Business Analytics Intro Talk at Sabanci University

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Introductory presentation on Business Analytics for a talk at Sabanci University

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Business Analytics Intro Talk at Sabanci University

  1. 1. Numeric Stories (Some) from the Field April 28 th , 2010
  2. 2. Agenda <ul><li>SnA Introduction </li></ul><ul><li>Cracking the Code </li></ul><ul><li>Recent Focus on Business Analytics </li></ul><ul><li>Real Life Applications </li></ul><ul><ul><li>Cost of Acquiring Customers </li></ul></ul><ul><ul><li>Share of Wallet Question </li></ul></ul><ul><ul><li>Social Network Analysis to Prevent Disconnections </li></ul></ul>
  3. 3. About SnA <ul><li>SnA is a business analytics consultancy based in Istanbul, Turkey with active projects in the EMEA region. </li></ul><ul><li>SnA helps bluechips in Banking, Insurance, Telco and Retail understand their transaction level history and produce predictive analysis and forecasts. SnA's core capabilities include: </li></ul><ul><li>* Analytic CRM Strategy Development * Predictive Modeling for CRM * Customer Segmentation * Customer Life Time Value Analysis * Customer Attrition Analysis * Share of Wallet Prediction * Forecasting Sales and Attrition * Rate Making for Insurance </li></ul>www.sna-consult.com
  4. 4. Cryptography <ul><li>… is the practice and study of hiding information </li></ul><ul><ul><li>Modern cryptography intersects the disciplines of mathematics, computer science, and engineering </li></ul></ul><ul><ul><li>Applications of cryptography include ATM cards, computer passwords, and electronic commerce </li></ul></ul><ul><li>Crack the Code: </li></ul><ul><ul><li>Tree, Bicycle, Tripod </li></ul></ul><ul><ul><li>BWYA </li></ul></ul><ul><ul><li>974 132 321 </li></ul></ul>http://en.wikipedia.org/wiki/Cryptography
  5. 5. 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
  6. 6. Solving The Caesar Shift <ul><li>A simple histogram may help answer the question </li></ul>*http://en.wikipedia.org/wiki/Caesar_cipher
  7. 7. Math Will Rock Your World <ul><li>A generation ago, quants turned finance upside down. Now they're mapping out ad campaigns and building new businesses from mountains of personal data … </li></ul>http://www.businessweek.com/magazine/content/06_04/b3968001.htm
  8. 8. Statisticians – Object of Desire <ul><li>“ I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.” </li></ul>http://www.nytimes.com/2009/08/06/technology/06stats.html?_r=1
  9. 9. Data Storage: Cost vs. Utilization http://www.economist.com/printedition/displaystory.cfm?story_id=15557443 http://dataspora.com/blog/the-data-singularity-is-here/
  10. 10. Internet Advertising in the UK <ul><li>The UK has become the first major economy where advertisers spend more on internet advertising than on television advertising, with a record £1.75bn online spend in the first six months of 2009 </li></ul><ul><li>To put this in perspective, in 1998, when the IAB first measured internet advertising, just £19.4m was spent online </li></ul>* http://www.guardian.co.uk/media/2009/sep/30/internet-biggest-uk-advertising-sector <ul><li>The internet now accounts for 23.5% of all advertising money spent in the UK, while TV ad spend accounts for 21.9% of marketing budgets. </li></ul>
  11. 11. 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
  12. 12. Cost of Acquiring New Customers
  13. 13. Cost of Acquiring New Customers <ul><li>As new customers are acquired through marketing efforts; quantifying the impact of Media and Other Marketing Initiatives becomes a key challenge </li></ul><ul><li>Attributing the cost of acquisition for different customer types may help target more valuable customers (with higher Average Revenue per User) at the right channel </li></ul>Post Paid Plan A Plan C Plan D Plan B Pre Paid Media Spend Other Marketing Initiatives Customer Type ARPU HIGH LOW
  14. 14. Marketing Mix Modeling <ul><li>Applying statistical analysis techniques to understand the individual and combined contributions that multi-media marketing investments have on business results </li></ul><ul><li>Marketing mix models help: </li></ul><ul><ul><li>Estimate Return On Investment (ROI) associated with historical marketing spending </li></ul></ul><ul><ul><li>Forecast the prospective business results that future spending will generate </li></ul></ul><ul><li>Model are particularly applicable if </li></ul><ul><ul><li>Direct attribution of sales to marketing stimuli is weak </li></ul></ul><ul><ul><li>Consumer response is fairly long and complex </li></ul></ul><ul><li>Marketing mix models use time series data in a structural modeling approach to determine all the relevant independent marketing and non-marketing variables that affect business performance. </li></ul>
  15. 15. Marketing Mix Phases Early (Insights) Mid (Optimization) Advanced (Predictive) <ul><ul><li>Are diminishing returns being realized? </li></ul></ul><ul><ul><li>What are thresholds of spending? </li></ul></ul><ul><ul><li>What is the impact of a new channel? </li></ul></ul><ul><ul><li>What return (ROI) are specific channels providing? </li></ul></ul><ul><ul><li>What is the predicted impact of mix allocation? </li></ul></ul><ul><ul><li>What is the impact of a budget change? </li></ul></ul><ul><ul><li>What are optimal level of spend in channels? </li></ul></ul><ul><ul><li>Are specific campaigns working better than others? </li></ul></ul><ul><ul><li>What are the results of a structured test? </li></ul></ul><ul><ul><li>What is the true lag effect of ad dollars? </li></ul></ul><ul><ul><li>Does marketing work? </li></ul></ul><ul><ul><li>Do scaled channels work? </li></ul></ul><ul><ul><li>Is there evidence of subscale channels having impact? </li></ul></ul>
  16. 16. Strategies from Marketing Mix Modeling <ul><li>Optimizing marketing spend based on Channel Appropriateness is a “natural” output of the marketing mix modeling exercise </li></ul><ul><li>Shifting marketing spend towards media / initiatives that attract more “ valuable ” customers will be a key strategic initiative </li></ul>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
  17. 17. The Share of Wallet Question
  18. 18. The Share of Wallet Question <ul><li>Given the competitive landscape of the industry; subscribers are given incentives to distribute their spend across multiple service providers </li></ul><ul><li>Predicting the current level of a given customer’s spend to competitors help estimate Share of Wallet </li></ul>Post Paid Line 54% 24% Pre Paid Line 22% *Only for illustrative purposes
  19. 19. Predicting the Share of Wallet <ul><li>Multiple types of multivariate analysis techniques can be utilized to predict the Share of Wallet at the individual (GSM subscriber) level for all or selected subscribers </li></ul>*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
  20. 20. Predicting the Share of Wallet <ul><li>Predictive models can be developed during the analysis process and would assign a Share of Wallet score to each customer as well as the size of their wallet </li></ul>GSM Spend ($) $ 50 $ 85 Share of Wallet (%) 37 % 63 % *Only for illustrative purposes Add-on Services (#) 0 1
  21. 21. Social Network Analysis to Prevent Disconnections
  22. 22. Hypothesis <ul><li>If your friend disconnects from your common mobile operator you are more likely to disconnect… </li></ul>
  23. 23. Analyzing Detailed Call Data Total Call Volume Total Subscribers Total Disconnections ~ Billions ~ Millions ~ Hundreds of Thousands
  24. 24. Visualizing Social Networks Dataspora Presentation in Predictive Analytics World
  25. 25. Thank You
  26. 26. 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

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