This document provides an overview of analytics and its increasing use in business. It discusses: - What analytics is and common business questions it can answer with different techniques. - Examples of how various industries use analytics in areas like marketing, risk management, and operations. - Data showing analytics adoption is growing as more companies recognize its benefits. - Different analytics tools and techniques ranging from basic to advanced. - Potential career paths in analytics fields like statistical modeling, software development, and domain expertise. - How even small and medium enterprises can leverage analytics for solutions like management systems, data-driven decisions, and online marketing optimization.

1. Power of Analytics
Startups Special
Nitin Godawat
DeciDyn Systems
May 2009

2. 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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3. Analytics – An Introduction
Business Queries/ OLAP Data Advanced Data
Question Reports Analytics Analytics
What is the revenue
History
from a campaign?
Which age group had
Drill down
the highest response?
How are customers likely to
Adds prediction
respond to the next offer?
How do I deliver a
Prediction, personalization and optimization
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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4. Example from Financial Services
Complaint
Complaint
Activity Calls,
Calls,
Level Request Calls,
Request Calls,
Waiver Calls
Waiver Calls
Credit Line Increase // Credit Line
Credit Line Increase Credit Line
Decrease, Decrease,
Decrease, Decrease,
Purchase Authorization, Credit Line Freeze
Purchase Authorization, Credit Line Freeze
FC/ Late Charge Waivers
FC/ Late Charge Waivers Call Frequency,
Call Frequency,
Welcome Campaigns,
Welcome Campaigns, Call Timing
Call Timing
Discounts,
Discounts,
Involuntary
Demographic Profiling,
Demographic Profiling,
Closures
Triggers,
Triggers,
Customer Value Based
Customer Value Based
Campaigns
Campaigns
Reactivation,
Reactivation,
Cross Sell
Cross Sell
Solicit,
Solicit,
Discount,
Discount,
Advertisement
Advertisement
S2S Cross Sell
S2S Cross Sell
Credit
Credit
Approval,
Approval,
Acquisition Attrite
Credit Line
Credit Line
Time
Application,
Application, Marketing Risk Operations Collections
Marketing Risk Operations Collections
Activation
Activation
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5. 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
5

6. 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)
Do I still need to answer ‘Why Analytics’?
Source: Publicly available information 1 ZB = 1 trillion GB 6
1 PB – 1 million GB 1 EB = 1 billion GB

7. Users of Analytics
Procter & Gamble
Unilever
Consumer
Products
Barclays Bank
AT&T, BT,
Financial
Capital One Telecommunications Sprint
Services
MBNA
Wal-Mart Retail, Store and Harrah’s International
Hospitality and
Tesco
Supply chain Marriot International
Entertainment
JC Penney
Boston Red Sox
Users of
Analytics
E-Business
Industrial
and
CEMEX
Products
Web Analytics
John Deere
Google
Yahoo
Amazon
Transport Pharmaceuticals
FedEx Pfizer, GSK
UPS
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8. Increasing Use of Analytics
15% of top performers versus 3%
of low performers indicated that
47%
analytical capabilities are a key
2002
element of their strategy.
2006
37%
33%
27%
19%
12% 10%
9%
8%
0%
No analytical Minimal Some analytical Above average Analytic
capability analytical capability analytical capability is a
capability capability key element of
strategy
Source: Accenture study of 205/392 companies
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9. Analytics Tools and Techniques
Techniques range from ‘easy to understand’ to incomprehensible
Easy
• Exploratory Analysis (Distributions, Ratios, etc.)
• Objective Segmentation Techniques
• Non-objective Segmentation
• Regression, Time-Series Models
• Pattern Recognition, Text Mining
Hard
• Advanced Techniques (e.g. Neural Net, SVM, GA)
Business Intelligence Miscellaneous Tools
Analysis Tools
Tools
• SAS BI • Campaign
• SAS, SPSS, R
Management: Unica
• Hyperion
• Knowledge Studio
• Google Analytics
• Business Objects
• Model Builder, KXEN
• Oracle, SAP, etc.
• Cognos
• Octave/Matlab
have basic analytics
• Palo
• Crystal Ball
capability
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10. Is Investment in Analytics Worth?
Visible ROI
Predictive Analytics
BPM/CRM/BI
Back-Office Applications
Middleware & Infrastructure Technologies
Operational Systems
Hardware
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11. 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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12. 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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13. 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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14. Careers in Analytics
Statistical/
MIS
Mathematical/OR
Developers
Modelers
• MBA/M.Tech/B.Tech/MCA
• PG in Stats/Eco/Maths, B.Tech
• SAS, SQL, Excel, VBA
• SAS, SPSS, R, Knowledge Studio
• OLAP Tools like Cognos, • Neural Net, Genetic Algorithm,
Business Objects, etc. SVM, KNN, etc.
• 1-10 year of experience • 1-10 years of experience
Software Database
Developers Consultants
Well-rounded
• M.Tech/B.Tech/MCA
M.Tech/B/Tech/MCA
•
Analytics
• Java, C++, SQL, Python
Oracle, SQL Server, ETL, etc.
•
• Good understanding of
Professional Database Design/Optimization
•
databases
1-10 years of experience
•
• 1-10 years of experience
Market Research Domain
Analysts Consultants
• MBA/BBA/MA(Eco) • MBA or Any PG
• Market/Domain Understanding • Experience of one industry like
• Understanding of Survey and
Retail, Financial Services, etc
MR tool • 5+ Experience in Operations
• 1-10 years of experience Role
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15. 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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16. For Small & Medium Enterprises
Quick 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
Advance Solutions
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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17. For any clarifications, feel free to contact the author at
Nitin.Godawat@decidyn.com
Do visit our site at
www.DeciDyn.com
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