Strategic Statistics


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Strategic Statistics

  1. 1. SUNZ Annual Conference 2007 A Big Thank You, to Our Sponsors
  2. 2. Dr. Paul Bracewell 29 th November '07 Strategic Statistics Navigating Analytical Politics
  3. 3. Overview <ul><li>Statistics in an analytical framework </li></ul><ul><li>Key analytical players defined </li></ul><ul><li>Analytical ‘soup’: how the players mix </li></ul><ul><li>Politics and success </li></ul><ul><li>Communicating the message </li></ul>
  4. 4. Define Analytics <ul><li> </li></ul><ul><li>“ the extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions.” </li></ul>Davenport and Harris, 2007, p. 7 Competing on analytics: the new science of winning
  5. 5. Key Players <ul><li>Data Expert </li></ul><ul><li>Analyst </li></ul><ul><li>Power Consumer </li></ul><ul><li>Sponsor </li></ul><ul><li>Analytical Infrastructure </li></ul>
  6. 6. ‘ Internal’ Definitions/Perceptions <ul><li>“ There are three kinds of lies: lies, damned lies, and statistics” </li></ul><ul><li>Mark Twain, 1906 </li></ul><ul><li>Chapters from My Autobiography. North American Review 186 </li></ul><ul><li>“ Numbers don’t lie; people do” </li></ul><ul><li>Various </li></ul>
  7. 7. Interaction Between Players data expert analyst power consumer analytical infrastructure sponsor
  8. 8. Politics and success “ Politics is the process by which groups of people make decisions.” Analytics: “… drive decisions and actions.” (Davenport and Harris, 2007)
  9. 9. Interaction Between Players <ul><li>The sponsor gives equal weighting to comments of the three core entities. </li></ul><ul><li>Sponsor uses this ‘balanced’ view to inform the wider business about the project. </li></ul><ul><li>Analyst must satisfy requirements of data expert and power consumer to ensure right message is communicated to wider business </li></ul>Representation of strength and direction of interactions between core entities contributing to an analytical exercise.
  10. 10. Ability to Succeed Governed by “Sponsor” <ul><li>Likely to succeed if sponsor can guide analyst to deliver what is required. Sponsor able to “sell”. Ideal for ‘junior’ analysts. </li></ul><ul><li>Likely to succeed if sponsor can impart vision on analyst, and analyst can deliver. Sponsor able to “sell”. Ideal for ‘senior’ analyst. </li></ul><ul><li>Possibly succeed but reliant on ability of analyst to do work and sell to business. Best suited to senior analysts. </li></ul><ul><li>Likely to fail : results capped by sponsors knowledge - high frustration from analysts and business. </li></ul>1 3 2 4 HIGH LOW Level of Control Level of Understanding LOW HIGH
  11. 11. Communication <ul><li>Successful uptake requires understanding </li></ul><ul><li>Educating the business on analytics </li></ul><ul><ul><li>segmentation </li></ul></ul><ul><ul><li>visualisation </li></ul></ul><ul><li>“ A picture speaks a thousand words ” </li></ul>
  12. 12. Purpose of Segmentation? <ul><li>… the first step towards understanding individual customer behaviour… </li></ul><ul><li>Process: organisation -> interpretation -> action </li></ul><ul><li>Level: all customers -> meaningful groups -> individual </li></ul><ul><li>Builds a picture for the business </li></ul>
  13. 13. Multi-dimensional Behaviour <ul><li>Customers are complex </li></ul><ul><li>Instead of building one segmentation model to rule them all… </li></ul><ul><li>… model one behaviour at a time… </li></ul><ul><li>… and model many behaviours </li></ul><ul><li>Take the wider business along for the ride </li></ul><ul><ul><li>Builds trust </li></ul></ul><ul><ul><li>Business takes ownership </li></ul></ul><ul><ul><li>The analytics experience becomes favourable </li></ul></ul>
  14. 14. Risk/Reward Segmentation <ul><li>Customer Centric Approach </li></ul><ul><li>What does the customer do? </li></ul><ul><li>Business Centric Approach </li></ul><ul><ul><li>What impacts upon our bottom line? </li></ul></ul><ul><li>Business/Customer Overlap </li></ul><ul><ul><li>REWARD: the value of the customer’s behaviour </li></ul></ul><ul><ul><li>RISK: the chance that they will stop that behaviour </li></ul></ul>
  15. 15. Typical Features of R/R Segmentation Low Value High Value Low Risk High Risk Dormant Customers Ideal Customers Consistent Customers Inconsistent Customers Note: Consistent = Low Variability Inconsistent = High Variability Prevalent Behaviour (High Counts)
  16. 16. Organisation <ul><li>Self Organising Map clusters similar individuals in a meaningful way </li></ul><ul><li>Two (or more variables) define Risk and Reward of Customer Behaviour – these may need to be modelled (e.g. churn). </li></ul><ul><li>Clusters that are close are similar for one attribute, but not for another. </li></ul><ul><li>R/R Segmentation is a pre-cursor to life-stage analysis… (hints at where to start) </li></ul>
  17. 17. Building Map <ul><li>SAS Enterprise Miner defaults work well </li></ul><ul><li>Standardisation allows each piece of information to have an “equal say”… </li></ul><ul><li>Map structure important (rugby example) </li></ul><ul><li>If data is clean, well structured and has behaviours of interest, then it takes about 2-3 hours to build a suitable segmentation model, and about an hour to interpret. </li></ul><ul><li>4 Hours to create and deploy. </li></ul>
  18. 18. Statistical Significance <ul><li>For each segment, create indicator (Is customer in the segment or not? Repeat for all segments.) </li></ul><ul><li>Using demographic data (census), consumer survey data, and internal data fit stepwise regression model for each segment indicator – these are the key features that distinguish segment from rest of population. </li></ul><ul><li>Appropriate interpretation defines strategy: cross-sell, up-sell, pricing, retention, acquisition, cost reduction etc. </li></ul>
  19. 19. Practical Significance Acquisition Example <ul><li>Features that distinguish segment of interest : </li></ul><ul><ul><li>home owners </li></ul></ul><ul><ul><li>starting a family ( children < 2 years old ) </li></ul></ul><ul><ul><li>Well educated ( postgraduate qualification ) </li></ul></ul><ul><ul><li>Aged 28-45 </li></ul></ul><ul><ul><li>Earn >$80k </li></ul></ul><ul><ul><li>Have 2 or more cars </li></ul></ul><ul><li>“ Affluent up-and-coming families” </li></ul><ul><li>These features are used to score the population </li></ul>
  20. 20. <ul><li>Greater Auckland </li></ul>Action Deployment: Acquisition LOW HIGH Desire to Acquire
  21. 21. <ul><li>North Shore </li></ul>Action Deployment: Acquisition LOW HIGH Desire to Acquire
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  23. 23. SUNZ Annual Conference 2007 A Big Thank You, to Our Sponsors