Architecting experience a marketing science and digital analytics handbook

Architecting
Experience
A Marketing Science and
Digital Analytics Handbook

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Advances and Opportunities with Big Data and Analytics (AOBDA)
Series Editor:  Russell Walker (Northwestern University, USA)

Published:
Vol. 1:



Architecting Experience:
A Marketing Science and Digital Analytics Handbook
by Scot R. Wheeler

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Advances and Opportunities with Big Data and Analytics

Architecting
Experience

A Marketing Science and
Digital Analytics Handbook

Scot R Wheeler
Medill-Northwestern University, USA

World Scientific
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Published by
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Library of Congress Cataloging-in-Publication Data
Names: Wheeler, Scot R.
Title: Architecting experience : a marketing science and digital analytics handbook /
Scot R Wheeler, Medill-Northwestern University, USA.
Description: | Series: Advances and opportunities with big data and analytics; 1 |
Includes bibliographical references and index.
Identifiers: LCCN 2015028389| ISBN 9789814678414 (hardcover : alk. paper) |
ISBN 9814678414 (hardcover : alk. paper) | ISBN 9789814725651 (softcover : alk. paper) |

ISBN 9814725651 (softcover : alk. paper)
Subjects: LCSH: Communication in marketing. | Digital media.
Classification: LCC HF5415.123 .W48 2016 | DDC 658.8/02--dc23
LC record available at />British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.

Copyright © 2016 by World Scientific Publishing Co. Pte. Ltd.
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Printed in Singapore

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Contents

About the Author

viii

Introductionix
Chapter The Foundations of Personalization
ONE
1.1 The New Business Value: Analytics
Increase Relevance
1.2  Introducing the “Demand Chain”
1.3  The Customer Journey
1.4  Research and Analytics

1
4
7
8
16

Chapter Strategy, Technology, Science & Art
TWO
2.1  Paid, Earned, or Owned Breakdown
2.2  The Changing Nature of Marketing Data
2.3 The Fundamental Analytics Architecture:
The Analytics Pyramid

37

Chapter The Applied Digital Analytics Playbook
THREE (ADAP) Part One

49

3.1  ADAP Section One: Problem Definition
3.2  ADAP Section Two: Solution Definition

21
26
32

50
55

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viContents

Chapter The Changing World of Owned Media
FOUR
4.1 Web Architecture & Web Data Collection
4.2  Client-side Tagging
4.3  Tagging Design & Deployment

4.4  Mobile Marketing
4.5  Email Marketing
4.6  Introducing Cookies
4.7  Applying Owned Channel Metrics
Chapter Earned Media: Organic Social & SEO
FIVE
5.1 History
5.2  Organic vs. Paid Social Media
5.3  Organic Social Media Strategy
5.4 Inbound Organic Social
Data Sources for Key Objectives
5.5  Applying Social Metrics
5.6  Search Engine Optimization

73
79
87
95
96
102
105
115
115
119
121
123
127
146

Chapter Paid Media Analytics

SIX
6.1  Digital Paid Media Touch-points
6.2  The Paid Media Ecosystem
6.3  Targeting & Retargeting
6.4 DSPs and Programmatic Real-time
Bidding (RTB)

168

Chapter Testing & Optimization. Marketing
SEVEN Automation. Attribution

173

7.1 Prescriptive Analytics:
Testing & Optimization
7.2  Marketing Automation
7.3  Cross-channel Attribution

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71

151
152
153
159

173
187

196

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Contentsvii

Chapter Data Management, Models, and Algorithms
EIGHT
8.1 The Applied Digital Analytics Playbook
(ADAP) Part Two
8.2  Data Mining & Data Visualization
8.3  Predictive Analytics & Machine Learning

199
199
205
208

Chapter The Cultural and Organizational
NINE Impact of Data

221

9.1 Visualization
9.2 The Information Society: Media

Cycles & Feedback Loops
9.3 Organizational Change for Effective
Digital Analytics

221
229

Conclusion

263

250

Index267

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About the Author

Scot Wheeler is a leader in digital analytics delivery, overseeing
a team which develops consumer
intelligence, prospect conversion propensity scoring, crosschannel performance evaluation,
environmental trend analysis,
testing, targeting and optimization, and predictive modeling

for budget allocation and response forecasts.
He is also an adjunct lecturer in Northwestern
University’s Master’s Degree program in Integrated
Marketing Communications, where he teaches Digital
Analytics and Statistics.
Scot received his MBA in Strategy, Finance and
Marketing from Northwestern University’s Kellogg
School of Management. Prior to his current roles,
Wheeler was Group Director of Marketing Science for
the digital agency Critical Mass. Before that, he ran
product development, marketing and sales for the
social media analytics platform Evolve24. Wheeler’s
professional background spans a variety of technology,
consulting and agency roles. From his start in software
development, Scot’s 20 years of experience at the intersection of technology and marketing includes work
with Yahoo!, GE, Electronic Arts, AT&T, MasterCard,
State Farm, USAA and HP.
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Introduction

In the Integrated Marketing Communications
approach taught at Northwestern University, the consumer is placed at the center of all marketing practice. Unfortunately, this customer-centricity is not
always as common in practice as it should be in the
real-world of digital marketing. In actual practice,
brands often place concern for awareness of their
message at the center of their marketing practice,
and much “digital strategy” is simply an effort to
ensure consistent branding and “messaging” across
digital channels. However, any digital marketing
practice that is focused on brand message and structured primarily by channel and function (the paid
media team, the social media team, the web team)
will typically fail to create a truly integrated and relevant experience as a consumer moves across digital
channels. The capability to capture and use data
from any consumer’s digital engagement, and the
growing expectation for content personalization that
consumers have as a result (beginning with each
user’s Amazon and Netflix experience), means that
the disconnection of data across channels will be felt
by the user and will adversely impact their experience with the brand. Conversely, the effective collection and connection of data across channels will play
a significant role in creating and maintaining brand
relationships with the digitally embedded consumer.

ix

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Thus, the question this book sets out to answer is the
biggest question facing digital marketers today: how
do I deliver content and experience around my
brand that is relevant enough to drive engagement in
the user’s current context? The quick answer to this
is of course through the application of data and analytics to drive highly relevant, contextual targeted
content and adaptive experience, but since this
answer is not as easy to achieve as it is to say (and it
is a mouthful), this book has been designed to help
you develop the understanding and skills required
to make this happen.
The path to delivering relevant, contextual and even
adaptive digital experiences is not one for the marketer to walk alone, and this book will explore the
relationships that must emerge between marketing,
technology, research and operations to bring about
truly effective 21st digital experience delivery. At the
end of the day however, the envisioned reader of this
book has the strongest interest in the marketing perspective on these conversations, with a 21st century
marketing mindset that understands marketing as
innovation and technology driven customer-centric
relationship building for long-term customer value
versus message dissemination for the masses myopically focused on driving business transactions above
all else.
Digital communications long-ago turned mass-media

on its head, a fall from which mass-media as the top
effective communications form will never recover. In
a world with a seemingly infinite amount of content
and scores of methods for consuming that content,
communication today is about appealing to individuals, person by person, and appeal requires relevance

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Introductionxi

in context. In any conversation, delivering relevance
in context requires understanding the person you’re
speaking with. This is true for digital marketing as
well.
This book will focus on the impact of data and technology on marketing both within businesses and for
consumers as well. It will allow you to guide your
organization in a necessary process of continuous
evolution to effectively collect and use the right data,
analytics, technology platforms and algorithms to
achieve valuable outcomes.
This evolution is built around a six-stage process
which is facilitated through an Applied Digital
Ana­

­
lytics Plan (ADAP), which is introduced in
Chapter 3:
1. Define the problems that data can solve.
2. Identify sources of data (existing and potential).
3. Collect, manage and analyze data.
4. Overcome organizational and cultural inertia.
5. Apply data and analysis to solve the problems.
6. Evaluate the outcomes.
This book will explain how evolution within the process detailed above is achieved through the following activities:
1. Data-driven problem identification and data-oriented strategic communications design (design
research).
2.Strategic alignment of customer and business
objectives.
3.KPI development and documentation from
objectives.

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4. Marketing channel digital data collection strategy

and implementation.
5. Multi-channel data integration.
6. Testing and optimization across all channels.
7.Integrated planning models and performance
reporting.
8. Predictive analytics and adaptive digital experience enablement.
These activities build upon and interact across each
other and are discussed in detail through the
remaining chapters, with the first several chapters
focusing on the specifics of data and collection and
analysis for owned media, earned media and
paid media channels, and with the later chapters
focused on integrating data across channels and
applying it to continual optimization of results in
omni-channel engagement with customers through
applied analysis and technology. Before proceeding
into these activities however, it is worth our while to
begin with a deeper examination of the question of
relevance in digital marketing. What is relevant to
our customers at any given point in time? How do we
know? And how do we take that knowledge and use
it to deliver better experiences that in turn yield better results?

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Chapter
ONE

The Foundations of
Personalization

As the digitization of life proceeds with seemingly
exponential progress, we continually find ourselves in
an ever changing cultural landscape, where each day
the amount of new information recorded is greater
than all of the world’s recorded information prior to
the digital age, where the average citizen of nearly all
nations has unprecedented access to knowledge,
entertainment and opinion, and of course where
those same citizens are exposed to hundreds of advertisements a day on screens both stationary and mobile.
We find ourselves in a world where social life increasingly means digital life, and where success in business and marketing require advanced capabilities to
access and interpret data. In short, we’ve crossed the
horizon into a world where it can be argued that
culture (i.e. work, arts and entertainment, customs,
habits and pastimes) has largely become a product
of information technology, and that correspondingly, information has become the core of culture in
the developed world.
Living in a technologically and digitally driven world
means living with constant change. In the 20th century,
the economic and cultural base of the developed
world transformed from being agriculturally (and
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land) based to manufacturing and technology based
over half a century, with the Second World War finally
cementing the rise of the technocrat over the gentry
as culture’s new elite. The transformations that have
occurred as developed culture has then shifted from
capital intensive analog technology to skill and information intensive digital technology have come more
and more rapidly, and many businesses are still trying
to catch up with the changes in technology and society
that have come at them over the last two decades.
The continual development of information technology and applications arises from a human drive to
continually expand both our knowledge and convenience, a drive that has been at the core of advancement
in science and technology for centuries. In the middle
of the 20th century, the study of such advancement in
communication technology was taken up by a professor of communications at the University of Toronto
in a way that forever changed our understanding of
the relationship between media and society.
Given the breadth and depth of any individual’s
exposure to media today, it seems inconceivable that

the phrase “Media” and the concepts associated with
it would at one time have required an introduction
and development within popular thinking, but in
fact there was such a time not that long ago, and the
man who made the introduction was Professor
Marshall McLuhan.
McLuhan’s 1965 book Understanding Media: The
Extensions of Man introduced the concept of Media
where previously there had simply been notions of
independent communication technologies such as
‘the press’, ‘television’ and ‘advertising’ which were

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recognized to synthesize into a single unit in practice,
but were nonetheless typically evaluated individually
with regards to their impact on culture.
McLuhan observed that these and many other information and communication technologies and practices not only work together and proceed from one
another, but that in doing so they actually extend the
perceptual power of individuals and mediate communication and thinking in ways that significantly
influence culture and human affairs; thus his application of the term Media to these mediating ‘extensions of man’.

Perhaps the most lasting convention introduced by
McLuhan (and the most useful for the subject of this
book) is the notion of “hot” and “cool” mediums, or
elements of culture. Very few people wonder who
should be credited for coining the use of ‘cool’ in
social context, e.g. a ‘cool’ new band or the ‘cool’
kids at school, since the expression seems to have
always been a part of the vernacular. Equally, the idea
of a ‘hot’ new sound or a person with a ‘hot’ body are
commonly used in American parlance without consideration of origin. Today, these notions of “cool”
and “hot” seem natural in the ways they are applied,
but it was just as recently as the beginning of the Cold
War that Marshall McLuhan observed that different
media exerted different influences on people’s perceptions and engagement with those media, and classified those media into “hot” and “cold” categories.
McLuhan’s theory has held up incredibly well into
the early 21st century media environment, and still
provides an excellent framework for understanding
the influence of mediated information on culture,

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and the countervailing response of culture in the
subsequent development of new information technology. Everyone wants their content to be “hot”,
“sticky” and “viral”, and these metaphors for information owe much to McLuhan’s understanding of how
people engage with messages in media. McLuhan’s
thinking is worth consideration by anyone interested
in the science of marketing communications, and
will be explored in detail in Chapter 9. But to begin
with, we’ll first boil down the application of McLuhan’s
ideas to marketing to a fundamental principle — the
message is either relevant in the receiver’s current
context, or it is not.

1.1 The New Business Value: Analytics Increase
Relevance
In 20th century marketing, messages from the brand
were thought to be uni-directional, and consumer
interaction with these messages was thought of as
passive receipt and absorption of the message. Digital
communications turned that idea on its head. Digital
communications are multi-directional, as consumers
have the capability to respond and interact directly to
and/or about the brand through both shared and
owned media channels. This interaction between
brand and consumer or by consumers about/around
a brand in all digital channels is commonly referred
to as “engagement”, and effective engagement is the
objective of all digital marketing. Engagement resulting from digital marketing may be as simple as clicking on a link or liking/sharing content (and thus
passing it along through a network), or it may be
more involved, such as returning to a brand’s digital

experience, going deeper into content and tools,

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adding their own new content, attending an event,
completing a form, making a purchase or sharing a
referral.
When it comes to engagement, one rule stands out
over all others: relevance drives results. We are no
more likely to engage with activities and conversations that have no appeal or value to us in digital
than we are in the real world. In fact, digital gives us
much better ways than we have in real-life to filter
out irrelevant and uninteresting content. Digital also
gives us much more content to filter than we face in
real-life, which for most users creates a high threshold between what is potentially viewable for them
and what actually elicits engagement.
This returns us to the largest problem that the digital
marketer faces today: how do I deliver content around
my brand that is relevant enough to drive engagement
in the user’s current context? The answer to this is of
course through the application of data and analytics

to drive highly relevant, contextual targeted content
and adaptive experience.
The figure below tells a story about the rise of relevance in digital communications channels over the
past two decades.
As we see in Figure 1.1 in the earliest days of mainstream digital communication, marketing was conducted through email, display advertising and websites.
Of course, the dawn of email marketing brought the
immediate dawn of spam, since in the early days, simply having an email address qualified you for targeting
by anyone who could get that email. In early digital
marketing, display advertising was made more relevant
than email by virtue of some occasional effort to align

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6

Figure 1.1  
The Rise of
Relevance

Architecting Experience

advertising with the content of the page on which it was
advertised, at least by marketers who didn’t want to throw
their digital budget down a black hole. And marketing on

websites was the most relevant content on the web for
those exposed to it since they were qualified to see it based
on having sought it out.
Search marketing arrived on the scene in earnest in 1999
to take the top position for delivering relevant marketing
content through algorithms that matched expressed interest or intent with digital content results. Being based on
explicit interest cues and algorithmic matching of content
to those cues, search has remained toward the top of the
relevance ladder ever since, being surpassed recently only
by content that has interest cues, algorithmic content targeting and memory of user history in a more specific context than the blank page of a new search. However,
Google’s interest in having more user context data from
across all platforms will likely see the return of “predictive”
search (exhibited currently in the Google Now application)

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as the most relevant form of content delivery around
any users immediate content needs in context.
What Figure 1.1 shows us — beginning with search
then extending to social content, social ads and
eventually lifting all boats — that increasing data

about context and algorithms for matching content
with context are helpful in delivering relevance. But
where is this data, and how do we use it to discover
what is relevant, and apply that understanding to
driving results?

1.2  Introducing the “Demand Chain”
A company’s supply chain and the practice of supply
chain management is critical to that company’s ability
to produce and deliver its goods to its customers. An
organization’s supply chain is the linkage of material,
processes and people that proceeds from the initial
procurement of the raw materials needed to product
a product, all the way through production to the final
delivery of that product to the end customer. Without
careful management of the supply chain, among
other problems, materials required for production
might not be available when needed, warehouses
could be overflowing with un-needed raw material or
finished product, or more product than needed
could be produced to sit on shelves in stores without
buyers. Supply chain management begins with projections around the demand for products, then puts
into motion all of the gears required to produce and
then distribute the right amount of product in the
right places at the right time based on that demand.
While a complex array of production inputs, outputs
and logistics provide the material for supply-chain
management, it is the projection or forecast of

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demand from the market that provides the impetus.
If the forecasted demand for product is incorrect,
then the best a highly effective supply-chain management process can do is try to adjust to the estimating
error once it becomes apparent.
Creating perfectly precise demand forecasts is nearly
impossible, so producers of goods and services have
options. If the good is something packaged and sold
in a store, then a target for sales is set, and production (in actual units or in production cost-to-profit
ratio for something like software) is run to that target.
Once produced, the product is put up for sale in
stores and/or online. Once the product is up for sale
in some location, the supply chain has done its job
until more product is needed — which can be quickly
for made-to-order goods or services. However, once
the product is up for sale, the product has entered
the “demand chain” — a less recognized and less
understood area of the marketing equation.
If the supply chain is the process that pushes a product out to where it can be bought, the demand chain
is the counterpart process through which the customer ultimately pulls the product into their basket.

Similarly, if the supply chain is the process that produces product supply, then the demand chain is the
process that produces demand.

1.3 The Customer Journey
While marketing strategy and marketing communications tries to understand and tap into the demand
chain, it is a common and disadvantageous mistake to
think that marketing drives the demand chain, and is

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The Foundations of Personalization9

the source of product demand. Demand for products
starts with a need or desire in a consumer. It is very
true that advertising applies psychology to evoke
needs and desires, but it is also true that most goods
also fulfill an actual need or demand. Advertising can
be used to cultivate the perception of a certain brand
of clothing as more sexy or sophisticated than another,
but the demand for clothes was already there. Demand
also requires a stimulus to buy — I may be exposed to
advertising that guides me to fully perceive a brand as
tied to some characteristic or quality, but perception

is not purchase. To become a customer, there must be
some kind of trigger prompting me to buy something in that product category. Only then will my
pre-established perceptions of the characteristics of
various options begin to matter.
So, the demand chain begins with a “trigger” to consider a purchase. This can be as simple as running
out of toilet paper, or as complex as recognizing the
need to determine a care plan for an aging parent.
The most traditional concept of the consumer path
to purchase (along the demand chain) envisioned
marketing as a funnel that brought the consumer
from awareness, to interest, to desire and then finally
to action, or purchase.
This way of thinking of customer engagement has
guided generations of marketing planners and marketing campaigns, with no expense spared on awareness and branding campaigns based on the idea that
more volume at the top of the funnel has to translate to
more volume out the bottom of the funnel. Of course,
the funnel was never a funnel as there was never 100%
retention of what went into the top. Instead, it was
more of a sieve, with much of what went into the top

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spilling out before it ever reached the bottom. Thus,
the idea that increasing volume at the top of the funnel
would increase sales at the bottom has never been
guaranteed. In fact, depending on how many and
how large the holes in the process of moving consumers from awareness to purchase, there has always been
strong potential to waste huge amounts of time and
effort moving people into the top of a process from
which they would immediately fall out.
This recognition of prospect attrition throughout the
traditional “funnel” to purchase and the question
about how to decrease such attrition necessitated a
new way of thinking about the path to purchase. In
2009, McKinsey Consulting introduced the idea of
the Customer Decision Journey, which has subsequently become the new standard in thinking about
the path consumers take from awareness through
purchase and importantly, even after purchase. Since
its introduction, it has gone through several stages of
evolution and refinement, such as the version of the
journey we will reference throughout the pages of
this book.
Figure 1.2 takes the original notions of the McKinsey
Customer Decision Journey and adds two additional
dimensions: the role of external “life events” as triggers to the customer decision journey, and the interaction points between customers on the journey and
data about those customers.
The process begins on the far left with a “life event”,
which is some piece of context that provides the
impetus to take action in our product category. Life
events are diverse, and relevant life events for any

business will vary based on the nature of that business. Life events range from major events such as

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Figure 1.2  
The Customer
Decision
Journey

marriage, a new job, a new house or the birth of a
child to everyday events such as hosting a party or
even just having time for lunch, reaching the weekend, getting off from work in the afternoon, or needing toilet paper. Life events do not need to be major
in order to be significant triggers for the customer
decision process. The size and scope of the life event
is not what matters in itself. What matters most is that
we, as marketers, are cognizant of the fact that there
is always an external context to a customer’s entry into
a decision journey — that the customer has a life outside the decision process, and that something about
that life brought them in to the decision process.
Understanding this, the marketer should treat every
life event as something important enough to their
customer to trigger the expenditure of thought and

energy through the decision process, and should of
course recognize that the more significant the life
event, the more significant the customer problems,
objectives and needs.
From the triggering event, we proceed clockwise
through the diagram. Customers in the first round of

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the journey will travel around the outside path
labeled “active evaluation”. While on this path of
active evaluation, they are developing an “initial consideration set” with regard to the options available to
them to address the problems, objectives and needs
established with the triggering event. Awareness of
their options is of course very important at this stage,
so it is good that awareness building is already a significant piece of most marketing programs. But initial consideration extends well beyond just awareness
and is in fact a process of comparison and evaluation
leading to a decision.
To survive the active evaluation stage, brands must
not only stay within consideration throughout the

process, but must also stand-out from other options
by the time the process reaches a decision point.
While understanding the importance of awareness
has led to a focus on that stage, with data being used
in increasingly sophisticated ways to optimize spending on targeted impressions for awareness development, the entire active consideration process is still
under-addressed by most marketing programs. This
is changing with the evolution of marketing automation software, and those firms that are addressing this change with the most focus are poised to
reap the rewards. To repeat one of the most common themes of this book, relevance delivers results,
and if there is a critical time to establish relevance,
it is during the period of time when your company
is being considered against competitors for a fit
with your customers’ needs. The means to establishing relevance is of course by generating meaningful
points of engagement from insights about each
customer’s context as drawn from your data. The
method for this will emerge through the rest of the

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9”x6”

b2242   Architecting Experience: A Marketing Science And Digital Analytics Handbook

The Foundations of Personalization13

book, so suffice it to say here that knowing when and
why to apply data to engagement is at least as critical
as knowing how to do so.

Moving clockwise along the diagram, the customer
moves through active evaluation and ultimately
reaches a decision. If the trigger was not strong
enough to overcome dissatisfaction with all of the
options considered, then the customer may decide
not to make a purchase at all. If the trigger was strong
but no option was truly satisfactory, then the customer will begrudgingly select the least dissatis­
factory option. With the long-tail of options ranging
from standard to niche presented today in most markets, this is increasingly less of a concern to most
consumers, who are happy for example to switch
from hotels to Airbnb to meet a set of requirements
that the hotels could not match, but which they had
to accept before Airbnb was an option. And if during
active consideration one option differentiated itself
according to the customer’s needs, at this point the
decision will be made to purchase from that brand.
Continuing clockwise around the consumer decision
journey, the next stage focuses on the post-purchase,
or post-decision, experience that the customer has
with a brand. Typically, this is thought of as “customer service” for customers who have bought something from us, and that is a large segment of the
population that a company will engage at this stage
in the journey. But those customers who decision led
to an option other than ours still can, and should,
also be engaged here.
For those potential customers who chose another
option, our post purchase engagement will be

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