The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities. Next DSS MIA Event - https://datascience.salon/miami/

1. Adopting Machine Learning to
Drive Revenue and Market Share
David Frigeri | Advanced Analytics & Data Visualization | david.Frigeri@slalom.com

3. We Need to
Answer Before
Competitors.
We need answers
quickly; we don’t have
the time, resources or
ability to wait months
for analytic reports or
project results. Our
Competitors have the
same Opportunities.
We Want to Use
More Data to
Identify Change in
Preference.
We would like to see
insights from beyond
what is isolated inside
our 4 walls of a
business (weather
economic conditions,
blogs, social media,
environmental factors).
We Want to Predict
AND Impact
Outcomes.
We need prediction and
simulation capabilities to
really understand what
actions are likely to result
in our desired outcome like
increased sales,
profitability, customer churn
We Need Better
Alignment.
We need to connect our
data initiatives with the
corporate strategy, we
need to educate and drive
awareness across the
enterprise and we need
new interdepartmental
processes to acquire
insights.

4. In 2018, more than half of
large organizations
globally will compete
using advanced
analytics and
proprietary algorithms,
causing the disruption of
entire industries.
- Gartner
Serious AI adopters with
proactive strategies report
current profit margins
that are three to fifteen
percentage points
higher than the industry
average in most sectors,
but they also expect this
advantage to grow in the
future .
- McKinsey Global Institute
Up to 45% of work
activities can be
automated with current
machine learning
capabilities and
potentially up to 60%
depending on advanced
in Natural Language
Processing.
- US Bureau of Labor Statistics
What are the Potential Business Impacts?

5. Expectations for AI Impact on Processes
To what effect will the adoption of AI affect your organization’s processes today and five years from today?
Industry
0% 10% 20% 30% 40% 50% 60% 70% 80%
Percentage of Respondents Who Expect (”a lot” or “great”) Effect on a Five-Point Scale
Overall
Technology, Media, Telecom
Consumer
Financial Services
Healthcare
Industrial
Energy
Large Effect: Today Large Effect: 5 Years
Source: Boston Consulting Group

6. Expectations for AI Impact on Offerings
To what effect will the adoption of AI affect your organization’s processes today and five years from today?
Industry
0% 10% 20% 30% 40% 50% 60% 70% 80%
Percentage of Respondents Who Expect (”a lot” or “great”) Effect on a Five-Point Scale
Large Effect: Today Large Effect: 5 Years
Source: Boston Consulting Group
Overall
Technology, Media, Telecom
Consumer
Financial Services
Healthcare
Industrial
Energy

8. BACKGROUND
Retirement services was seeking to
optimize the way they engage with
their customers through advanced
analytics.
They realized that a “one size fits all”
approach to their prospects and
customers does not yield the best
long term FA relationships.
PROJECT
Build predictive models that would
allow internal sales teams to
optimize how they engage with
each of the 3 customer types:
Prospects, Leads, and Producers.
RESULT
Gained a much deeper
understanding of their customer
population allowing their internal and
external sales teams to focus on the
most likely conversions based on
the models results.

9. Changing Customer Behavior with Machine Learning
BACKGROUND
Goal: Increase prescription
adherence by improving a patient’s
Health Index Score that was
considered a precursor to increased
prescription adherence.
PROJECT
Program: Created a Health Index
Score that was a weighted
calculation of multiple data features
such as insurance product,
demographics and income, MSA,
disease, commute time etc.
RESULT
Analytics: Utilized advanced
neural networks changing inputs
and weights to optimize the
Health Index Score.

10. Create New Sources of Revenue by Selling Insights to
Your Customers & 3rd Parties
BACKGROUND
A large manufacturer was looking
to monetize sensor data coming
off their GPS enabled machines
into B2B products via a data-as-
a-service sharing platform as well
as iOS iPhone/iPad apps for their
B2C customers.
PROJECT
Utilizing AWS, designed a “data
lake” environment including
NoSQL data modeling for B2B
partners to have access to a
common data environment for
data sharing and to conduct
predictive analytics on spatial-
temporal based data.
RESULT
Customers pay a subscription to
gain access to unforeseen insights
including being able to directly
correlate their product inputs to
operational outcomes seen in the
platform driving future product
decisions.

11. BACKGROUND
Modernize its data storage and
analytics infrastructure, capture
big data elements previously
unavailable for analysis, optimize
its data processing pipelines, and
enable predictive modeling.
PROJECT
Implemented a Data Lake S3
environment and loaded into
Amazon Redshift for further data
analysis.
Utilized Spark for data science
and advanced analytics model
training and implementation.
RESULT
• Targeted customer offers based
on website activity
• Single point-of-access for all
enterprise-wide data
• Ability to scale far beyond
existing system capacity

12. Infrastructure and Data to Predict Changes in
Customer (Listener) Preferences
BACKGROUND
The major music label wants to
ensure that their Systems and
Architecture can support the
increased flow of consumer data
and need for analytics,
especially data from streaming
partners like Spotify and iTunes.
PROJECT
Dashboards that visualize 70+
billion rows (and growing) in
seconds.
Robust streaming of
consumption data for clustering
and recommendations.
RESULT
The creation of a robust data
integration platform built in
SparkSQL & Redshift.
The solution met client SLAs for
ingestion and reporting and
continues to scale to +100 Billion
rows in Redshift.

13. Using Natural Language Processing to Reduce Costs of
Manual Labor and Identify Hidden Revenue
BACKGROUND
A global specialty insurance
provider established a data
science team that aims to
leverage policy and claims data
to improve business outcomes,
including improved productivity
and being more responsive to
customers.
PROJECT
Natural language processing,
OCR and machine learning to
analyse documents attached to
past claims in order to
determine the level of
complexity for each new claim.
RESULT
For claims automation, built
machine learning models that
would classify new claims into a
complexity category.
For pre-approved quotes,
generated pre-approved quotes
to be used by brokers.

14. Identifying Insurance Cross-sell Opportunities Using Recommendation Engines
BACKGROUND
Objective to develop a proof of
concept that would showcase
the possibilities around using
advanced analytics to suggest
the next best insurance product.
PROJECT
Data scientists extracted data
on policies for the past two
years and used best in class
collaborative filtering algorithm
to suggest the product that
could be paired with an existing
policy.
RESULT
Project results included a list of
current policies that were paired
with the next best opportunity
and a probability that the
suggested product is a good
match.

15. Contacting the Right People at the Right Time with the Right Message
BACKGROUND
Drive forecasted script writing
volume for a seasonal
medication, the KPI was
telesales’ reach-rate.
PROJECT
Program: Improve the ability of
telesales to optimize reach-rate
and to calibrate the call plan
activities with other channel
activities
RESULT
Analytics: Optimized call lists
and probability scores for
reaching each account on the
target list on a bi-monthly basis.

16. Advanced Analytics Quality of Service and Customer Satisfaction
BACKGROUND
Proof of concept to explore the
impact of network performance
on customer experience as it
relates to number of
dispositions, outages, truck rolls
and churn.
PROJECT
Collected network performance
data and built models that
explain the factors leading to
negative customer experience.
RESULT
The outcome of the project included
a set of models that explain key
factors in network performance that
drive customer experience.

17. Improving Revenue Forecasting With New Statistical Techniques
BACKGROUND
The analysts needed an
improved forecasting process
that both reduces the impact of
human error in data input and
that is more accurate in terms of
future projections.
PROJECT
To improve the accuracy of the
model, built more granular
models at the major product
categories, regions and key
client levels.
RESULT
In the end, the team delivered a
set of statistical models that
would forecast the revenue and
cargo weight for all major
products, regions and key clients.

19. Elements of Successful Machine Learning Introductions
19
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption

20. Elements of Successful Machine Learning Introductions
20
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption

21. Introduction to Business Imperatives
Revenue
Lifetime Value
Lead/Conversion
Churn/Sentiment
Marketing Mix
Segmentation
Bundling/Cross-sell
Efficiency
Real-time Alerts
Predictive Maintenance
Automate Business Rules
Resource Optimization
Demand Forecasting
Supply Chain Optimization
Innovation
Digitization/Categorize
Customer Self-service
Decision Support Insights
Recommendation Engine
Customized Customer Offerings
Anticipatory Customer Offerings

22. Elements of Successful Machine Learning Introductions
22
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption

23. Multifunctional data storage.
Often includes Raw Landing
Zones, Data Discovery
Zones, and Golden Records
1
Single source of truth for
data consumers. Curated
data accessible for multiple
purposes and parties
2 Hot, Warm, and Cold data
can be economically stored
with a single provider
3

24. Elements of Successful Machine Learning Introductions
24
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption

25. Tools & Skills
25
Development
Languages
Algorithm
Libraries
Open Source
Software
Education Skills
• Python
• R
• Scala
• Java
• PySpark
• MLlib
• H.20
• SciKit-Learn
• Weka
• KNIME Analytics
• TensorFlow
• Amazon ML
• Apache Spark Mllib
• Apache Mahout
• PyTorch
• Caffe
• Jupyter
• Zepplin
• Inferential
Statistics
• Linear Algebra
• Probability Info
Theory
• Numerical
Computation
• Multivariate
Analysis
• Time-Series Data
• API
• SQL Query
• Classification
• Regression
• Computer Vision
• Natural Language
Processing
• Recommendation
• Graph/Influencer

26. Define
Design
Model
Test
Learn
OutcomesData
Agile
Experimentation and Openness