Machine Learning in Customer Analytics
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Transcript of Machine Learning in Customer Analytics
Machine Learning in Customer Analytics
January 23, 2014 | Proprietary and Confidential
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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.
Transformation Through Integration: Realizing
the Full Potential of Your Information
What is Machine Learning?
Machine learns
patterns in the training
data using input
features
Patterns learned
applied to unseen data
to ensure generalization
Regression or
classification performed
If generalization fails,
input features modified;
more training data fed to
algorithm
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Machine Learning Comes of AgeThe era of Analytics 3.0 combines structured
transactional data and unstructured text data with
complex machine learning algorithms to generate
better and faster insights
Key Technology Enablers for
Machine Learning
• Better and inexpensive storage capacities
• Increased processing power of machines
• Large scale availability of data
• Open source revolution
• Advent of Hadoop ,NoSQL technologies
Key Business Enablers for
Machine Learning
• Applications in unconventional fields
thus gaining wider acceptance
• Organizations have higher analytics
maturity curve
• Lower implementation cost
Analytics 1.0 • Implementing business intelligence
• Reporting
• Descriptive Analytics
• Focus on internal, structured data
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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From Science to Enterprise – How Big Data is Assisting Machine Learning
• 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
Telephonic conversation
Sensors used
to gather
information
Transaction records
Unstructured data comes from multiple sources:
Emails and
feedbacks
CDR data
(Telecom)
GPS data
(from
mobile
devices)
CCTV camera
dataDigital pictures
and videos
posted online
Posts to social media sites
Access
Logs
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
What can Machine Learning Do for Business?
With machine learning everybody wins
Learn – Algorithms and
computational models
to learn and gain
knowledge about users
Predict – Predictive
analytics to provide
actionable information
for organizations
Cloud Computing Big data
Natural Language
Processing –
Sentiment Analysis
Text Classification
Knowledge
Acquisition
Multilingual
language
processing
Algorithms
• Bayesian
Classifier
• Neural Networks
• SVM
Wide applications across industries:
• Recommender Systems
• Biotechnology
• Supply chain
optimization
• Product Marketing
• Counter-Terrorism
• Fraud Detection
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Use-Case: Machine Learning in Customer Analytics (Telecom)
STR
UC
TU
RED
UNSTRUCTURED
Network data
Call Data Records
GPRS Data Records
Contact Centre logs
Build single view
of customer
Analytics Engine
Next Best offer
Churn prediction
Campaign Mgmt
Social Network Analytics
Data
Aggregation
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Categories of Machine Learning Algorithms
Unsupervised Learning Algorithms: • Training dataset does not require labeled outputs.
• Function mapping from inputs to output not done.
• Objective is to understand structure in the data.
Examples:• Discovering different segments of telecom subscribers based on their call patterns and
data usage.
• Social Network Analysis: Discovering communities within large groups of people.
Supervised Learning Algorithms: • 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:• Classifying email as spam/not spam
• Predict loan default ( Yes/No)
• Forecast stock prices
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Advantages of Machine Learning
• Useful where large scale data is
available
• 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)
• Recommended for solving classification
and regression problems
• Ensures better profiling of customers to
understand their needs
• Helps serve customers better and
reduce attrition
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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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Challenges in Machine Learning Implementation
• Integration of data from different sources within the organization
• Good business understanding required to build better input features
• Thorough understanding of algorithms required before it can be
deployed
• Appropriate selection of machine learning algorithm essential
• Implementing algorithms which can give more business
interpretability and insights
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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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Case Studies
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Case Study: Gender Prediction Using Supervised Learning Algorithms
• 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.
Challenge
Approach
• 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
• 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
Result
Mach
ine L
earn
ing
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Case Study: Incentivizing existing policies for a leading Insurance Company
• 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
Challenge
Approach
• 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
• 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
Result
Mach
ine L
earn
ing
& P
red
ictive
Analy
tics
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Case Study : Applying face recognition to enable multiple applications
• Design a face detection and recognition algorithm for applications across multiple domains
Challenge
Approach
• 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
• 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
Result
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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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Blueocean Analytics Service Areas
Customer Analytics Marketing Analytics Special Focus Areas
Focus on better customer
experience through enhanced
engagement
Develop and optimize marketing
strategies through smart
evaluation of programs
Specialized intelligent solutions
that keep pace with socio-
economic trends
• Customer Acquisition
• Portfolio Management
• Attrition/Churn Analysis
• Loyalty Management
• Customer Contact Analytics
• Customer Risk Analytics
• Others …
• ROMI
• Market Mix Modelling
• Simulated Pricing Models
• Promotion Analytics
• Product Analysis
• Others …
• 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
Big Data ServicesReporting and Smart
BI Services Datamart Solution
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Thank you
For more information:
Durjoy Patranabish
Senior Vice President
Eron Kar
Analytics Delivery Lead