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Adopting Machine Learning to
Drive Revenue and Market Share
David Frigeri | Advanced Analytics & Data Visualization | david.Frigeri@slalom.com
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.
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?
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
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
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Elements of Successful Machine Learning Introductions
19
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption
Elements of Successful Machine Learning Introductions
20
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption
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
Elements of Successful Machine Learning Introductions
22
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption
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
Elements of Successful Machine Learning Introductions
24
ML Applications Foundational Data Workflow & Automation
Expertise and Tools Agile Adoption
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
Define
Design
Model
Test
Learn
OutcomesData
Agile
Experimentation and Openness
Data Science Salon: Adopting Machine Learning to Drive Revenue and Market Share

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Data Science Salon: Adopting Machine Learning to Drive Revenue and Market Share

  • 1. Adopting Machine Learning to Drive Revenue and Market Share David Frigeri | Advanced Analytics & Data Visualization | david.Frigeri@slalom.com
  • 2.
  • 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
  • 7.
  • 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.
  • 18.
  • 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