Power Of Analytics

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This presentation was presented at Headstart event at IIM, Bangalore on May 09, 2009.

This presentation was presented at Headstart event at IIM, Bangalore on May 09, 2009.

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  • 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 2
  • 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” 3
  • 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 4
  • 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 7
  • 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 8
  • 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 9
  • 10. Is Investment in Analytics Worth? Visible ROI Predictive Analytics BPM/CRM/BI Back-Office Applications Middleware & Infrastructure Technologies Operational Systems Hardware 10
  • 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 11
  • 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 12
  • 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 13
  • 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 14
  • 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 15
  • 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! 16
  • 17. For any clarifications, feel free to contact the author at Nitin.Godawat@decidyn.com Do visit our site at www.DeciDyn.com 17