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Strategic Statistics


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  • 1. SUNZ Annual Conference 2007 A Big Thank You, to Our Sponsors
  • 2. Dr. Paul Bracewell 29 th November '07 Strategic Statistics Navigating Analytical Politics
  • 3. Overview
    • Statistics in an analytical framework
    • Key analytical players defined
    • Analytical ‘soup’: how the players mix
    • Politics and success
    • Communicating the message
  • 4. Define Analytics
    • “ the extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions.”
    Davenport and Harris, 2007, p. 7 Competing on analytics: the new science of winning
  • 5. Key Players
    • Data Expert
    • Analyst
    • Power Consumer
    • Sponsor
    • Analytical Infrastructure
  • 6. ‘ Internal’ Definitions/Perceptions
    • “ There are three kinds of lies: lies, damned lies, and statistics”
    • Mark Twain, 1906
    • Chapters from My Autobiography. North American Review 186
    • “ Numbers don’t lie; people do”
    • Various
  • 7. Interaction Between Players data expert analyst power consumer analytical infrastructure sponsor
  • 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. Interaction Between Players
    • The sponsor gives equal weighting to comments of the three core entities.
    • Sponsor uses this ‘balanced’ view to inform the wider business about the project.
    • Analyst must satisfy requirements of data expert and power consumer to ensure right message is communicated to wider business
    Representation of strength and direction of interactions between core entities contributing to an analytical exercise.
  • 10. Ability to Succeed Governed by “Sponsor”
    • Likely to succeed if sponsor can guide analyst to deliver what is required. Sponsor able to “sell”. Ideal for ‘junior’ analysts.
    • Likely to succeed if sponsor can impart vision on analyst, and analyst can deliver. Sponsor able to “sell”. Ideal for ‘senior’ analyst.
    • Possibly succeed but reliant on ability of analyst to do work and sell to business. Best suited to senior analysts.
    • Likely to fail : results capped by sponsors knowledge - high frustration from analysts and business.
    1 3 2 4 HIGH LOW Level of Control Level of Understanding LOW HIGH
  • 11. Communication
    • Successful uptake requires understanding
    • Educating the business on analytics
      • segmentation
      • visualisation
    • “ A picture speaks a thousand words ”
  • 12. Purpose of Segmentation?
    • … the first step towards understanding individual customer behaviour…
    • Process: organisation -> interpretation -> action
    • Level: all customers -> meaningful groups -> individual
    • Builds a picture for the business
  • 13. Multi-dimensional Behaviour
    • Customers are complex
    • Instead of building one segmentation model to rule them all…
    • … model one behaviour at a time…
    • … and model many behaviours
    • Take the wider business along for the ride
      • Builds trust
      • Business takes ownership
      • The analytics experience becomes favourable
  • 14. Risk/Reward Segmentation
    • Customer Centric Approach
    • What does the customer do?
    • Business Centric Approach
      • What impacts upon our bottom line?
    • Business/Customer Overlap
      • REWARD: the value of the customer’s behaviour
      • RISK: the chance that they will stop that behaviour
  • 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. Organisation
    • Self Organising Map clusters similar individuals in a meaningful way
    • Two (or more variables) define Risk and Reward of Customer Behaviour – these may need to be modelled (e.g. churn).
    • Clusters that are close are similar for one attribute, but not for another.
    • R/R Segmentation is a pre-cursor to life-stage analysis… (hints at where to start)
  • 17. Building Map
    • SAS Enterprise Miner defaults work well
    • Standardisation allows each piece of information to have an “equal say”…
    • Map structure important (rugby example)
    • 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.
    • 4 Hours to create and deploy.
  • 18. Statistical Significance
    • For each segment, create indicator (Is customer in the segment or not? Repeat for all segments.)
    • 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.
    • Appropriate interpretation defines strategy: cross-sell, up-sell, pricing, retention, acquisition, cost reduction etc.
  • 19. Practical Significance Acquisition Example
    • Features that distinguish segment of interest :
      • home owners
      • starting a family ( children < 2 years old )
      • Well educated ( postgraduate qualification )
      • Aged 28-45
      • Earn >$80k
      • Have 2 or more cars
    • “ Affluent up-and-coming families”
    • These features are used to score the population
  • 20.
    • Greater Auckland
    Action Deployment: Acquisition LOW HIGH Desire to Acquire
  • 21.
    • North Shore
    Action Deployment: Acquisition LOW HIGH Desire to Acquire
  • 22.
  • 23. SUNZ Annual Conference 2007 A Big Thank You, to Our Sponsors