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

durjoy.p@blueoceanmi.com

Eron Kar

Analytics Delivery Lead

eron.k@blueoceanmi.com

analytics@blueoceanmi.com