"The reality of the battlefield is that you don't study it. We just do what we can to apply what we know. Therefore, to do a little, you have to know a lot and well" - Marshal Foch Customer Intelligence will dominate the new decade (new intelligence solutions are being built every day such as Oracle, SAS, IBM or data clean rooms), as brands develop more sophisticated machine learning models and proprietary algorithms to extract insights, information about their customers and their future behaviors, and make the best marketing decisions in real-time. Since Consumer Intelligence is at the heart of every smart marketing decision, what are the options for marketers to truly understand their customers and their behaviors?

1. Customer
Intelligence
Platform
Encourage the
Presented for:
By: Naully Nicolas
November 24th 2020

2. 22 0 1 9 . A L L R I G H T S
Welcome
Hello! My name is Naully.
digital consultant, analyst, author, and digital
historiographer.
I makes sense of digital trends with historical
lessons from the past!

3. WHAT IS
DIGITAL INTELLIGENCE

4. DIGITAL INTELLIGENCE
Digital intelligence involves understanding your customers and how they’re
using your website, mobile site or mobile app, then using this data to optimize
their experience no matter when, where or how they interact with you. In
today’s mobile, multi-device and multi channel world, digital intelligence is
the ability to transform digital data into real time , actionable, customer-
centric insights.

5. DATA TRANSPARENCY
The more accountability of data, the more possibilities will be unlocked and
the opportunity for greater market value and transformation of business will
be increased.

7. Proper use of
“Intelligence”

8. 82 0 1 9 . A L L R I G H T S
“Good intelligence is nine-
tenths of any battle.
- Napoleon Bonaparte

9. 92 0 1 9 . A L L R I G H T S
Proper use of “Intelligence”
It is quite possible that his understanding of the importance of
intelligence has derived from actual combat experience.
 Newer methods of gathering customer intelligence mean you can get to
know what your customers require in real time and address it as soon as
possible.
 Customer intelligence enables you to monitor your customer journey and
identify the areas that you can make more efficient so that your buyers have
the best experience when doing business with you. If they go through this
smoothly, they are more likely to return and become loyal to the brand.
 Customer intelligence provides you with a constant stream of information
about your customers and the industry at large. This means you are up-to-
date with the goings-on at every level of your target market. This real-
time customer intelligence provides you with insights that you can use to
make quick decisions.

10. 102 0 1 9 . A L L R I G H T S
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 Building Relationships With Your Customers
Proper use of “Intelligence”
 Deeper Understanding Through Analytics
 Social Connection With Customers

11. Harmful use of
“Intelligence”

12. 122 0 1 9 . A L L R I G H T S
“ Many intelligence reports in war are
contradictory; even more are false, and most
most are uncertain .... In short, most
intelligence is false.” - Carl Von Clausewitz,
On War (1832)

13. 132 0 1 9 . A L L R I G H T S
Harmful use of
“Intelligence”
 Research reveals that 80 % of the data
obtained by today's businesses were rarely
actively used to make any enhancements
or modifications considered necessary by
the consumer
 That brings up the issue of the experience
of the customer. In the past, businesses
would look at their customer relationships
as something that began with outreach or
advertising and finished with a point of sale
 This is likely because the organization
did not invest in the proper resources to
use it. That's a big mistake in today's
digital world, and that's why many
businesses are left behind in the digital
transformation.
 Digital innovation has changed all of this,
allowing customers to think about what
they want and need. Now, the journey
does not end with a purchase.
 Digitally reasonable means are adopting
these new phases of the customer journey–
not as a new assignment, but as an
opportunity for growth.

14. Creating a “Digital”
Intelligence Platform

15. 152 0 1 9 . A L L R I G H T S
“The reality of the battlefield is that you don't
study there. We just do what we can to apply
what we know. Therefore, To do a little, you have
to know a lot and well.” – Marechal Foch

16. 162 0 1 9 . A L L R I G H T S
The Importance of a Customer
Intelligence Platform
Customer intelligence is the collection, identification, and
analysis of customer data, behaviors, and activities so you
can deliver great customer experience and make informed
decisions.
Customer Intelligence

17. 172 0 1 9 . A L L R I G H T S
Customer intelligence platform
A customer intelligent platform (CIP) is the next stage of
customer data management. It serves and connects business
users in sales, marketing, commerce, and service, linking
billions of data points across disparate data sources to
uncover insights.
 Resolving and de-duplicating core master data about
prospects, customers, accounts, locations, transactions,
preferences, and associated reference data.
 Matching data entities and new record types.
 Enriching the data with inferred or derived indicators
such as engagement, sentiment, value, and journey.
 Allowing a wide range of users to perform complex
analysis via an easy-to-use interface.
 Generating multiple unique customer views in real time.

18. 182 0 1 9 . A L L R I G H T S
Healthcare
 Understanding patient behaviors can
help a hospital system plan demand for
services, staffing levels, equipment
purchases, and medical supplies, from
vaccines to platelets, by location and
season.
 Because a CIP lets the hospital relate
its decisions to individual patients, it
can help administrators understand
how changing those decisions will
impact business outcomes and quality
of care.
Financial Services
 Using a CIP to identify patterns in
individual customer behaviors lets banks
put specific events in context, making it
easier for them to identify unusual or
suspicious activities that might indicate
fraud or assess other types of risk, such as
bad debt, in the broader context of what
they know about the customer.
 Banks can also use aggregated customer
data to see and recognize new fraud
techniques as they emerge. And by
combining individual and aggregated
data,

20. 202 0 1 9 . A L L R I G H T S
Data management
Pick up the latest tech news, and the top stories are likely to include
a major data breach scandal or talk about the coming restrictions on
international privacy. A hot topic is global data privacy concerns: the
digital universe, the data we produce and copy annually, is more
than doubled every two years, according to IDC, and data bits are
expected to exceed 44 trillion gigabytes by 2020.
For the company, although this unparalleled data explosion comes
with a new world of business opportunities, very real risks also
come with it. User privacy and protection will no longer remain an
afterthought as more regulations arise, and as large penalties are
levied on businesses found to be non-compliant.

21. Five Questions to
Ask About Your
Data management

22. 222 0 1 9 . A L L R I G H T S
Who deploys data?
Data security and governance have traditionally been difficult
topics of debate for decision-makers, as technologists
encourage business users to take ownership of "their"
information. Those debates are much more difficult today. As
business teams are called upon to innovate across various
channels with data, data deployment and management roles
are now shared by IT and business.

23. 232 0 1 9 . A L L R I G H T S
Who governs what?
This issue can be difficult. If you were to guess that it should
be controlled by the community that ingested the data, then
you would be partially right. From a consistency point of
view, the corporation also owns all data storage as well as the
business-related metadata associated with enterprise data. But
now the organization still owns the business context
governance (business and technological metadata) of the data
they ingest in order to ensure the enterprise data can be
incorporated (if necessary).

24. 242 0 1 9 . A L L R I G H T S
Where is data deployed?
Ask this question and you will certainly produce some lively
debate. You've definitely heard this phrase over and over
again if you've been shopping around for a cloud vendor: "All
your data should be with us." We suggest that you deploy
some of your data in the data pool, whether on-site or in the
cloud. But some of the data should be in sandboxes,
laboratories, and data marts as well. In addition, you should
be in your data warehouse with your tidy, organized and
trusted data.

25. 252 0 1 9 . A L L R I G H T S
Does everyone agree on data
definitions?
Business customers need to know where the information they
need is to be located and what the information entails. "As
Jeff Burk writes at Datanami, "The number, complexity, and
nature of accessible market data is growing rapidly, making it
extremely difficult to locate, grasp, and trust." Burk suggests
developing systems that help speak the same language for IT
and industry. A managed data catalog, for example, may
translate data into business words and attach and link
different data sets.

26. 262 0 1 9 . A L L R I G H T S
Are your people dedicated to
data governance?
As any administration, effective leadership is not assured by
bringing up the best technologies and procedures. It's the
people who decide if government is going to function in fact.
As part of a data governance system, the tools and systems
set up can only work if people keep them up to date.
Communication between IT and industry, any step of the way,
is important. When IT creates safe access to business data
and self-service analytics, it is the job of the organisation to
be transparent and honest about what works and what is not.
When developed, the organization will rely on IT as a
resource to handle processes. As for IT in order to build a
thriving, self-provisioning, and cost-effective empirical
environment, they should plan to take on fresh governance
positions.

27. 272 0 1 9 . A L L R I G H T S
Real-Time Decision
Analysis
Real-time analytics enables organizations instantly or soon after the
data reaches their system to obtain insights and act on information.
The analytics in real time applications answer questions in seconds.
With high velocity and low response times, they process large
volumes of data. Big data analytics in real time, for instance, uses
data in financial databases to inform trading decisions.
Analytics may be persistent or on-demand. When the customer
needs it, On-demand produces results. Users are constantly
updated when events occur and can be configured to automatically
react to those events. For example, if page load output goes outside
of preset parameters, real-time web analytics can update an
administrator.

28. Five Questions to
Ask About Your
Real-Time Decision
Analysis

29. 292 0 1 9 . A L L R I G H T S
What exactly do you want to
find out?
It's nice to assess the company's well-being first. Agree on
what KPIs are most important to your business and how they
are already evolving around the company. Study numerous
KPI examples and compare them to your own. Think about
how you want to further improve them. Will this
development affect you? Identify where they should make
improvements. There's no point in analyzing data if nothing
can be modified. But if you see a growth opportunity and see
that it is possible to dramatically enhance your business
performance, then a KPI dashboard software might be a good
investment to track your main performance indicators and
provide a clear overview of the data of your organization.

30. 302 0 1 9 . A L L R I G H T S
What standard KPIs will you
use that can help?
Think about it like this: with business intelligence, the
purpose is to easily see facts so that you can make profitable
choices to help your business succeed. When evaluating
results, the questions to ask would be the structure, the lens,
which helps you to reflect on particular facets of your market
reality.
Think about it like this: with business intelligence, the
purpose is to easily see facts so that you can make profitable
choices to help your business succeed. When evaluating
results, the questions to ask would be the structure, the lens,
which helps you to reflect on particular facets of your market
reality.

31. 312 0 1 9 . A L L R I G H T S
The right KPI examples for every
use case
KPI by Function
• Management
• Finance
• Sales
• Marketing
• Human Ressources
• Services & Support
• Procurement
• IT
KPI by Industry KPI by Platform
• Healthcare
• Logistics
• Manufacturing
• Retail
• Digital Media
• FMCG
• Energy
• Market Research
• Facebook
• LinkedIn
• Twitter
• Youtube
• Google Analytics
• Google Adwords
• Salesforce
• Zendesk

32. 322 0 1 9 . A L L R I G H T S
Which statistical analysis
techniques do you want to
apply?
There are dozens of methods for statistical analysis which
you can use. In our experience, however these 3 statistical
methods are most commonly used for business analysis:
• Regression Analysis – a statistical process for estimating the
relationships and correlations among variables.
• Cohort Analysis – it enables you to easily compare how different
groups, or cohorts, of customers, behave over time.
• Predictive & Prescriptive Analysis– in short, it is based on
analyzing current and historical datasets to predict future
possibilities, including alternative scenarios and risk assessment.

33. 332 0 1 9 . A L L R I G H T S
Who are the final users of
your analysis results?
Another critical thing with the data analytics affects the end
users of your research. It's who they are? When will the
findings be implemented? You've got to get to know the end
users, including:
•What they expect to learn from the data
•What their needs are
•Their technical skills
•How much time they can spend analyzing data?
Having all the answers will allow you to decide how
comprehensive your data report will be and what information
you should concentrate on.

34. 342 0 1 9 . A L L R I G H T S
What data visualizations
should you choose?
Your data is healthy, and your calculations are done, but
you're not done yet. You may have the world's most important
insights, but if they're poorly delivered, the target audience
won't get the effect you're looking for from them.
And we don't live in a world where the end of all, be all, is
merely getting the right data. Within your business, you have
to persuade other decision makers that this knowledge is:
1.Correct
2.Important
3.Urgent to act upon

35. 352 0 1 9 . A L L R I G H T S
AI and Machine
Learning
The phrases' Artificial Intelligence '(AI) and' Machine Learning '(ML)
have been very popular in recent years. AI here, ML there. In most
discussions about the future of technology, industry, the workplace
and even society itself, they play a prominent role. Therefore it is
not shocking that business and technology executives are deeply
interested in learning more about them and their future uses,
advantages and threats.
Although the buzz could be fresh about AI and ML, the underlying
technologies are not. In fact, over 100 years ago, their roots can be
found: linear regression already existed in the 19th century and in
the 20th century, neural networks were conceptualized. However,
only in the 21st century is sophisticated algorithms introduced to
real market problems, due to improvements in computing power,
computers reaching ever faster speeds, and the ubiquitous
availability of vast volumes of data.

36. Five Questions to
Ask About AI and
Machine Learning

37. 372 0 1 9 . A L L R I G H T S
Does the data even exist for
your model?
Historically, all the characteristics and actions it takes to
construct an accurate model have not always been obtained
by organizations. If more enterprises continue to understand
the need for data-driven automation, attempts are being made
to gather the correct kind of data to create applications for
machine learning. For example, an eCommerce merchant
may want to relate data about past orders, browsing habits,
and contact behaviors of consumers to create a model to
suggest a customer to take the next best step. To do this
requires a plethora of data, and usually only the biggest
distributors have the quantity of valuable data to retain.

38. 382 0 1 9 . A L L R I G H T S
Is the data clean?
Generally, it is agreed that inaccurate or low-quality input
would still yield defective results. Or to put more simply,
garbage in, garbage out.
A computer trained on bad data can learn the wrong lessons
from even the most fitting model, come to the wrong
conclusions, and struggle to work as you or your clients
expect. If you have good data at appropriate quantities, a
simple algorithm will have utility on the flip side.
What characterizes "poor" data? The details can be unrelated
to your question, wrongly annotated, misleading, missing, or
biased. It is imperative that any column is correctly classified
in a given training data set, and that any human annotators
who categorize or tag the data are free from biases.

39. 392 0 1 9 . A L L R I G H T S
Is the data structured?
A lack of access to data is one of the key reasons enterprises
fail to develop machine learning and AI-powered products.
And when organizations attempt to aggregate the diverse data
that their data analysis team uses to construct models, the
knowledge is also not organized in such a manner that models
can be developed around it.

40. 402 0 1 9 . A L L R I G H T S
Is the data easy to refresh?
Just the first step to training the model is an initial data dump.
If the model is part of a long-term approach, training the
model on updated data on an ongoing basis is crucial.
Crowdsourcing the necessary behaviors is an easy way to
ensure that you can build solutions for real human behavior
without diverting the vital resources of your engineering team
if you do not have the required number of users or data
pipeline.

41. 412 0 1 9 . A L L R I G H T S
Do you have enough data?
Think of machine learning data such as research data: the
larger and fuller your sample size, the more reliable the
findings will be. If the sample data set is not large enough all
variations will not be taken into consideration, and the
machine may reach inaccurate conclusions, learn patterns that
do not actually exist, or fail to recognize patterns that do
exist.

42. 422 0 1 9 . A L L R I G H T S
Integrated Analytics
How do businesses make sense of the huge amounts of data that
they accumulate? How can they make better business decisions by
leveraging that information? They employ Business Intelligence (BI)
and Analytics products, which use data visualization to uncover
actionable insights.
It is possible to think of data visualization as a visual interpreter for
data. This class of products can be used for descriptive analytics
(what has happened) or they can be used for predictive analytics
(what could happen) when combined with machine learning
models.

43. Five Questions to
Ask About
Integrated Analytics

44. 442 0 1 9 . A L L R I G H T S
How much data do we have?
Knowing how much data you have helps you determine
whether a traditional BI architecture is sufficient for the need
for modern BI architecture (such as a data lake). Make sure
you align the product you select with the expected usage.

45. 452 0 1 9 . A L L R I G H T S
How big is my company?
Small businesses probably do not need a high degree of
control for installations and deployments, whereas large
businesses need a high degree of control. For clustered,
closely managed installations and applications, some vendors
have a superior solution and tools.

46. 462 0 1 9 . A L L R I G H T S
How many people will have
access to analytics and reporting
capability?
This question helps to assess the license costs to be paid for
the solution to be offered.

47. 472 0 1 9 . A L L R I G H T S
Is there a data governance team
and process in place?
For a single source of the facts, a mature company would at
least have certain governance and stricter standards. It is
possible that these organisations will still provide systems to
require and supply access.

48. 482 0 1 9 . A L L R I G H T S
What type of dashboards and
reporting are needed?
If you try to grasp the degree of specificity required, this
question helps. In order to make informed strategic choices,
are you searching for reports and dashboards that are clearly
"directionally correct"? Or, are you looking for Board of
Directors/C-suite/Wall Street-level reporting?

50. What’s is next?

51. 512 0 1 9 . A L L R I G H T S
Future of Customer Intelligence
 Many companies have invested significant resources in
analytics, but are just realizing that they must also invest in
data governance and data management or they risk basing
decisions on bad data—which leads to bad results
 To deliver the more automated, more personal, and more
predictive services that customers expect, organizations need
a CIP driven by AI and machine learning to manage and
analyze data from multiple disparate sources at volumes and
speeds no humans can match
 AI will also be necessary to automate the back-office
processes and customer-facing interactions necessary to
deliver omnichannel and digital experiences that are
sufficiently accurate and personalized.

52. 522 0 1 9 . A L L R I G H T S
Future of Customer Intelligence
 These customer expectations are driving companies to seek
out advice from consultants and analysts about how best to
implement a customer intelligence platform to achieve
insights into individual customer behavior
 Both in the buying process and in their broader lives, will
help them further tailor the end-to-end customer
experience, from marketing offers to sales interactions, in
person or online, with goods or services, after the sale and
for renewals.
 The more accurately companies can identify and remove
friction points in their internal and external processes, the
better they become at creating experiences that deliver
what customers want when—and maybe even before—they
know they want it.

53. 532 0 1 9 . A L L R I G H T S
THANK YOU

54. HOW TO
REACH ME
Linkedin: Naully Nicolas
Twitter: @naullyn
Instagram: @naullyn
E-mail: sayhi@naullynicolas.ch