In general, Analytics help you leverage investments that you have done already in your IT investments, on ERP, on CRM systems, on sales force automation systems, and on all the data collection that you put in place. Unfortunately, reality isn’t that straightforward. It’s still a struggle for most companies to drive valuable insight into the data they have.

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Harvesting the value from Advanced
Analytics
Analytics add value. This value can
be seen on the aggregate company
performance as well as on value
created in specific business
processes.
Recent research by the International
Institute for Analytics comparing
companies with different levels of
analytics maturity presents some key
findings:
• Companies with higher
analytics maturity are likely to
outperform companies that are
lower in maturity on market
valuation, shareholder returns,
and financial & company
performance.
• Companies with higher
analytics maturity are likely to
have a better reputation,
stronger brand, and are more
able to innovate.
MIT Sloan Management Review on
the value in customer engagement
found:
• Almost 60% of managers said
that they used analytics to
gain a competitive edge
• Higher analytical maturity
drives improved customer
engagement.
• Sharing data with stakeholders
(customers, vendors,
government agencies, and
even competitors) increases a
company’s influence in its
ecosystem.
When going through research and
use cases of Analytics several key
benefits arise. In general Analytics:
• Increases productivity, for
example: increases marketing
conversion, increases revenue
on marketing campaigns,
increases close rates per
salesperson, reduces job
candidate interviews, and
automation of processes &
decision making.
• Increases customer lifetime
value by, a.o., reducing
attrition, improving cross- &
upsell, and improved targeting
of valuable customers.
• Reduces employment costs by
reducing employee turnover,
reducing absenteeism,
improving recruitment, and
improving employee
development
• Improves customer experience
by improving product
development, by reducing
complaints, by streamlining
processes.
• Reduces operational expenses
by cutting out unnecessary
actions and streamlining
processes.
• Prevents risk on credit, or
fraud, or cyber security; and
streamlining complex
processes.
In general, Analytics help you
leverage investments that you have
done already in your IT investments,

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on ERP, on CRM systems, on sales
force automation systems, and on all
the data collection that you put in
place.
Unfortunately, reality isn’t that
straightforward. It’s still a struggle
for most companies to drive valuable
insight into the data they have.
There is also a cultural and
organizational problem in adopting
driven decision making in most
organizations. And that leads to only
11% of companies being very
satisfied with the results of their
investment in data and analytics
projects.
Synergy is key
Figure 1 Synergy helps find the V-spot
To generate value from analytics you
need to bring your business process,
your IT architecture, and your data
together with analytics, and try and
find the sweet spot. The sweet spot
is the cross section between these 4
dimensions. This is where you will
generate the value: the V(alue)-
Spot. If you do not deploy the
analytics into your business
processes, into your decision-making
processes, and do not] use the right
IT architecture and data to support
that, you will not derive value.
We will need to use multiple analytics
capabilities working together to reach
a decision. Business decisions are
complex. In the example of
acquisition, these are some the
questions that need to be answered
to make a right decision on whether
you are going to make an offer to
someone or not.
• What is in the target area and
who is in it?
• Which channels can we best
use?

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• What are the messages that
we want to use?
• Who is likely to respond to an
offer, and if they respond to it,
will they actually buy it, and
for how much?
• If they buy it, will they pay,
and when?
• If they bought, will they buy
more, and what, and how long
will they remain a customer?
Continuously learn,
adapt & improve
And because a business and its
environment are dynamic we need to
be agile to adapt to changing
circumstances. We need to
continuously learn and adapt. In
Figure 2 you see a modified version
of the ‘OODA’ loop: The Observe,
Orient, Decide, and Act loop, that
John Boyd developed, based on the
decision-making process of fighter
pilots.
Figure 2 OODA Loop for continuous learning and agility
In business, it is the same. We need
to continuously observe what is
happening in the market, what is
happening in our organization. We
then need to orient: where are we,
and where we need/want to go. We
need to make decisions and based on
those decisions we act. But those
actions have an effect on the
environment, so we need to have
feedback loops at every moment in
time to actually observe what is
happening. Our business actions are
part of our analytics, because when
we act on a decision, we influence
the market that we’re in. We change
the reality that we built our previous
analytics on.

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An example: when you build a
retention model, to predict which
customer is likely to cancel their
contract or go to competition, and
you take an action to prevent that,
you essentially take an action to
make your prediction not come true.
Next time around, you need to adapt
your analytics for that, and you need
to incorporate your own actions to
the process. So, you may also need
to build a model that looks at offer
acceptance to identify who is actually
going to accept the offer: who is
“savable”?
A follow up step that you then may
consider, is to also look at who is
worth saving: What can we expect
from this customer in the future?
Does it make sense for me to make
an offer to keep them, or are they
not worth it? Is there a likelihood
that this customer is someone that I
don’t want to keep?’ So overall, we
continuously need to look at: what
are we seeing, where are we, how do
we decide to take an action? Then we
use that feedback loop to make sure
that we continuously learn, adapt
and improve.
Not a “silver bullet”
Analytics is not the “silver bullet”
that will help you be successful with
the push of a button. “Beyond the
Hype: The Hard Work Behind
Analytics Success” in MIT Sloan
Management Review (2016),
mentions 5 very important issues:
1. Competitive advantage with
analytics is waning: If
everybody is starting to use
analytics, then it becomes
more difficult to distinguish
yourself from your
competition. The only way to
do that is to look at ‘how can I
do analytics my way and how
can I leverage the information
that I have in my organization
best, to be better at analytics
and better in the market than
my competitor?’.
2. The optimism about the
potential of analytics remains
strong. Analytics still add a lot
of value, so non-analytic
companies still perform less
than analytic companies. The
question you need to ask
yourself is then: do you need
to actually perform better than
everybody, or do you want to
perform good enough? You
should ask yourself:
a. Is your strategic focus
on maximizing,
optimizing, satisficing?
b. What are your priorities
c. What is it that you want
to accomplish?
d. How far do you
want/need to go with
Analytics?
3. Successful analytics and data-
driven decision making
requires a sustained
commitment to changing your
decision making.
4. Companies that are successful
in analytics, have a strategic
plan for analytics. They
thought about:
a. How does it align with
our strategy?
b. How are we going to
use it?

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c. How and where does it
fit in our organization?
5. Investments and cultural
change are required to achieve
a sustained success in
analytics. It is not just a
matter of implementing some
technology and loading a lot of
data. It requires thinking
about ‘how do I change my
decision-making processes,
and how do I change my
organizational processes, and
how do I change my processes
interacting with customers,
partners, and other
stakeholders?’.
Incomplete &
subjective data
We also need to keep in mind is that
perfect information does not exist.
There are many dynamic processes
going on in our organization, in our
business environment, and in the
world. Constant change is the rule
and not the exception.
Data that were relevant and usable
yesterday may not be usable
tomorrow. Data about the situation
last year may be relevant for some
forms of analytics while it will be
irrelevant for others. We need to
make sure that we realize that and
manage the life-cycle of data.
No matter the promises that are
made by data vendors and by data
collection technology venders, and
the hype of ‘we can get all the data
on the internet’, we will never have
complete data. We will be unable to
get all data that is needed. If we are,
for example, looking at a marketing
interaction, we will never have the
specific information about what is
happening within a person reading
our email, or looking at our website
at a specific time. We would need to
consider, a.o.:
• What has happened that
morning at home.
• What & when (s)he has eaten
or not eaten.
• what the weather is like.
There are all kinds of other data that
we will never be able to process. We
also need to make sure that we
understand that humans are
emotional decision makers.
Whenever we try to find a new
customer, or we want to approach a
customer, we need to take into
account that there is a certain level
of unpredictability in their behavior.
Therefore, we will never be perfect.
All the data that we’re using is also
subjective. Choices have been made
in what data to store, how to store it
and more. When we collect Social
Media data from the internet, it is
subjective and biased because we’ll
never have the opinions of all the
people on Facebook or on Twitter.
We will only be able to collect the
data on people that are actively
using these platforms and voicing
their opinion. We definitely will not
have data about people not on
Facebook or Twitter.
The ‘flaw’ in the
algorithm
There is a lot that science and
mathematics cannot tell us.
Especially, there are limitations to

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what we can do on a technical
perspective, but also limitations on a
moral and philosophical perspective.
There is no way that science,
mathematics, and logic can tell us
what is morally right, and what is the
right way to go.
What we need to understand, is that
algorithms make mistakes.
Algorithms are basically computer
programs, and computer programs
can go wrong, and the development
of computer programs can be wrong.
There are some famous examples on
algorithms that misclassified people
or were inherently biased. Therefor it
is a good practice to question
algorithms, and to monitor them,
and to be very wary about the
“perfect algorithm”. You always need
to be aware of ‘the boundaries of
usability’.
Algorithms are not neutral. They’re
always defined with a business goal
in mind and business goals are based
on assumptions, priorities, and
emotions. Choices have been made
in:
• Building the algorithm
• The techniques employed
• The design and programming
• The translation of the technical
output to a business decision
Lastly, you might have Rogue
algorithms. You might have
algorithms that have errors in them,
because they’re not developed
correctly, or you might have
algorithms that are using data that
you do not want, and that therefor
might have an effect that you do not
want. For example, they might be
discriminatory, or they might actually
miss out on certain opportunities
because they are too limited.
We need to remember the advice
from Box and Draper (Box, G. E. P.;
Draper, N. R. (1987), Empirical
Model-Building and Response
Surfaces, John Wiley & Sons.):
“Remember that all models are
wrong; the practical question is: how
wrong do they have to be not to be
useful”.
We always need to question a model,
because models are always
incomplete and a wrong
interpretation of reality. They deliver
us useful information that will help us
make better decisions, but they
continuously need to be
• Challenged.
• Governed.
• Reviewed.
Questions to ask
yourself
Based on the above you can start
asking yourself some questions that
will help you drive successful
Analytics & data driven decision
making initiatives in your
organization. The main question is,
of course,
How do YOU want to make
decisions?
What are your organization’s ethics
and values? Do they put any
limitations on the type of analytics
you want to perform, or the type of
data that you want to collect?
What are your strategic objectives?
do you want to have long term

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growth, or do you want to have short
term gains?
Do you want to have long term
customer relationships, or do you
just want to maximize your volume?
How do you want your brand
perceived in the market?
What is it that you want to do with
analytics and how far do you want to
go? Do you want to expose yourself
to possible negative press, like what
happened with Facebook, or with
Cambridge Analytica, or with others?
How do you decide on the boundaries
of analytics in your organization;
How do you plan to govern decision
making in your organization; how do
you actually keep control as a
business over decisions being made,
instead of basically making it
possible for technical analysts to
make decisions for you, and thereby
losing control of how your
organization is making decisions?
How are you going to organize it; are
you going to organize it as a center
of expertise that works with all
partners, or do you work with the
hub and spoke model, where you
have analytics deployed into different
functions?
How are you going to break down the
silos between different areas, to
have, for example, finance and
marketing working together to make
sure you’re not marketing to
customers that are unlikely to pay
their bills, or your marketing and
supply chain working together, so
that when you set up a marketing
campaign, you make sure that your
supply chain demand optimization is
aligned?
How can you best leverage business
knowledge in your decision-making
process?
How do you actually deploy it into
your process? How are you going to
bring the data, the business, the IT
platform, and the analytics together
to generate the value?
About Zeno Analytics
Zeno Analytics is an “Analytics Translator” and “Data Translator” that will help
you close the gap between reality and expectations of your advanced analytics
(Predictive & Prescriptive Analytics, Machine Learning, AI) investments &
projects.
By applying an incremental approach, connecting business, analytics and IT,
Zeno Analytics takes you closer to you objective step-by-step. This approach
allows for an agile approach to gaining business value and adapting the
organization and processes to create maximum leverage from your data &
analytics initiatives.
You can contact us at:
info@zenoanalytics.com
www.zenoanalytics.com