Practical AI
For E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
b
Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
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Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Chapter 1: What is Practical AI? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Chapter 2: On NLP and Voice Commerce . . . . . . . . . . . . . . . . . . . . . . . . 17
Chapter 3: On Color Synonym Mapping . . . . . . . . . . . . . . . . . . . . . . . . . 33
Contents
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Gartner
Similar to greenwashing, in which companies exaggerate the environ-mental-friendliness of their products or practices for business benefit, many technology vendors are now ‘AI washing’ by applying the AI label a little too indiscriminately.
“
3
Introduction
The term “artificial intelligence” didn’t even crack the top 100
search terms on Gartner.com in January 2016. By May 2017,
it was the 7th most searched for term on the site.
These searches offer a glimpse into
the rapid rise of interest in artificial
intelligence. But peer behind the
curtain of this rise, and you’ll see a
cottage industry forming — especially
in the marketing technology space,
where vendors proudly wave the AI
flag without knowing what AI is while
hoping their waving will impress
potential clients who don’t know
what AI is either.
People are generally afraid to ask,
and potential decision-makers often
don’t want to appear uninformed
when standing before somebody so
apparently knowledgeable. So even
basic questions — such as How exactly
does your product use AI? How does
your company define the use of AI? Why
should I care about your company’s use
of AI? — all die on the vine.
“For today’s consumers, it’s not the technology itself
that’s most important; it’s the impact the technology
has on their lives.”
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
And the AI flag-bearer escapes
unscathed, once again.
As a company that received the
3rd highest score on CB Insights’
prestigious list of the top 100 AI
companies of 2018, we’ve been forced
to think about the intersection between
how we use AI to drive individualized
customer experiences through
our ecommerce platform and how
(or if) we want to enter into the fray
of conversations around AI.
Internally, we often come back to the
analogy of hybrid and electric cars.
Consumers who buy those cars may
have a basic understanding of how
the technologies work, but most tend
to focus on the benefits that come
with an investment in the technology,
notably spending less on gas while also
helping to save the environment.
For today’s consumers, it’s not the
technology itself that’s most important;
it’s the impact the technology has on
their lives. That’s why it’s frustrating
for us to see companies that tout
AI in the marketing of their products
and services rather than the experience
their AI offers.
90%of consumers around the globe are either
interested in AI or willing to try AI tools. And
63% have already been exposed to AI without
knowing it according to HubSpot Research.
D I D Y O U K N O W
5
It’s no wonder that companies are quick
to pitch their AI capabilities at trade
shows, in their marketing materials, and
in their sales pitches. But are they being
completely honest about the AI in their
products? After all, there are variants of
AI — such as machine learning and deep
learning — that provide varying results
and experiences. And that may lead to
blurred lines about what really qualifies
as artificial intelligence.
A machine learning system such as
IBM’s Watson, for example, utilizes the
information it has available to answer
questions and make decisions based
on probabilities. But it doesn’t have
the capabilities to remember and apply
an understanding of what may have
happened in the past.
This is fine if Watson goes against
humans in a Jeopardy showdown.
However, it takes a deep learning type
of AI to perform the tasks likely to
resonate the most with consumers.
This type of AI stores what it has
learned in the past, takes note of how
variables and results have changed
under different scenarios, and then
makes decisions based on that.
An ecommerce site, for example,
may utilize a chatbot that knows
what the product inventory looks like,
how to calculate shipping information,
and complete the sale — no human
cashier needed.
But, without a deeper learning, it can’t
recommend products that a specific
shopper might like based on previous
visits. Without an understanding
of what’s happened in the past,
the machine is unable to provide
an optimal customer experience.
“Retailers that don’t adapt and explore ways to give
customers what they want, when they want it, will
soon find themselves not just behind the AI curve but
behind the overall competitive curve.”
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
As Adweek stated last year “the point
of engagement and the point of
transaction are converging, meaning
brands that can offer immediacy,
personalization, authenticity
and accessibility will win out.”
When retailers don’t respond to these
changes in customer habits, they can
quickly fall behind their competitors.
Retailers that don’t adapt by exploring
ways to give customers what they
want, when they want it, will soon find
themselves not just behind the AI curve
but behind the overall competitive curve.
After all, it’s one thing to implement
an AI strategy into your business and
quite another to talk about it. Think
about the hybrid car analogy again.
Are consumers being subjected to
advertising that explains how those
cars work so that buyers can be better
informed? No.
Instead, consumers are fed information
on how much money they’ll save at
the pump or how they’ll emit fewer
emissions into the atmosphere. Some
are even happy that they can drive
alone in an HOV lane because of their
car’s technology.
80%of executives believe AI boosts
productivity, according to research
from Narrative Science.
D I D Y O U K N O W
7
Because these are the things that
matter most to consumers, it makes
little sense for a company to push
the AI marketing line unless it becomes
a selling point based on the experience
it provides.
For companies to say they’re utilizing
AI just for the sake of saying it, without
quantifying its effects in any way, seems
disingenuous.
When companies can say their AI
technology is not only smart enough
to respond in microseconds but
powerful enough to understand
consumers’ habits, history, and
tendencies based on past experiences
in the same amount of time and with
personalized results, then they can
credit their AI technology for delivering
a great experience.
Because in the end, the experience is
what consumers judge and what leads
to repeat business — and referrals.
In Practical AI for E-Commerce, we
address what AI is, offer a glimpse into
how it works, highlight a few concerns
to be aware of in an increasingly
AI-washed ecommerce solutions
marketplace, and show practical
examples of how AI can improve
the customer experience in ways
that impact critical ecommerce KPIs.
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Eliezer YudkowskyCo-founder of the Machine Learning Research Institute
By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.
“
9
What is Practical AI?
Practical AI is the valuable application of intelligence rendered
by machines. It’s rooted in present-day use cases, and it’s
separate from the futuristic promises and predictions of what
artificial intelligence may be able to accomplish.
The term artificial intelligence, coined in
1955 by Dartmouth math professor John
McCarthy, has undergone changes in its
scope since its initial use. As the field
of artificial intelligence has expanded,
two phenomena have played partic-
ularly important roles in our collective
understanding of what it means.
1. The emergence of the AI Effect.
This describes how as machines
have become more intelligent, small
accomplishments in the field drop off
the radar of what is considered AI.
In dismissing past achievements as
not really intelligent, the AI Effect has
led to artificial intelligence as a term
lending itself well to futuristic leanings,
including those of:
2. AI in popular culture.
Frequently the backdrop of post-apoc-
alyptic thrillers, artificial intelligence in
popular culture has painted prominent
pictures that aren’t grounded in
the reality of the day, including the
fear-based rise of the technological
singularity, where AI will lead to
runaway technological growth that
overtakes and even displaces humanity.
C H A P T E R 1
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Practical AI, then, grew as a result of
how the AI Effect and AI’s portrayal
in popular culture led to a muddled,
distorted purchasing and selling
environment where vendors of
AI-powered solutions felt the need
to lean into and leverage pop culture’s
use of the term, and customers found
it increasingly difficult to separate
the signal from the noise.
For consumers, this has led to a sense
of distrust and confusion because,
as Sol Rashidi, Chief Data and Cognitive
Officer at Royal Caribbean International,
put it, everybody wants to put their
technological solution “under the AI
umbrella just because there’s a bit more
glamor to it.”
So then what is artificial intelligence?
Well, it’s clear that many people have
questions about it. Here are a few
of those questions:
Source: Answer the Public
11
14%Fashion to Figure
experienced a 14%
increase in site-wide
conversion rate
when they used an
AI-powered e-commerce
personalization platform.
Preview Search
Home Page Product Merchandising
Product Display Page Product Merchandising
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Unfortunately, the hyperlaxity of the
term exists even in the field itself.
At Forrester’s CXSF 2017 event last year,
for example, a collective gasp emerged
from the audience when Pinterest’s
CTO Vanja Josifovsk stated that
artificial intelligence and machine
learning are essentially the same thing.
“I’ve been doing this for decades,” he
told the audience. “And I see them as
basically the same… just at different
points on a continuum.”
The somewhat-agreed-upon definition
of AI is that it is intelligence displayed
by machines.
But when terms such as machine
learning and deep learning are thrown
around and often used interchangeably
with artificial intelligence, it can
become difficult to gain a fundamental
understanding of one or the other.
Many writers of articles on both
subjects assume their readers already
have an in-depth knowledge of AI or
are content with their relatively abstract
understanding. And as mentioned
with AI-washing, most vendors aren’t
explaining the topics very well either.
The concept of Practical AI serves a
critical role in separating the relevant
from the simply interesting.
“The term artificial intelligence was coined in
1955 by Dartmouth math professor John McCarthy.”
13
Here’s how to think of the relationship between these terms:
As you’ve likely gathered by now,
heated debates exist about where
these terms intersect or separate.
The above image represents the
traditional view. And while beliefs
around this view continuously evolve,
it remains the foundational model used
by many universities and most speakers
on artificial intelligence.
In his piece at Forbes, Machine Learning:
The Evolution From An Artificial
Intelligence Subset To Its Own Domain,
David Thieras points out one of
the more significant changes:
“It took me a while to wrap my head
around it, given my earlier AI biases,
but I’ve concluded that machine
learning is now its own discipline,
intersecting with both AI and BI in
a very overlapped Venn Diagram.”
Artificial Intelligence
Machine Learning
Deep Learning
The broad grouping of techniques enabling machines to mimic aspects of human intelligence. Programs use if-then rules, decision trees, logic, and machine learning (including
deep learning) to predict, reason, adapt, and act.
Algorithms that improve on tasks through experiences and have the ability to learn
without continuous programming.
Machine learning subset that uses multilayered neural networks to learn from large amount of data.
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
So why is Practical AI important?
While the definition of Practical AI will
remain the same, its scope will shift
and evolve in similar ways to the terms
we’ve covered above. Its importance
rests on its immediacy and on its ability
to increase the public’s (including
consumers’ and vendors’) skill at differ-
entiating the signal from the noise.
In an article at Gigaom, Rudina Seseri,
founder of Glasswing Ventures and
Entrepreneur-In-Residence at Harvard
Business School, echoed words similar
to Sol Rashidi:
“AI has now become a buzzword.
Startups work AI into their pitches even
if their businesses aren’t really oriented
around the technology.”
Perhaps the best way to move beyond
buzzword is to see a few Practical
AI examples. AAccording to Erik
Brynjolfsson and Andrew McAfee
of MIT, the most practical real-world
applications of artificial intelligence
fit into two categories: perception
and cognition. Concerning perception,
voice commerce and image recognition
are prime examples.
While Alexa, Siri, and Google
Assistant have paved the way for
voice commerce, its democratization
is underway. Many online retailers,
for example, are powering their on-site
search functionality with technologies
such as Natural Language Processing
(which we’ll cover in the next chapter)
that can easily respond to longer, more
expressive search queries.
Beyond simply being a cool feature,
there’s value in the practicality of it.
A recent study at Stanford University
showed that speech recognition is
currently about three times faster than
typing on a cell phone. According to
ComScore, voice search will be 50%
of all searches by 2020. Customers
will find what they’re looking for more
quickly, which will lead to significant
wins for retail companies — if their
site is prepared to handle it.
In continuously learning all facets
of human speech, including which
words matter in a particular query,
AI is at the heart of voice commerce.
Similarly, image recognition is changing
entire industries.
It’s helping doctors improve the
accuracy and speed at which they
can detect cancer, and it’s opening
up new educational possibilities for
everybody, everywhere—you can now
snap a picture of a plant and instantly
gain information about it.
15
And as with voice commerce, digital
merchandisers are opening up search
functionality by allowing consumers
to search by photo.
Concerning cognition, AI examples
tend to grab headlines and are often
talked about for years to come —
such as IBM Watson defeating
Jeopardy champions and Google’s
AlphaGo defeating the Go master.
But there’s also the quieter, more
Practical AI manifestations:
technologies allowing insurance
companies to assess credit risk
and process claims more quickly;
and customer engagement platforms
that can predict with stunning accuracy
what you’ll want to purchase next,
and display that item in real-time.
Both examples improve operational
efficiency for all parties involved
and can lead to time-savings for
the customer and increased produc-
tivity for the provider.
Ultimately, Practical AI is about what’s
valuable right now. While peering into
the future is exciting and can be valuable,
it can also serve to distract us from
seeing the use cases already available.
And when the seemingly infinite
definitions of AI are all used with
equal weight, it can appear as though
a relatively simple chatbot CMS plugin
has the power to take over the world.
In this sense, Practical AI can help
establish an important baseline
that improves communications
in the consumer-vendor relationship.
Photo Search
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Science indicates that babies’ brains are the best learning machines ever created.
Dr. Patricia KuhCo-director of the Institute for Learning & Brain Sciences at the University of Washington
“
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On NLP and Voice Commerce
That quote from Dr. Kuh (adjacent) has made the rounds in
many parenting and life science magazines. The time has come
for it to be used in discussions on artificial intelligence.
All discussions around artificial
intelligence are at some level grounded
in this fundamental truth: we’re trying
to fuse what we know about our
own brains and what we know about
machines to replicate the immense
learning potential of a baby’s brain,
“the best learning machine ever created.”
Our founder and CEO Amar
Chokhawala was an early employee
at Google where he worked on using
AI to improve the Gmail user experience.
He thinks about AI in a similar way:
“Humans created AI by thinking about
how the human brain works. A baby’s
brain is the perfect example because
it is continuously learning patterns
and sequences through digesting
sensory input.”
C H A P T E R 2
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
So, because many of us are buying voice-enabled products, it seems more fitting
than ever to examine one particular aspect of Practical AI: the interplay between
Natural Language Processing (NLP) and voice commerce.
Before we dig into how NLP works, let’s level-set.
The top 6 bestsellers on Amazon are voice-enabled.
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What is Voice Commerce?
Voice Commerce is intelligent
voice-based search that powers
intuitive shopping experiences.
Okay, but what does that mean?
If you’ve ever asked Alexa to make
a purchase for you, or if you’ve ever
navigated to the search bar on your
favorite retailers’ site and searched by
voice instead of typing, you’ve engaged
in some element of voice commerce.
How we got to the point of digitally
shopping through voice is a result
of more technological advancements
than we can possibly cover here.
However, let’s consider the
simultaneous rise of three major forces
that bent consumer expectations and
behaviors toward voice-based digital
shopping experiences.
1. Google Hummingbird
In September 2013, Google announced
it had revamped its search engine
to focus not only on the keywords
searched for, but also on the implied
meaning of the entire search query.
This quite literally changed the game
for everybody who searches for
things on the web, and it continues
to set a benchmark for what searchers
(consumers among them) expect from
their search results — whether they’re
searching on Google, in an app,
or on a retailer’s site.
Regarded by SEO experts as Google’s
most significant search update since
2001, this shift to “semantic search,”
as it is known, means the displayed
results of a query now take into account
user intent, which is an incredibly
challenging concept for machines
to understand.
Humans can easily assess and respond
to the contextual relevance of a
sentence, but up until this point search
engine algorithms were primarily relying
on weighting keywords and learning
to determine content relevancy based
on what was clicked on the most.
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
But the user intent underlying a search for “pizza shops,” for example, was far more
likely to be something like this —
— rather than something like this:
In other words, while “pizza shops” is the subject, the users’ intent is far more likely
to be about finding the closest pizza shops so they can decide which is the best
one to order from.
Here’s a query I just conducted for “pizza shops”:
Thanks, Hummingbird.
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According to Cornell University,
the original Hummingbird relied
“on over 200 other ranking algorithms
and techniques, which include
algorithms that deal with semantic
analysis and natural language
processing on search queries.”
Before we explore that NLP portion,
let’s dive into the two other major
forces at play:
2. The Growth of eCommerce
Although the history of ecommerce
dates back over 40 years, Amazon
is largely credited with the industry’s
massive growth over the last few years.
As Business Insider reported, Amazon
alone accounted for 53% of all U.S.
online sales growth in 2016. Apartment
mailrooms everywhere are filled with
packages from goods ordered online,
and in my particular apartment complex
the vast majority of those packages
look something like this:
Amazon’s growth isn’t happening
simply because it offers a larger variety
of products than its competitors;
it’s happening because the company
offers an unrivaled customer experience,
on-site and off.
Through reducing the various consumer
friction points associated with digital
retail — from delivery and returns to
helping consumers wade through an
endless digital aisle — Amazon has
effectively made it easier for consumers
to buy online than to buy in a store.
Coming back to my own apartment
complex, it’s not only the mailroom
that’s filled with Amazon packages.
I’m now seeing AmazonFresh totes
(groceries that are delivered) outside
of many tenants’ doors even though
there are a few grocery stores within
walking distance.
Similar to how “Google it” has become
the unquestioned way of searching
for information, purchasing items on
Amazon has grown into the way for
digital consumers to easily purchase
certain goods.
And just as Google’s Hummingbird
continues to give rise to an empowered
searcher who expects search engine’s
everywhere to understand their
intent, Amazon’s growth, thanks to
the AI-powered experience they offer,
continues to give rise to an empowered
digital consumer who expects to easily
find, purchase, and receive goods.
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
3. The Rise of Voice Search
Some credit the rise of voice search with
the 2010 launch of Google Voice Search,
but two other obvious players have also
propelled voice search forward:
1. In October 2011, Apple introduced
a beta version of Siri in the iPhone 4S
2. In November 2014, Amazon
released Alexa.
All of this points to how voice search
adoption is skyrocketing. According
to AdWeek, the U.S. will have 67 million
voice-assisted devices in use by 2019.
With such widespread consumer
adoption rates, and all of the big
players investing heavily in continuing
the trend and capitalizing on it
(Amazon alone has 5,000 employees
working on Alexa), it’s easy to see
why many industry analysts consider
voice-powered digital shopping
the next frontier of ecommerce.
Unfortunately, the on-site search
experience offered by most retailers
is woefully behind.
Customers that are accustomed to
Google Voice Search, Siri, or Alexa
expect retailers to offer an intelligent
voice commerce experience, one that
incorporates elements from every
aspect we’ve covered so far.
Enter Natural Language Processing.
Because of the immense and rising
use case for retailers, NLP in voice
commerce is arguably the most
practical of all Practical AI examples.
But to understand it, we must circle
back to Hummingbird and the way the
majority of us still search today: typing.
Let’s level-set again: What is Natural
Language Processing?
50%of all online searches will be voice
searches by 2020, according to ComScore.
D I D Y O U K N O W
23
Natural language processing is a field
of computer science and a dimension
of artificial intelligence that studies and
develops the processes and interactions
between computers and human
(natural) language.
NLP technologies allow for accurate,
automated understandings of text
and speech.
The overarching goal is for machines
to understand the natural language
(typing or speaking) of humans. And
because we’re in the realm of artificial
intelligence here, the algorithms aren’t
static; they’re continuously learning
and improving.
As you can imagine, trying to get
a machine to understand natural human
language is quite a challenge.
To begin to understand a search query,
for example, the machine must parse
the parts of speech within a query.
It’s like back in Grammar 101, except
automated. Various parsing systems
can label words with tags based on
parts of speech (.n = noun, for example).
There are numerous ways this can
happen, but here are two automated
parsed examples of “red socks that
are less than twenty dollars.”
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
It doesn’t end there. Us humans still misconstrue each others’ sentences, so it’s
important for NLP to be able to figure out the multiple ways a sentence could
be understood and then score which is the most likely given the context signals
of the search (e.g., whether the search took place on Toms.com or inside
an academic journal’s app).
Here’s one example from SyntaxNet, an open-source neural network framework:
25
Starting with “Red”
We’ll explore color more in-depth in Chapter 3, but for our purposes here let’s
consider the color red for a moment.
To truly understand all that can be
encompassed by “red,” our NLP
algorithms at Reflektion go far beyond
lexicon and into cognitive semantics.
They use topic modeling to identify
that “red” is referring to a color,
and from there they map this baseline
understanding to a comprehensive
color hierarchy that includes every
known hue of red.
Why is this so important?
For starters, because red can refer
to a seemingly infinite array of reds.
Let’s say an internet retailer sells
red socks, but in all of the product
descriptions and metadata those red
socks are referred to as “scarlet socks.”
A search for “red socks” simply will
not be able to find and then display
those scarlet socks unless the algorithm
understands red both as a specific color
and as part of a vast neighborhood of
colors — the algorithm must be capable
of mapping the visual distance from
one hue of red to another.
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Moving to Socks
Similar to finding color synonyms, NLP
can also understand product synonyms
in order to build out a knowledge base
around particular keywords. This allows
a search for “socks” to map to every
possible query for socks, and through
parsing the sentence, it can determine
both what is typically meant by “red
socks” and what a users’ intent typically
is when they search this query.
Such knowledge can only be built
from training data. A baby’s brain
is processing and learning from
every sensory detail; an algorithm
is processing and learning from every
detail it’s being told to learn from.
In ecommerce, this can include product
catalogs and sources such as Google
News, which can be used to feed
the algorithm all of the world’s articles
so it can begin to develop a sophis-
ticated understanding of how words
are strung together in various contexts.
Underlying all of this, of course,
is the development of a model capable
of feeding the machine so that it
can understand and make real-time,
accurate predictions.
By leveraging NLP that is mapped
to such massive datasets, digital
retailers are essentially augmenting
and optimizing their existing product
attributes so that, for example, a search
for “red socks” may display the only
related product they have: a pair
of “scarlet stockings.”
27
And finishing with “less than twenty dollars”
Understanding price-based product
searches demands a deep semantic
understanding because operator words
that are part of product search queries,
such as “under,” (as in the example
from O’Neill) can have various
syntactical meanings.
NLP-supported operator words
can include a few of the following:
• Under
• Less than
• Over
• Above
• From _ to _
• Between _ and _
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Here’s a glimpse into how these processes would coalesce for a similar query,
let’s say “red dresses under $100”:
29
Additionally, price adjectives are critical for context. This includes a few of the following:
• Expensive
• Inexpensive
• Cheap (including cheapest
and cheaper than)
Here’s an example of “cheapest” from TOMS:
And then there’s “on sale,” which a retailer’s NLP-powered site should be able
to understand and map to related terms, such as “discount.”
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Practical AI… back to the roots
Even in the arena of voice commerce,
language must go back to its digitally
typed roots.
When you speak to Siri or Alexa or to
a retailer’s site, you are sending data
to a server that analyzes your speech
and translates it into text so it can work
through a few of the processes we’ve
addressed here.
As voice continues to constitute larger
and larger shares of all searched queries,
retailers will need to adapt or they’ll
quickly be viewed as behind-the-times
and uncaring of customer expectations.
In a Forrester report titled, Voice
Search Will Change Customer Discovery
Forever, Collin Colburn implores digital
sellers to ask a fundamental question to
determine the urgency with which they
should make the move to incorporating
voice search:
“Are my target customers using
voice search?”
To answer this question, Colburn
suggests using Google Search Console
for phrases such as “Ok, Google…”
as well as assessing the longest
of the long-tail queries — spoken
questions tend to be far longer
than typed questions.
To Colburn’s advice, I’d add factoring
in a variety of demographic information.
For example, a study covered in June
2017 by Search Engine Land found that
43% of Millennials made a voice-device
purchase in the past year.
All signs point to this number
skyrocketing. Are retailers ready?
Not if they’re still forcing voice-ready
consumers to type their complete
search, click submit, and hope.
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31%FramesDirect.com
increased email open
rates by 31% and email-
generated revenue
by approximately
22% when they used
artificial intelligence
to individualize their
customer emails.
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Some people worry that artificial intelligence will make us feel inferior, but then, anybody in his right mind should have an inferiority complex every time he looks at a flower.
Alan KayReferred to as the “father of modern computing”
“
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On Color Synonym Mapping
Rana el Kaliouby mentioned Amazon Alexa as she was
rehearsing a speech about AI for an upcoming conference.
This shouldn’t have been a big deal, but when her personal
Alexa woke up and said, “Playing Selena Gomez,” it quickly
grew into a situation that broke her focus.
After trying several times to make
Alexa stop, Rana recognized what
is perhaps AI’s most severe limitation:
it can’t recognize and respond to what
we’re feeling.
Her company, Affectiva, grew out of
MIT’s Media Lab and is seeking to
address this limitation through emotion
measurement technology.
Still, the example highlights how
far away AI is from achieving anything
remotely close to true human
understanding — which is often
the assumptive underpinning of most
fear-based discussions on the topic.
Take Sophia, for example. She’s a robot
from Hong Kong’s Hanson Robotics,
and she terrifies people precisely because
she appears as though she is self-aware
and can understand human emotions.
But she’s not and she can’t. It’s all
appearances.
When her eyebrows furrow it may seem
as though she’s thinking, but it’s simply
a gesture she’s been programmed to
do while she processes information.
C H A P T E R 3
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
And she can’t tell if you’re upset or
happy; she’s capable only of processing
language input and responding thanks
to an encyclopedic knowledge on
a variety of topics — similar to many
other devices that are using NLP.
However, what AI can do, and
can do exceptionally well, is color
synonym mapping.
At Reflektion, we’ve been working
for years to build the world’s most
comprehensive color knowledge base
on fashion and apparel. We pair this
with our proprietary algorithms and
CB Insights-recognized AI technology
so that our clients, including Ann Taylor
and DXL, can provide an individualized
customer experience in each moment
of their customers’ journey.
This is all to say: using AI for color
synonym mapping is a practical AI use
case that we know a thing or two about.
So let’s dive into a few of the most
common questions about color
synonym mapping and see some
practical ecommerce examples
to tie everything together.
59%By 2035, the wholesale and retail
industries could see a profitability
increase of 59%, according Accenture.
D I D Y O U K N O W
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What is color synonym mapping?
Color synonym mapping is the process of collecting all of the world’s potential color
names and calculating the visual distance between each color so that one individual
color becomes part of a neighborhood of related colors.
When a potential customer types “violet” into an on-site search bar, for example,
color synonym mapping is what enables “violet” to also be equated with all colors
the algorithm has determined to be on the spectrum of violet.
This means that a search for “violet” will display violet as well as orchid, plum,
magenta, and fuchsia, but it will also include colors such as “pretty princess purple”
that aren’t officially recognized colors but are names in the broad spectrum of violet
that have been used in a retailer’s catalog or on the web somewhere.
Warm Violet Cool Violet
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
How are color synonyms compiled?
Compiling an exhaustive list of color
synonyms can be challenging for
a variety of reasons, including because
color naming is language dependent
and because people typically refer
to only a few colors to describe what
they’re seeing: light blue, blue, and
dark blue, for example, to describe
what could be hundreds of different
hues of blue.
To establish a baseline at Reflektion,
we initially started with a color dataset
of 140 colors. This obviously wasn’t
enough, so we incorporated over 1,000
of the colors mentioned in Wikipedia,
but that wasn’t enough either.
From there we moved on to Pantone,
gathering another 1,800+ colors.
After combing through the web
and assessing color datasets across
all types of merchandising, we grew
the algorithm to respond to well over
4,000 colors and color names —
and at the time of writing, we’ve far
surpassed that figure.
Despite our own achievements, however,
we’re not even in the ballpark of what
the human eye can do. Researchers
have not yet been able to understand
how many colors a typical person can
see, but one oft-cited BBC article puts
it at about 1 million.
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How are all of those colors mapped by distance?
This is where things get technical.
The first step is to define every color
numerically by coordinates or additional
features. There are multiple ways to
do this, and because each has their own
strengths we’ve blended them in such
a way as to maximize the strengths
of each.
One relatively well-known and straight-
forward approach is to use the RGB
representation, which divides colors
into three basic elements (red, green,
blue). With RGB, each color takes
a value that ranges from 0 to 255
(8 bit per primary).
RGB Color Space
Credit: Rice University
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Practical AI for E-Commerce
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The additive combination of the three
elements yields a color. This kind
of representation is useful because
it allows graphic designers to pick
colors via a hexadecimal codification
(#FF0000 for pure red, for example)
which reduces the effort to identify
and compare colors.
Other methods take into account other
properties, such as hue, luminosity,
chroma, combinations of colors, etc.
Some examples of the latter are
HSL/HSV (hue/saturation/luminosity),
XYZ, LCH, or LAB, among others.
From there we use formulas to convert
colors from one classification approach
to another, for computing distances
between colors (we use euclidean
or manhattan distance measures),
and for increasing overall accuracy.
Then, finally, we can compile a list
of color names and the respective
distances between each color,
denoting similar colors that can
be used as synonyms:
Color 1 Color 2 Delta E (ΔE’00 Textile)
Threshold (e.g., 15)
Blue Navy 8.86 very similar
Navy Light Blue 39.53 not similar
Navy Midnight Blue 5.12 extremely similar
Dark Red Blue 46.47 not similar
Dark Red Fire Brick 5.71 extremely similar
Dark Red Coral 19.98 somehow similar
Misty Rose Coral 22.44 somehow similar
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As shown in the snapshots below, the results of computing color synonyms
produces a list of related colors comparable to what a human would perceive
with their own eyes:
Synonyms for “Light Pink”
Synonyms for “Blue”
Still with me? Let’s move on to some practical examples.
There are, of course, a variety of applications for AI-powered color synonym
mapping in the retail space. But here are 3 we’ve had particular success with over
the years.
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
1. The end of all-or-nothing color searches
Elite retailers have catalogs of their
colors, but they are often filled with
the retailers’ own diverse, creative
color names. These, of course, must
be accounted for and they are part
of the reason why our own color
catalog has grown so vast.
Some retailers may, in fact. have purple
shoes, or at least shoes in purples’
neighborhood of colors, but search
results will come back with 0 results
because they have creative color
names and failed to include common
color names in their metadata and
product descriptions.
We’ve worked to combat this challenge
by applying the color synonym
mapping described earlier to our clients’
product catalogs. This ensures that
products in similar color neighborhoods
will be displayed to potential customers
— and this can lead to fairly dramatic
revenue gains.
Removing this unfortunately common
friction point in the buyer’s journey
is a surefire way to improve digital
merchandising conversion rates.
Showing a customer something
remarkably similar is far better
than showing them nothing at all.
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2. More accurate search display results
It’s one thing to simply display the
results of a retailer’s product based
on a search for color, but our color
synonym mapping takes accuracy
to the next level.
To us, the products themselves provide
another rich source of color attributes.
By leveraging our AI image analysis
and combining it with our color
synonym mapping, we’re able to scrape
client image files to understand the
weight of a color as part of the image,
and then weight each color based
on a percentage of the image make
up — in other words, we can also use
the image itself (not just the text-based
search query) to map colors back
to the RGB.
One example could come from the watch
shown above. The bezel and band are
both types of dark blue, so if a shopper
were looking for a “dark blue watch,”
we would take into account that this
particular watch contains dark blue in
those areas. We’d weight it and display
it based on its color relevance in relation
to the other products in the catalog.
Similarly, there’s red in the bezel and
band. So, although it’s unlikely to be
the watch a potential customer would
want displayed first when they search
for “red watch,” it would still show up
in the results (likely toward the bottom
of the search results depending on the
available products).
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Practical AI for E-Commerce
How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences
Here’s an example from from our client Ann Taylor:
Our own comprehensive color catalog ensures that if a potential customer searches
for, say, “green shorts,” they’ll see results, in order and based on which is closer
to green (and, of course, whether or not “green” was actually a term the customer
or retailer used to describe said shorts).
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3. Photo search is here, are you ready?
In October 2017, eBay launched two
AI-based photo search capabilities.
One allows customers to essentially
begin their search anywhere by sharing
a product image (from social, another
website, etc.) with eBay’s mobile app.
The other allows users to start their
search on eBay’s site or app with
a photo they took with their phone.
It lends credence to the “a picture
is worth a thousand words” cliche.
But while the adoption of photo search
capabilities haven’t yet taken off the
way voice-based search has, the stage
is set.
With players like eBay and Amazon
making big moves in this space, it’s only
a matter of time before customers expect
the same from their favorite brands.
Customers are equipped with excellent
cameras in their smartphones; they
aren’t going to forever remain content
with only voice commerce.
When they see something, a dress
for example, or maybe even a sliver
of a color in a painting that they’d
love to have as the primary color
in their watch-face, they’ll be able
to capture the image with their phone
and immediately begin their path
to purchase — if their favorite retailers
have embraced practical applications
of AI and equipped their sites to handle
such moves.
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Practical AI for E-Commerce
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Notes
45
Reflektion is an AI-driven customer
engagement platform that understands
and influences the intent of each
customer in real-time, and instantly
delivers the most individually relevant
content across the touchpoints that
matter most — including Web, site
search, merchandising and email.
Our founder, Amar Chokhawala, was
an early employee at Google. For over
a decade, he helped to engineer Gmail,
Google AdSense, and many other
Google products and tools.
Over the years, however, he realized
that businesses still had no idea how
to optimize the buying experience
for their visitors.
This is why he created Reflektion in
2012. The company, based in San Mateo,
California, and with an office in Chicago,
Illinois, was named Shop.org’s 2015
Digital Commerce Startup of the Year,
a 2016 Gartner Cool Vendor in Digital
Commerce Marketing, and a Top 100
AI Startup in 2018 by CB Insights.
Combining individual shopper insights,
product intelligence, and deep learning
to create more intimate and impactful
commerce experiences, Reflektion is
driving dramatic conversion growth
and revenue increases for the world’s
best brands, such as Disney, TOMS,
Ann Taylor, Sur La Table, and Godiva.
To learn more, visit reflektion.com.
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Practical AI for E-Commerce
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reflektion.com