Business Analytics Intro Talk at Sabanci University

Numeric Stories (Some) from the Field April 28 th , 2010

Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

About SnA ,[object Object],[object Object],[object Object],www.sna-consult.com

Cryptography ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],http://en.wikipedia.org/wiki/Cryptography

Crack the Code * http://www.wired.com/magazine/2010/01/ff_hideandseek/2/ # of Characters to Shift? Mnemonic Code The Caesar Shift The Book Code

Solving The Caesar Shift ,[object Object],*http://en.wikipedia.org/wiki/Caesar_cipher

Math Will Rock Your World ,[object Object],http://www.businessweek.com/magazine/content/06_04/b3968001.htm

Statisticians – Object of Desire ,[object Object],http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=1

Data Storage: Cost vs. Utilization http://www.economist.com/printedition/displaystory.cfm?story_id=15557443 http://dataspora.com/blog/the-data-singularity-is-here/

Internet Advertising in the UK ,[object Object],[object Object],* http://www.guardian.co.uk/media/2009/sep/30/internet-biggest-uk-advertising-sector ,[object Object]

Levels of Analytics Analytics What’s the best that can happen? What will happen next? What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Competitive Advantage Degree of Intelligence Reporting Decision Optimization Predictive Analytics Forecasting Statistical models Alerts Query/drill down Ad hoc reports Standard reports Competitive Advantage Competitive Advantage Competitive Advantage Degree of Intelligence Competitive Advantage Degree of Intelligence Competitive Advantage *Davenport, Competing on Analytics

Cost of Acquiring New Customers

Cost of Acquiring New Customers ,[object Object],[object Object],Post Paid Plan A Plan C Plan D Plan B Pre Paid Media Spend Other Marketing Initiatives Customer Type ARPU HIGH LOW

Marketing Mix Modeling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Marketing Mix Phases Early (Insights) Mid (Optimization) Advanced (Predictive) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Strategies from Marketing Mix Modeling ,[object Object],[object Object],Post Paid Plan A Plan C Plan D Customer Type Prime Time TV Direct Mail Online Adv. Print - News N/A Day Time TV DRTV N/A N/A Print - Mag Plan B Search Best Fit Pre Paid Notes: Blank cells represent channels that are a poor fit for an initiative “ N/A” in a cell represents a channel that does not align with an initiative and should not be considered N/A Updated

The Share of Wallet Question ,[object Object],[object Object],Post Paid Line 54% 24% Pre Paid Line 22% *Only for illustrative purposes

Predicting the Share of Wallet ,[object Object],*Only for illustrative purposes High (80% - 100%) Medium (50% - 80%) Low (20% - 50%) Transaction Level Data Share-of-Wallet Levels Customer Level Modeling with Latent Variables

Predicting the Share of Wallet ,[object Object],GSM Spend ($) $ 50 $ 85 Share of Wallet (%) 37 % 63 % *Only for illustrative purposes Add-on Services (#) 0 1

Social Network Analysis to Prevent Disconnections

Analyzing Detailed Call Data Total Call Volume Total Subscribers Total Disconnections ~ Billions ~ Millions ~ Hundreds of Thousands

Visualizing Social Networks Dataspora Presentation in Predictive Analytics World

Contact SnA Consulting Address: Sun Plaza Maslak Mah. Bilim Sk. No:5 K:13 34398 Istanbul / TR Tel: +90-212-366-0246 E-mail: info@sna-consult.com Web: www.sna-consult.com

Business Analytics Intro Talk at Sabanci University

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Business Analytics Intro Talk at Sabanci University