The Power of Prediction: Predictive Analytics, Workplace ...
Power Of Analytics
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Transcript of Power Of Analytics
Power of AnalyticsStartups Special
Nitin GodawatDeciDyn 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
3
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
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
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)
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
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
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
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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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
• 1-10 years of experience
• 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
• 1-10 years of experience
• M.Tech/B.Tech/MCA
• Java, C++, SQL, Python
• Good understanding of
databases
• 1-10 years of experience
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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
16
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
Do visit our site atwww.DeciDyn.com