strategic statistics
TRANSCRIPT
SUNZ Annual Conference 2007
A Big Thank You, to Our Sponsors
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Dr. Paul Bracewell29th November '07
Strategic StatisticsNavigating Analytical Politics
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Overview
Statistics in an analytical framework Key analytical players defined Analytical ‘soup’: how the players mix Politics and success Communicating the message
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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. 7Competing on analytics: the new science of winning
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Key Players
Data Expert Analyst Power Consumer Sponsor Analytical Infrastructure
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‘Internal’ Definitions/Perceptions
“There are three kinds of lies: lies, damned lies, and statistics”
Mark Twain, 1906Chapters from My Autobiography. North American Review 186
“Numbers don’t lie; people do” Various
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Interaction Between Players
data expert
analyst
power consumer
analytical infrastructuresponsor
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Politics and success
“Politics is the process by which groups of people make decisions.”
Analytics: “… drive decisions and actions.” (Davenport and Harris, 2007)
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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 businessRepresentation of strength and direction
of interactions between core entities contributing to an analytical exercise.
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Ability to SucceedGoverned by “Sponsor”
HIGHLOWLevel of Control
Leve
l of
Und
erst
andi
ng
LOW
HIG
H
1. Likely to succeed if sponsor can guide analyst to deliver what is required. Sponsor able to “sell”. Ideal for ‘junior’ analysts.
2. Likely to succeed if sponsor can impart vision on analyst, and analyst can deliver. Sponsor able to “sell”. Ideal for ‘senior’ analyst.
3. Possibly succeed but reliant on ability of analyst to do work and sell to business. Best suited to senior analysts.
4. Likely to fail: results capped by sponsors knowledge - high frustration from analysts and business.
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Communication
Successful uptake requires understanding Educating the business on analytics
segmentation visualisation
“A picture speaks a thousand words”
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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
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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
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Risk/Reward Segmentation
Customer Centric ApproachWhat does the customer do?
Business Centric Approach What impacts upon our bottom line?
Business/Customer OverlapREWARD: the value of the customer’s behaviourRISK: the chance that they will stop that behaviour
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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)
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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)
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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.
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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.
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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
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LOW HIGH
Desire to Acquire
Action Deployment: Acquisition
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LOW HIGH
Desire to Acquire
North Shore
Action Deployment: Acquisition
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SUNZ Annual Conference 2007
A Big Thank You, to Our Sponsors