Power Of Analytics

Power of AnalyticsStartups Special

Nitin GodawatDeciDyn Systems

May 2009

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Today’s Menu

Starters� Analytics – An Introduction

� Example from Financial Services

Main Courses� Why Analytics?� Users of Analytics

� Increasing Use of Analytics

� Analytics – Tools and Techniques� Is Investment in Analytics Worth?

� The Next Wave & The Enablers

Dessert� Some More Examples

� Careers in Analytics

Finally A Candy� For Small & Medium Enterprises

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Analytics – An Introduction

Business Question

HistoryWhat is the revenue from a campaign?

Queries/ Reports

OLAP

Which age group had

the highest response? Drill down

Data Analytics

How are customers likely to

respond to the next offer? Adds prediction

Prediction, personalization and optimization

Advanced Data Analytics

How do I deliver a

personalized offer with the highest ROI within my budget?

“Data Analytics is a combination of art and science to understand,

predict and influence customer’s behaviour”

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Example from Financial Services

Acquisition Attrite

Involuntary Closures

Solicit,

Discount,

Advertisement

Solicit,

Discount,

Advertisement

MarketingMarketing RiskRisk OperationsOperations CollectionsCollections

Credit Line Increase /

Decrease,

Purchase Authorization,

FC/ Late Charge Waivers

Credit Line Increase /

Decrease,

Purchase Authorization,

FC/ Late Charge Waivers

Complaint Calls,

Request Calls,

Waiver Calls

Complaint Calls,

Request Calls,

Waiver Calls

Time

Activity Level

Welcome Campaigns,

Discounts,

Demographic Profiling,

Triggers,

Customer Value Based Campaigns

Welcome Campaigns,

Discounts,

Demographic Profiling,

Triggers,

Customer Value Based Campaigns

Credit

Approval,

Credit Line

Credit

Approval,

Credit Line

Application,

Activation

Application,

Activation

S2S Cross SellS2S Cross Sell

Call Frequency,

Call Timing

Call Frequency,

Call Timing

Credit Line

Decrease,

Credit Line Freeze

Credit Line

Decrease,

Credit Line Freeze

Reactivation,

Cross Sell

Reactivation,

Cross Sell

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Today’s Menu










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Few Facts• By 2010, 1.6 billion users are expected to come online (Imagine the amount of

clickstream data that’s going to be generated!)

• 40 billion personal emails, 17 billion alerts and a further 40 billion spam emails are sent

each day (What’s the requirement for server space, broadband??)

• Visa and Mastercard had approximately 90 billion purchase transactions in 2007

• The digital universe in 2007 was estimated at 281 exabytes (EB) and is projected to be

nearly 1.8 zettabytes (ZB) in 2011

• In healthcare, the Enterprise Research Group estimated that compliance records

exceeded 1,600 petabytes in 2006

• Chevron's CIO says his company accumulates data at the rate of 2 terabytes –

17,592,000,000,000 bits – a day

• Wal-Mart - reputed to have the largest database of customer transactions in the world.

In 2000, database was reported to be 110 terabytes, with recordings and storage of

information on tens of millions of transactions a day. By 2004, it was reported to be half

a petabyte (1 PB)

1 PB – 1 million GB 1 EB = 1 billion GB 1 ZB = 1 trillion GB

Do I still need to answer ‘Why Analytics’?

Source: Publicly available information

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Users of Analytics

Retail, Store and

Supply chain

Pharmaceuticals

Consumer

Products

Telecommunications

Hospitality and

Entertainment

Industrial

Products

Financial

Services

Transport

E-Business

and

Web Analytics

Users of Analytics

Pfizer, GSK

Wal-Mart

Tesco

JC Penney

Google

Yahoo

Amazon

AT&T, BT,

Sprint

Barclays Bank

Capital One

MBNA

Procter & Gamble

Unilever

Harrah’s International

Marriot International

Boston Red Sox

CEMEX

John Deere

FedEx

UPS

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Increasing Use of Analytics

15% of top performers versus 3% of low performers indicated that analytical capabilities are a key

element of their strategy.

No analytical capability

Minimal analytical capability

Some analytical capability

Above average analytical capability

Analytic capability is a key element of

strategy

12%

0%

33%

8%

27%

37%

19%

47%

9%10%

Source: Accenture study of 205/392 companies

2002

2006

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Analytics Tools and Techniques

Techniques range from ‘easy to understand’ to incomprehensible

• Exploratory Analysis (Distributions, Ratios, etc.)

• Objective Segmentation Techniques

• Non-objective Segmentation

• Regression, Time-Series Models

• Pattern Recognition, Text Mining

• Advanced Techniques (e.g. Neural Net, SVM, GA)

Easy

Hard

Analysis Tools

• SAS, SPSS, R

• Knowledge Studio

• Model Builder, KXEN

• Octave/Matlab

• Crystal Ball

Business Intelligence Tools

• SAS BI

• Hyperion

• Business Objects

• Cognos

• Palo

Miscellaneous Tools

• Campaign

Management: Unica

• Google Analytics

• Oracle, SAP, etc.

have basic analytics capability

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Is Investment in Analytics Worth?

Hardware

Operational Systems

Middleware & Infrastructure Technologies

Back-Office Applications

BPM/CRM/BI

Predictive Analytics

Visible ROI

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The Next Wave & The Enablers

• Intelligent Datawarehousing: Embedded with Analytics capability

• Understanding Unstructured Data: Pattern & Image Recognition, Text

Mining, Speech Analytics

• Faster Processors, Grid/Parallel Computing

• In-memory Analytics

• Personalization: Customized Recommendation at Individual Level

• Real-time Analytics, Web 3.0

• Extensive Research on Artificial Intelligence/Machine Learning Techniques

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Today’s Menu










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Some More Examples

� Retail Sales Analysis: Correlate sales with weather pattern and decide how much to stock a

particular item

� Fraud Detection Applications: To track certain factors that define a credit card user’s

fraudulent behavior. If the owner of the card usually travels in known regions of the world, but

card usage starts appearing in other geographical regions, that spending pattern could

indicate someone other than its owner is using that card.

� Quality Analysis in the Manufacturing Process: Predicting when a piece of equipment will

fail given the factors that existed when similar equipment failed in the past.

� Fighting terrorism: Authorities can monitor data banks for information like a suspicious

person’s visa status and firearm registration, and then extrapolate from that data to see if the

individual in question fits a common terrorist’s behavior profile.

� “People You May Know”: Facebook and Linkedin suggests people that a user may know

� Recommender System: Amazon recommends products/books based on your surfing

behaviour and past transactions

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Careers in Analytics

MIS Developers

MIS Developers

Statistical/ Mathematical/OR

Modelers

Statistical/ Mathematical/OR

Modelers

Market Research AnalystsMarket Research Analysts

Domain Consultants

Domain Consultants

Software DevelopersSoftware Developers

Database Consultants

Database Consultants

Well-rounded

Analytics Professional

• MBA/M.Tech/B.Tech/MCA

• SAS, SQL, Excel, VBA

• OLAP Tools like Cognos,

Business Objects, etc.

• 1-10 year of experience

• PG in Stats/Eco/Maths, B.Tech

• SAS, SPSS, R, Knowledge Studio

• Neural Net, Genetic Algorithm,

SVM, KNN, etc.

• 1-10 years of experience

• M.Tech/B/Tech/MCA

• Oracle, SQL Server, ETL, etc.

• Database Design/Optimization


• MBA or Any PG

• Experience of one industry like

Retail, Financial Services, etc

• 5+ Experience in Operations Role

• MBA/BBA/MA(Eco)

• Market/Domain Understanding

• Understanding of Survey and

MR tool


• M.Tech/B.Tech/MCA

• Java, C++, SQL, Python

• Good understanding of

databases


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Today’s Menu










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For Small & Medium Enterprises

Quick Solutions

Advance Solutions

� Set up a comprehensive Management Information System

� Analyze Cause and Effect - Try Fish Bone Diagram

� Apply 80:20 rule (Pareto) – It works!

� ‘Champion-Challenger’ approach. e.g. Price Discovery

� Integrated Campaign Management System with Web Analytics

� Develop Customer Profiles based on demographic information

� Identify Product Bundles using Market Basket Analysis

� Analyze Click-stream data to build intelligent website

� Use Recommendation Engine for online and offline campaigns

� Apply Text Analytics to convert unstructured data into structured one

� Optimize Web Pages using heat maps, etc

� Use Web Crawling and Text Analysis to gain Competitive Market Intelligence

� Carry out Social Network Analysis to engage customers/prospects

� Perform Optimization to reduce inventory, save costs, etc.

Data, Data and More Data…Use Data for Decisions!

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For any clarifications, feel free to contact the author at

[email protected]

Do visit our site atwww.DeciDyn.com