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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4

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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

20Greater Auckland

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