CCO Replica Sample - 13

Executive sponsorship: Time and again, clear executive
sponsorship for the overall concept rises to the top of
the list. While mid-level marketers may successfully buy
point-solutions, larger organizations will find in order
to open up the right data sets and drive overall business
value, they eventually need an executive sponsor to
champion a more automated approach.
Defined outcomes: Early innovators had to make leaps
of faith without a known objective. But as the vendor
landscape matures and client examples are documented,
every project can and should have objectives linked
to valued and measurable business outcomes.
Available data set: Most experts would agree a
mediocre algorithm with a large data set always
trumps a great algorithm with a small data set. Dig into
the options available, clean up what you can, integrate
new data sources, and run tests to see results.
Team composition: Although the aim of AI
systems is to reduce manual tasks, the technology
still needs to fit into a team and business process
that understands its value. Increasingly, nontechnical business users are being served, but for
the meantime, it is important to ensure the team
understands data and are technical enough to grasp
the strengths and shortcomings of an algorithmic
approach. Perhaps more importantly, they must
be humble and eager to learn, and data-driven (i.e.
willing to link activity to results).
Vendor selection: Although there is a case for building
in house or using an agency for a bespoke application,
the menu of options on the market from vendors is
increasingly robust. To choose the right vendor, ask

about the data set, try multiple competitive demos or
trials, and push to understand whether the system is
pre-trained or requires you to do it.
TOWARDS THE PREDICTIVE ENTERPRISE
A shift towards the predictive enterprise requires
an ideological and practical rededication to
understanding the customer. The competitive
advantage afforded by artificial intelligence is not
based on the algorithm or the eventual application,
but rather understanding the customer in more
depth-and acting on that insight in the moment.
The obvious obstacles are exclusively organizationcentric: politics, technical roadblocks, resource
constraints and not-invented-here syndrome. Yet in
a flat world, with disruptive new entrants focusing
on a quality and seamless customer experience,
the only sustainable option is to invest ahead of the
competition. To twist the overused Wayne Gretsky
quote, it's time to skate to where the market is going,
not where it has been. The irony is that in this case, you
don't need to guess or rely on instinct. The customer
already moved. As a customer, I expect a Facebookinspired content feed, with the resultant privacy
trade-off. I expect Amazon-like recommendations to
be useful. And a la Google, I expect you to anticipate
my needs and offer help before I ask. Bring on the
intelligent and predictive enterprise.

Andrew Davies is the co-founder and chief marketing
officer of Idio. Follow him @andjdavies.

How do other marketers believe the industry
should evolve? http://cmi.media/evolve

Application Menu for AI in Marketing

Although there is an increasing list of potential applications for AI in marketing, these are some
of the most interesting.
Content Strategy
Recommending what content to create next
Campaign Strategy
Recommending what sequence of
communications to deliver
Personalization
Recommending the right content for each
customer based on behavior
Segmentation
Clustering customers based on behavior or intent
Copy Automation
Automatically generating subject lines and
descriptions

Lead or Account Prioritization
Ranking leads or accounts by their likelihood
to close

Turning Dark
Data Into
Business Drivers

Initial forays into predictive
marketing have hooked into
the first-party profile data in
large customer management
and CRM systems. It's not
always clean data, but it is a
good start. The deeper and
more defendable approaches
tackle a fundamentally harder
problem: turning unstructured
customer data into actionable
insight. Unstructured data,
often called dark data, is largely
unused within the enterprise,
yet comprises 88 percent of all
data gathered (IBM Research).
At Idio, we summarize our
approach to dark data with
the thesis, "You are what you
read." What we mean is that the
content you consume is highly
indicative of your interests and
highly predictive of your intent.
AI-enabled tools analyze this
dark data-essentially how your
customers engage and behave
with your content-to predict
their interests and intent, and
personalize their experience.

Project Checklist
* Do I have executive sponsorship for an
AI-based approach?
* Have I defined several business
outcomes?

Sales Strategy
Recommending the right product/service
offering and content to use in sales

* Is there an urgency and clear time frame
to achieve those outcomes?

Sales Intent
Predicting the right product offering, deal size
and close date

* Has my team bought in to the project?

Retargeting
Recommending the right content within
retargeted ad units

* Is there a data set to model?
* Have I assessed the build-vs.-buy
decision?
* Have I created a shortlist of vendors?
* Are their systems pre-trained or is there a
lengthy training process?

http://www.cmi.media/evolve

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