CCO Replica Sample - 12

KEY TERMS

Artificial intelligence (AI):
The science of building
machines that can do things
that would be considered
intelligent if done by a human.

Machine learning: A subset
of AI that allows computers
to learn without being
explicitly programmed.
Common machine-learning
use cases are optimization
(over time choosing the best
option to achieve a set goal),
identification (extracting
meaning from images or text),
anomaly detection (isolating
an event that occurs outside of
the norm), and segmentation
(clustering based on inferred
or known characteristics).

Content intelligence: The
application of AI to content
management, most notably the
understanding and classification
of content to improve targeting
and measure performance .

Predictive marketing: The
application of AI to marketing,
usually to identify prospects,
predict what they might be
interested in, and recommend
the next best piece of content or
product information.

set up campaigns, you define business rules: "If
A happens, then do B" or "if the individual has
this characteristic, then put them in segment 4."
These can be simple to start with, but are always
an inadequate reduction of complex and varied
buyer journeys. So you add more rules to make
the campaign more targeted. And every time you
measure results, the outcome is that more rules
need to be written. Some of our enterprise clients
estimate they spend $500,000 per year on these
manual elements of marketing automation-and
that is disregarding the vital and significant
investment in ongoing content creation.
While marketing automation promises the world,
what it actually does is automate the execution of
content marketing, while decision-making remains
an impractically manual effort. It offers marketers a
strong workflow and even insights, but fails to provide
an automated way to act on those insights at scale.
Fundamentally, the content in those systems is dumb;
the system doesn't understand what the content is
about and who should read it. To track those looking
at address this, Forrester has recently started a
new research theme it calls "content intelligence,"
which it defines as "the use of artificial intelligence
technologies to understand and capture the
qualities inherent in any content." As the marketing
technology analyst David Raab says, "Something has
to give: either marketers stop trying to make the best
decisions or they stop relying on rules."
BRIDGING THE EXPECTATION GAP
In the face of relentlessly rising customer
expectations, leading marketers are investing
in AI-based tools-a category that encompasses
everything from personalization tools that "learn"
from individuals' online behavior to recommend
content more effectively, to tools that can detect
minute patterns across massive consumer data sets
and predict future behavior. (See Application Menu
sidebar on next page for more examples.) Since the
major marketing suites have yet to fully deploy or
productize their AI offerings, adopting AI usually
requires a blend of point solutions and datasets.
Indeed, marketers are increasingly piecing
together their own technology stacks from bestin-class point solutions, allowing the technology
to be built around customer need rather than
vendor features. Especially in complex customer
environments-for example, high-touch
relationship sales with long purchase cycles-the
application of AI promises to start bridging the
gap between customer expectation and actual
experience. This is most pertinent in global
businesses, as AI solves for (and relies on) scale.

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For Byron O'Dell, senior director of marketing
at IHS Markit, employing predictive machine
learning rather than marketing automation has
been about overcoming the challenges of scale. He
explains, "enabling marketing relevance at scale
is challenging, but predictive machine learning is
giving us a path to achieve this."
Initially, most marketers are considering two
key use cases: personalization and predictive lead
scoring. Personalization entails matching content
to the evolving customer need, particularly when
content is produced at scale and often poorly
classified. Predictive lead scoring is driven by the
insatiable desire for new sales conversations, where
the signals that identify an interested account are
difficult to identify or uncover.
THE INSIGHTS-DRIVEN BUSINESS
These new approaches address a fundamental
challenge: the buying process has changed, with
the buyer increasingly empowered, informed and
connected, but enterprises are largely selling in
the same way they always have. Using content to
attract, engage and convert is part of the solution,
but leading marketers are also using content
to understand the customer. In an increasingly
competitive world, any business that does not
understand its buyers will rapidly lose market
share as new digital-first competitors grow.
Disruptors obsess about their customer; they focus
on delivering a superb and seamless customer
experience; they are unencumbered by obsolete
technology and rigid processes. They appreciate
that gaining and acting on deeper customer
understanding build competitive advantage.
Forrester Research is building a body of evidence
around what it calls "insights-driven businesses."
One definition of these businesses is that they have
no friction between the point of understanding
the customer and the point of delivering the next
response. There is a feedback loop that is completely
automated. The cohort of businesses Forrester
defines in this category-fast-growing companies
innovating based on customer understanding and
experience-should be truly terrifying to incumbents.
Marketing AI promises unstructured, real-time
customer interactions that deliver value. Current
rules-based systems simply cannot scale nor can
marketing teams complete a manual process in the
time required to deliver relevance.
SUCCESS FACTORS
As an increasing number of businesses are investing
in AI-based approaches, the commonalities among
successful projects are becoming clearer.

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