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Practical AI

For E-Commerce

How Artificial Intelligence Can Dramatically Improve E-Commerce Customer Experiences

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Practical AI for E-Commerce


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1

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

2



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.”

https://ctt.ec/v6Y4f

4



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.”

https://ctt.ec/gdlZr

6



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.

8



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

10



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

12



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.”

https://ctt.ec/L1O3t

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.

14



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

16



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

“

17

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

18



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.

19

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.

20



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.

21

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.

22



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.”

24



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.

26



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 _

28



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.”

30



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.

31

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.

32



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”

“

33

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

34



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

35

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

36



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.

37

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

38



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

39

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.

40



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.

41

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).

42



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).

43

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.

44



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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