bypassed these technical roadblocks and
deep learning networks are regularly beating earlier benchmarks for accuracy.
The means and ways by which deep learning circumvented vanishing gradients
— via the use of convolutional networks, long-, short-term memory, and related
approaches — are quite involved topics.
The rise of cloud computing technology has enabled the processing of very
large data sets through machine learning algorithms in an affordable manner.
The next computer processing increment above general cloud computing is
the use of GPUs on the cloud (GPUs are a faster type of processor than the
more common CPU, and GPUs are well-suited to certain machine learning
computations).
Continued on page 6
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INSIGHTS
The New Age of Machine Learning
Continued from page 5
By bringing more
and more of the causal
factors as input to
machine learning, the
amount of user review
required to get good
forecasts can be vastly
reduced.
RONALD MENICH
Chief Data Scientist, Infor
Q: Retail is one of the key industries where machine learning is being
aggressively deployed. What makes retail such a good fit?
MENICH: Retail offers big data input to machine learning, many millions or even
billions of examples for a machine learning training set. For example, one of the
retailers we serve has 8,300 stores in which 150,000 SKUs are actively sold yearround (with many hundreds of thousands of additional seasonal SKUs entering and
exiting the assortment at various times), and a typical training set has over three
years’ worth of historical data by week or by day. When we join historical sales data
with a variety of potential demand drivers or causal factors — e.g., price, promo,
product, display, store, and temporal features — then that becomes big data. There
are many different patterns to be found in the manner in which customers purchase
product, and machine learning can find those.
Q: Which retail challenges has machine learning most frequently addressed?
MENICH: Historically, product recommenders were a primary application of
machine learning to online retailing. More recently, we see the application of machine
learning to demand forecasting and promotional and pricing impact assessments.
Q: Can you provide any real-world examples of how retailers have used
the still emerging technology to supercharge their demand forecasting
efforts?
MENICH: We take historical sales data and input attributes/causal factors/demanddrivers such as product attribute and hierarchy data, product description text, store
attribute and hierarchy data, price, promotion, display, and even weather data to
enable machine learning to understand how consumer demand changes as a function
of various features. One of our customers uses this machine learning technology to
forecast demand for a variety of hard goods. Another uses it to forecast demand for
apparel. Another uses it for forecasting grocery items. The applications are limitless.
Q: What are the key benefits of machine learning in the brick-and-mortar
environment?
MENICH: Most older/traditional demand forecasting algorithms have either no or
limited means by which to incorporate the impact of causal factors and/or demanddrivers. Because of this, users often have to spend significant amounts of time
interjecting their retail experience to correct the forecasts. By bringing more and
more of the causal factors as input to machine learning, the amount of user review
required to produce good forecasts can be vastly reduced.
Q: The machine learning/artificial intelligence field is evolving at breakneck
speed. What capabilities do you believe will be introduced in the next few
years that will elevate the technology to even greater heights?
MENICH: It is quite feasible that we will see a rise in the number of intelligent
agents which interact with a customer much like a store associate would, assisting
the customer to make purchases, recommending products consistent with the
consumer’s historical buying patterns and those of other consumers, bots which
will make optimal pricing, and promotional decisions which leave the consumer
delighted with their purchase and which enable the retailer to remain profitable.
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
sold per transaction and there are fewer abandoned carts.
Icebreaker, a New Zealand-based seller of Merino wool performance
garments, is using a cloud-based product intelligence engine that models consumer activities and affinities in real time. The site will show
three related items based on shopper history or other customers’ additional purchases under the “you may like” header. It also displays three
garments that complement the original under “designed to go with.”
With each click, the engine becomes smarter.
The technology has increased cross-sells and upsells on full-price
Best in class companies,
including retailers, are
43%
more likely to use
machine learning
to analyze data
across multiple
systems and provide
recommendations based
on resulting insights.
Source: Aberdeen Group, “CEM Executives Agenda
2016: Aligning the Business Around the Customer”
merchandise. Icebreaker, which operates stores and websites in the
U.S., New Zealand and elsewhere, generated 28% more revenue from
recommendations. The average order increased 11%. “Personalization
has become key to purchase decisions,” noted Brian Hoven, global head
of e-commerce.
Young adult apparel company Pacific Sunwear also implemented a
platform that has improved personalized site search, navigation and
product results along with how PacSun shows up in organic search engine results. If a consumer is on a landing page offering 12 items, software identifies navigation patterns, learning his/her purpose and intent.
It then analyzes product attributes to personalize what is displayed.
If a shopper clicks on a men’s denim jacket when searching jackets, additional men’s jackets are depicted — but not a women’s motorcycle jacket,
as would happen on some sites. PacSun has thousands of online products
and two million pages on its website, making the tool particularly valuable.
“Before, we didn’t have the capability — similar to a Google search
— to offer predictive suggestions,” said Nathan Liu, vice president of
e-commerce. “Now, when a shopper types ‘dresses,’ it leads them down
a path that identifies intent.”
On the big box side, Walmart.com developed Polaris, a search engine
that uses text analysis, machine learning and synonym mining. Purchases have increased 10% to 15%, according to medium.com’s, “How 10
Innovative Companies are Using Big Data Effectively.” Another proprietary product, Social Genome, automatically sorts through millions of
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
tweets, social media messages, blog postings, YouTube videos and other
elements to predict purchasing intent.
Improve Inventory Levels and ROI
with Demand Forecasting
The newest frontier in machine learning involves demand forecasting and
related functions. Introduced three years ago, demand forecasting that
uses machine learning can link assortment, space, price and fulfillment
into a single plan, factoring in time of year, weather and information on
competitive products, sell through, customer traffic and demographics.
Demand forecasting allows online and offline retailers to generate more
precise forecasts than traditional time series approaches. This yields
28%
Increase in revenue from
recommendations New
Zealand apparel retailer
Icebreaker enjoyed
after implemented a
cloud-based intelligence
system. Average order
increased 11%.
more accurate inventory levels in stores, online and warehouses. In-stock
positioning improves, there are fewer markdowns and better ROI’s.
The time series strategy, in contrast, employs just a handful of demand
factors (e.g., trend, seasonality and cycle) and is restricted to demand
history. Demand is analyzed only for a certain product, SKU, category,
demographic market or channel. The process uses single dimension algorithms, each of which analyzes demand based on data-limited constraints. Hence, data must be manually cleansed and separated, making
the process longer and costlier.
Machine learning-based demand forecasting combines learning algorithms with big data and cloud computing to analyze thousands — even
millions — of products using unlimited factors simultaneously across a
retailer’s business. Machine learning-based forecasting determines what
is significant, then prioritizes consumer insights (demand sensing). It
can forecast demand for all items, including promotional ones. According to suppliers of the software, demand forecasting can improve forecasting accuracy by up to 50%.
In addition to better in-stock positioning, key results of using machine
learning-based demand forecasting include:
• Alignment of item-level tactics with category and department-level
sales plans.
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
• Tighter management of planned, regular, promotional and markdown
pricing.
• Better collaboration with consumer products vendors.
• Real-time visibility, which allows better alignment of location with
assortment, category, space and price plans.
Amazon has become a master of inventory management across the
80%
Of spending on cognitive
systems globally occurs
in the US and Canada.
150 fulfillment and warehouse facilities it operates worldwide. By using machine learning to forecast demand, it is able to meet changing
inventory needs by location, product and other criteria. Changes can be
caused by anything from late shipment arrivals to hostile weather.
Better and tighter inventory control is a major concern among cutting-edge
retailers, stated the Retail Industry Leaders Association’s (RILA’s) annual
“State of the Retail Supply Chain” report. The report queried about 40
major regional and national chains, including Walmart, Walgreens, Dollar
General and Home Depot.
Source: Aberdeen Group, “CEM Executives Agenda
2016: Aligning the Business Around the Customer”
“It is no longer sufficient to achieve forecast accuracy at the aggregate level unless it is accompanied by the anticipated point of sale and
corresponding sales quantities,” said the RILA retail consensus.
Sixty-three percent of retailers said their ability to forecast store
demand is excellent. But on the e-commerce end, the satisfaction rate
dropped to half at 31%. When asked about specific challenges, 76% of
retailers said they have trouble achieving forecast accuracy goals, 55%
face challenges in peak period demand forecasting, and 53% have difficulties with promotional demand forecasting.
Managing Fresh Food Demand
Demand forecasting is particularly valuable in fresh food. In apparel
and hardgoods, unsold items may be heavily discounted or sold to rack
jobbers for cents on the dollar to minimize loss. But unsold perishable
food must be disposed.
Fresh food demand is impacted by everything from the economy and
changing demographics to holiday celebrations, and pop culture. When
a celebrity chef discusses the merits of a little known ingredient, for
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
example, sales go nuts.
Weather also plays a role. Some weather trends are obvious, like
spikes in bottled water and soft drinks during summer and better performance of soup and hot cocoa during winter. But other peaks are largely
detected by technology. Walmart, for one, found that Strawberry Pop-Tart
sales increase sevenfold before a hurricane, according to the New York
Times article “What Walmart Knows About Customers’ Habits.” Hence,
it further benefitted by placing Pop-Tarts near checkouts before a storm.
Fresh products represent up to 40% of grocers’ revenue and one third
of the cost of goods sold, according to McKinsey & Co.’s report “The
Secret to Smarter Fresh Food Replenishment? Machine Learning.” It is
63%
the most competitive segment in supermarkets since consumers often
choose a store based on the quality, assortment and price of peripheral
fresh offerings.
Retailers who said their
ability to forecast store
demand is excellent.
For e-commerce
satisfaction dropped to
31%
.
In recent years, fresh food has become more complicated and SKU
intensive due to the growth and influence of ethnic shoppers on mainstream food habits (chorizo, avocados and siracha, to name a few items),
the focus on year-round availability of all produce, and the rise of freshintensive store formats like Mariano’s in Chicago, Whole Foods and Harris Teeter.
Deli, bakery and prepared food sales have also grown as consumers
cook at home less frequently. Supermarket fresh prepared foods grew
by an annual rate of 10.4% between 2006 and 2014, making it one of
the highest performing segments in the food industry, according to the
Food Marketing Institute (FMI)’s report, “10 Findings on Fresh Food in
Source: Retail Industry Leaders Association, “State
of the Supply Chain”
Grocery Stores.”
In fresh food, retailers bounce from supplier to supplier and back
to ensure product availability. For example, 75% of US grapefruits are
grown in Florida; if a storm hurts the crop, a grocer may combine suppliers in Texas, Arizona and California to meet demand.
There is also the issue of waste. According to the USDA’s website, waste
at the retail level averages 8.5% for produce, 11% in dairy, 5% in meat/
fish/poultry and 7% for eggs. While some waste is unavoidable — e.g., egg
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
breakage or refrigeration failure — much stems from poor planning.
With conditions and local demand fluctuating daily, planners must manually enter price changes and promotions into replenishment systems. Processes are time consuming and error prone. Hence, there is a good reason
why produce managers are stores’ highest paid department heads.
A machine learning system can factor in supply constraints and generate order proposals every 24 hours, said the McKinsey Fresh Food Report. Each proposal optimizes product availability while minimizing risk
of waste and markdowns. It can calculate how price changes can impact
demand in a category where price elasticity can change daily. With machine learning, out-of-stocks can be reduced by as much as 80%, write-
Retail
Tribulations:
off by 10%, and gross margin can increase significantly said the report.
• 76% have trouble
achieving forecast
accuracy goals
One of the biggest grocery applications in machine learning is being
• 55% face challenges
in peak period demand
forecasting
• 53% have difficulties
with promotional
demand forecasting
Source: Retail Industry Leaders Association, “State of
the Supply Chain”
The Retail Beneficiaries
implemented by Texas-based Whole Foods. The company is revamping
90% of its core technology systems. Inventory management has been
challenging for Whole Foods, which uses many niche suppliers for shelfstable and fresh offerings. Its goal is to save $300 million, support future
growth and pave a faster route to market.
The plan includes replacing a home grown disparate ERP system involving different components for 12 geographic regions. The new cloudbased system will improve hosting of information, ordering and receiving.
It will add replenishment, allocation and forecasting capabilities. CIO
Jason Buechel called it the “most transformational project” and one that
is “changing how the company does business.”
He added, “Most things we do in 12 different ways and in Excel. We’re
moving from 12 systems to one common solution, leveraging machine
learning and big data. It gets us away from batch jobs, things that are
very 1990s architecture.”
Machine learning has many applications outside of food. Home Depot stocks countless SKUs. It also micro markets — an area with old
Victorian homes, for example, may stock ceiling medallions and mosaic
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
bathroom tile; a store in a flood prone area may have a wide assortment
of sump pumps.
Home Depot wanted to better manage assortments by store clusters
and demographics and improve in-season planning. With machine learning, it processes data quickly, creates consumer-centric assortments and
10%
TO
14%
optimizes sales and margins. “Now, we seamlessly evaluate assortment
change at the national and local levels,” said Steve Huth, vice president, merchandising. “The flexibility enabled integration with internal
systems, providing merchants with tools to evaluate and execute decisions quickly.”
Machines Continue to Grow Even Smarter
Moving forward, machine learning algorithms are expected to continue
to become more powerful and to provide more automation of retail processes. Newer functions, some of which are already in use, include automatic language translation, image recognition and even better product
searches and recommendations.
Existing smartphone voice recognition applications like OK Google
Increase in purchases
Walmart.com
experienced following
its development
of a search engine
that uses text analysis,
machine learning and
synonym mining.
and Siri could be enhanced to not only understand user input, but realize
context. These type of applications could also involve GPS and camera
data. On the language end, Microsoft is conducting a Skype trial involving real-time spoken translations that would allow users to understand
people speaking multiple languages.
The ability to acquire customers whose profiles match those of lucrative, existing ones is another focus. This involves mixing customer
profiling with inbound marketing, predictive analysis and targeting demographics. In e-commerce, a “clustering algorithm” could segment
parts of an audience exhibiting particular behaviors. And “propensity
modeling” — the epitome of demand forecasting — can gauge shoppers’
likelihood to purchase through lead scoring. This determines how much
money the shopper can potentially spend and how likely they are to convert or unsubscribe.
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HOW ARTIFICIAL INTELLIGENCE IS REVOLUTIONIZING DEMAND PLANNING
Conclusion
Machine learning has the unique ability to “cut through the clutter.”
On the demand forecasting end, the “clutter” involves an ever-growing
abundance of products for retailers to sort through, make sense of, and
adapt to particular environments. On the e-commerce search and recommendation fronts, retailers face an environment in which consumers are
being bombarded with more merchandise, marketing messages and “Big
Brother” tactics than ever. Online, retailers have about 90 seconds to
grab shoppers’ attention before they tune out. Winning retailers will be
Average Fresh
Food Waste in
Grocery Stores:
those that can meet customers’ needs as quickly and in the most pinpointed, noninvasive way possible.
18.5%
PRODUCE
17%
EGGS
15%
MEAT/FISH/
POULTRY
11%
DAIRY
Source: U.S. Department of Agriculture
13
RIS INFOGRAPHIC
MACHINE LEARNING
80%
Of spending on cognitive
systems globally occurs
in the US and Canada.
Source: Aberdeen Group, “CEM Executives Agenda
2016: Aligning the Business Around the Customer”
63%
Retailers who said their
ability to forecast store
demand is excellent.
For e-commerce
satisfaction dropped to
31%
Source: Retail Industry Leaders Association, “State
of the Supply Chain”
28% $31.1
Increase in revenue from
recommendations New
Zealand apparel retailer
Icebreaker enjoyed
after implemented a
cloud-based intelligence
system. Average order
increased 11%.
Best in class companies,
including retailers, are
43%
more likely to use
machine learning
to analyze data
across multiple
systems and provide
recommendations based
on resulting insights.
BILLION
Global spending on
cognitive systems by
2019, with a five-year
compound annual
growth rate of 55%.
Source: International Data Corp., “New IDC Spending
Guide”
Average Fresh
Food Waste in
Grocery Stores:
TO
14%
Increase in purchases
Walmart.com experienced
following its development
of a search engine that
uses text analysis,
machine learning and
synonym mining.
Retail
Tribulations:
• 76% have trouble achieving forecast accuracy goals
• 55% face challenges
in peak period demand
forecasting
• 53% have difficulties
with promotional demand
forecasting
Source: Retail Industry Leaders Association, “State of
the Supply Chain”
18.5%
PRODUCE
17%
EGGS
15%
MEAT/FISH/
POULTRY
11%
DAIRY
Source: Aberdeen Group, “CEM Executives Agenda
2016: Aligning the Business Around the Customer”
10%
Source: U.S. Department of Agriculture
It is no longer
sufficient to achieve
forecast accuracy at
the aggregate level
unless it is accompanied by the anticipated point of sale and
corresponding sales
quantities.
Source: Retail Industry Leaders Association, “State of the
Supply Chain”