Learn the advantages and disadvantages of machine learning algorithms versus traditional statistical modelling approaches to solve complex business problems.

1. Machine Learning in
Customer Analytics
January 23, 2014 | Proprietary and Confidential

2. Transformation Through Integration: Realizing
the Full Potential of Your Information
blueocean is a next-generation services organization with a deep focus on analytics, market
intelligence and digital media, all uniquely delivered under one roof by 650 plus professionals.
Our 360 Discovery TM process ensures the comprehensive utilization of all available structured and
unstructured data sources, enabling us to bring the best to bear against each project.
By combining the talent, speed and cost benefit of a flat world, along with our scalable delivery
model, we are able to achieve a more nuanced and comprehensive understanding of the market at
the delivery speed and price advantage that today’s business climate demands.
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3. What is Machine Learning?
Machine learns
patterns in the training
data using input
features
Patterns learned
applied to unseen data
to ensure generalization
If generalization fails,
input features modified;
more training data fed to
algorithm
Regression or
classification performed
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4. Machine Learning Comes of Age
The era of Analytics 3.0 combines structured
transactional data and unstructured text data with
complex machine learning algorithms to generate
better and faster insights
Analytics 1.0
Implementing business intelligence
Reporting
Descriptive Analytics
Focus on internal, structured data
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Key Technology Enablers for
Machine Learning
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Better and inexpensive storage capacities
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Increased processing power of machines
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Large scale availability of data
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Open source revolution
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Advent of Hadoop ,NoSQL technologies
Key Business Enablers for
Machine Learning
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Analytics 3.0
• Combining structured and unstructured data formats
• Analytics central to the business strategy
• Faster technologies
• Analytics model embedded into operational and decision processes
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Applications in unconventional fields
thus gaining wider acceptance
Organizations have higher analytics
maturity curve
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Lower implementation cost

5. From Science to Enterprise – How Big Data is Assisting
Machine Learning
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Big Data Analytics offers access to speech, text and social analytics tools and expertise on demand
Machine Learning allows rapid processing of large amounts of customer centric data including customer
conversations in the form of calls, email, chat
Unstructured data comes from multiple sources:
CCTV camera
data
CDR data
(Telecom)
Digital pictures
and videos
posted online
Sensors used
to gather
information
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Telephonic conversation
Emails and
feedbacks
GPS data
(from
mobile
devices)
Transaction records Access
Logs
Posts to social media sites
To churn big data to actionable insights brings in new
practical and theoretical challenges:
Data Acquisition l Storage l
Processing l Data Transport and
Dissemination l Data Management
and Curation l Archiving l Security
l Analyzing for Business Actions

6. What can Machine Learning Do for Business?
Learn – Algorithms and
computational models
to learn and gain
knowledge about users
Cloud Computing
Natural Language
Processing –
Sentiment Analysis
Text Classification
Knowledge
Acquisition
Multilingual
language
processing
Predict – Predictive
analytics to provide
actionable information
for organizations
Big data
Algorithms
• Bayesian
Classifier
• Neural Networks
• SVM
With machine learning everybody wins
Wide applications across industries:
• Recommender Systems
• Biotechnology
• Supply chain
optimization
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• Product Marketing
• Counter-Terrorism
• Fraud Detection

7. Use-Case: Machine Learning in Customer Analytics
(Telecom)
Build single view
of customer
STRUCTURED
Network data
Analytics Engine
Call Data Records
Data
Aggregation
GPRS Data Records
Next Best offer
Churn prediction
Campaign Mgmt
Social Network Analytics
Contact Centre logs
UNSTRUCTURED
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8. Categories of Machine Learning Algorithms
Supervised Learning Algorithms:
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Training the machine on a training dataset with set of input features and a
corresponding output
Generalization: Machine learns a mathematical function which could be generalized
and applied to unseen data
Examples:
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Classifying email as spam/not spam
Predict loan default ( Yes/No)
Forecast stock prices
Unsupervised Learning Algorithms:
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Training dataset does not require labeled outputs.
Function mapping from inputs to output not done.
Objective is to understand structure in the data.
Examples:
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Discovering different segments of telecom subscribers based on their call patterns and
data usage.
Social Network Analysis: Discovering communities within large groups of people.

9. Advantages of Machine Learning
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Large scale deployments of Machine
Learning beneficial in terms of
improved speed and accuracy
•
Understands non-linearity in the data
and generates a function mapping
input to output (Supervised Learning)
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Recommended for solving classification
and regression problems
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Ensures better profiling of customers to
understand their needs
•
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Useful where large scale data is
available
Helps serve customers better and
reduce attrition

10. Disadvantages of Machine Learning
• Limited understanding of the
machinery of classifiers (Black Box)
• Requires significant amount of data
• May not work in cases where data
collection is difficult or expensive
• Problem of over-fitting if model fitted
on small dataset
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11. Challenges in Machine Learning Implementation
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Integration of data from different sources within the organization
•
Good business understanding required to build better input features
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Thorough understanding of algorithms required before it can be
deployed
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Appropriate selection of machine learning algorithm essential
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Implementing algorithms
interpretability and insights
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which
can
give
more
business

12. Statistics in the Age of Machine Learning
• Statistics: Mainly deals with probabilistic or deterministic approach
• Popular in fields where data collection can be difficult or
expensive in nature
• Provides good understanding of population where only sample
data can be collected e.g. Brand survey, quality control checks,
clinical trials
• Intuitively provides more understanding about drivers of the
objective function
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13. Case Studies
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14. Case Study: Gender Prediction Using Supervised
Learning Algorithms
Challenge
Machine Learning
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The client is a pioneer in measurement of mobile subscriber behavior
The metering application installed on smart devices captures behavior of the device accurately
The client wanted to predict gender of the subscribers based on installed mobile Applications
This information was to be used by advertisers in order to ensure focused and targeted marketing.
Approach
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Initial data provided by the client was a set of user IDs along with the application names
Data cleansing and transformations were performed in order to ensure data can be fed to a supervised learning
algorithm
The data provided was highly imbalanced and skewed towards males as it was the dominant class to be
predicted
Applied weighted measures to give more importance to the minority class
Support Vector Machines Learning Algorithm was applied to predict gender of the subscribers
Result
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Achieved accuracy close to 80% for both classes of interest
Developed an integrated solution with a GUI to enable real time results to be obtained based on real time data
feeds to the learning algorithm

15. Case Study: Incentivizing existing policies for a leading
Insurance Company
Challenge
Machine Learning & Predictive Analytics
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Approach
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The two policies Traditional and ULIP were in two states – In-force and Lapsed.
Data cleansing was done using a proprietary statistical tool
A binary logistic regression algorithm was applied on each of the policies with lapsed and in-force data
Result
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Access lapsed insurance policies having a potential of repayment (and hence reactivation) within a specific time
frame
Identify criteria to incentivize existing in-force policies
Predictors that influenced the predictive model were:
o Premium to be paid
o Income of the policy holder
o Occupation and total sum assured at the end of maturity
It was important to target lapsed policies within a specific time frame beyond which customers would be difficult
to be re-activated

16. Case Study : Applying face recognition to enable
multiple applications
Challenge
•
Design a face detection and recognition algorithm for applications across multiple domains
Approach
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Create a databases of faces and performed face detection using Haar cascades algorithm
Matched captured face images in the existing database of facial images of people. - We used face recognition
algorithms using Principle component analysis
Result
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Achieved accuracy close to 60% for face recognition and 70% for face detection
Can be applied to strengthening security measures in organizations, identifying and providing offers to repeat
customers in retail stores

17. In Summary
• With big data a reality machine learning is finding wider acceptance across
various industries
• Machine learning is paving the way to solve complex business challenges in an
efficient and effective manner
• To reap the benefits of machine learning it is essential to identify the areas
where it can be applied effectively
• Good business understanding is required to build smarter solutions
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18. Blueocean Analytics Service Areas
Customer Analytics
Marketing Analytics
Focus on better customer
experience through enhanced
engagement
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Customer Acquisition
Portfolio Management
Attrition/Churn Analysis
Loyalty Management
Customer Contact Analytics
Customer Risk Analytics
Others …
Special Focus Areas
Develop and optimize marketing
strategies through smart
evaluation of programs
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ROMI
Market Mix Modelling
Simulated Pricing Models
Promotion Analytics
Product Analysis
Others …
Specialized intelligent solutions
that keep pace with socioeconomic trends
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Collections Analytics
Real Time Analytics
Social Network Analytics
Telemetry
Visual Analytics
Speech and Text Analytics
Social Media Analytics
Others…
Data Management, Big Data and Smart Business Intelligence
Focus on creating a single source of “truth” and providing insightful analysis rather than plethora of reports
Datamart Solution
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Reporting and Smart
BI Services
Big Data Services

19. Thank you
For more information:
Durjoy Patranabish
Senior Vice President
durjoy.p@blueoceanmi.com
Eron Kar
Analytics Delivery Lead
eron.k@blueoceanmi.com
analytics@blueoceanmi.com
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