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Dr. Paul Bracewell9th November '06

Customer AnalyticsDelivering Pizza Before the Phone Rings

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Overview

The Knowledge EconomySetting up for AnalyticsWhat is Customer Analytics?What Can Customer Analytics Do?Understanding the CustomerSummary

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Perspective

StatisticianEmpirical Methodologies (data driven, simulation,

approximation – close enough is good enough)Interpretation is key (business context)Focus is fully utilising the data (valuable business

asset – numbers don’t have a political agenda)Pizza – fast food – not a lot of substance…

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The Knowledge Economy

By-product of the knowledge economy is lots and lots of data. Data is the currency of the knowledge economy

How to cash in? Comes down to how the data is processed.

Just like any rush, there are plenty of people jumping on the band wagon (think of students at Pizza-Hut, all-you-can-eat)

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What is Analytics?Customer Analytics is not extracting customer numbers

– that is simple data extraction and reporting (slicing & dicing – how pizza is prepared)It is understanding Customer BehaviourThink of customer analytics as the interest to be gained

from your currencyImportantly – no interest is yielded by stuffing your

investment under the mattress…Slicing and Dicing is more like counting your investment

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Understanding Analytics

To understand customer analytics, requires a glimpse behind the scenes.

ProcessingPurpose of Analytics

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Before Analysis

Purpose of DataData ContextAccess to DataCleaning/Manipulation (Known Data Problems?)

Make sure the project isn’t set up to fail (it may sound as if customer analytics can deliver the world, but without preparation, it won’t come close)

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Data Collection

Every phone call,Every purchase,Every search,

generates data and is oftenan expression of behaviour

understanding that behaviouris your business advantage

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Data Storage

Out of Sight, Out of Mind:Cheap storage means that lots and lots of data

can be collected and stored without causing too many problems.Often this means data is stored in warehouses

with no doors, no windows.Even in an air-tight container, pizza goes stale

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Data Manipulation

Like that piece of pizza left under the couch after a night with the lads, data manipulation can often be forgotten.With vision, data manipulation can be

minimised for future projects (modelling mart)

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Data Types

Long Thin (one row per event)Short Fat (one row per entity)

Most work in preparing data for analyses is converting long thin data to short fat data.

Sponsors often neglect this important stepWhere are the results? Why are you taking so long?

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What is Analytics?

More than simple querying and reporting,Customer Analytics is the use of statistical techniques to identify pertinent information.

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What is Analytics (2)

Customer Analytics enables the following sorts of questions to be answered –

Who will respond to a campaign?How many calls will a call-centre get?What is an acceptable waiting time at a call-centre?What flavour of pizza will customers want?What are the triggers of customer churn?NOTE: Future Tense – prediction (pro-activity)

– using rigorously tested statistical techniques (need to look beyond the cheese)

Two Broad Types of Modelling – Predictive and Descriptive

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Effective AnalyticsCustomer Analytics requires five key elements:

Someone who knows the data (domain expert)Someone who knows how to analyse the data (analyst)Someone who knows how to interpret the results in the context of the problem (end user)Someone who can sell the results to the business (sponsor)Tools

Find the useful information and get that out to the people who need to use it.The task is to turn databases into sentences

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Using Analytics to Make Decisions

Statistical techniques are there to make working with numbers easier – show us when to look and where.Humans are poor at detecting patterns in

large data sets. Correct use of statistics avoids mistakes and bias.

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Making Robust DecisionsIs there a real difference between

$1677.58 and $2268.17?Consider the distribution of the data

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Understanding Customer Behaviour

FraudRiskMarketingChurn

BEHAVIOUR

BEHAVIOUR

BEHAVIOUR

BEHAVIOUR

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Role of Slicing and DicingRugby, Pizza and Beer

1. Customer Analytics

2. Slicing and DicingBracewell, P.J. (2002). Implementing Statistics in a Diagnostic Coaching Structure. Research Letters in the Information and Mathematical Sciences, 3, pp. 79-84.

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Fraud

New Zealand Insurance companies loose $150 million to vehicle fraud each year. They estimate that only 10% is picked up…

You pick up the rest in your premiums.For fraud to be profitable, it has to be abnormal. Customer analytics can detect abnormal

behaviour

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Fraudulent BehaviourWith a credit card, you are responsible for repaying the debt. A fraudster is not responsible for the debt. Any funds lost to fraud are recouped in bank fees and interest.A nice example is a type of fraud called a BIN attack (BIN = Bank Identification Number)

Look at your credit card – 16 digits or if AMEX 15 (Visa starts with 4999, Mastercard 5403)Software on Internet allows #’s to be generated. Fraudster takes the generated #’s and tests them at various websites -typically for purchases less than $30 to find genuine cards. Then those genuine cards are hit – without the card holder knowing…Fortunately, with the use of customer analytics BIN attacks can be identified.

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Minimising Workload

Potential Bottleneck if large number of casesImproved hit ratesIncrease value for Money of Investigation

Fraud

With Intent

Accidental

Compliant

Individuals Involved Cost of Fraud

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Acquisition Risk Scorecard

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MarketingAnother similar field is marketing – all about understanding consumer behaviour.Selecting customers for campaigns. Recent study on the North Shore at reducing junk mail showed that only 2% of people read junk mail. Better targeting, better response rates, better profitsListening to the customer – actions speak louder than words – this can be used to create campaigns. Work out which customers are likely to leave, but also establish why they are going to leave so measures/offers can be put in place to keep those customers.

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Interpreting Results

Intention of the data must not be neglectedResults are only as good as the:-

1. quality of data2. understanding of data and process3. “translation” to audience4. audience uptake

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Information Simplication

Intended audienceEase of communicationPin-pointing areas of “real” interestReduction of work-loadSolid platform for acting on insightGreater understanding

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Marketing Automation

Once these ideas/thinking have been embraced, the next logical step is further improvement of process……this improvement stems from ease of

deployment (automation)……paving the way for solutions like Marketing

Automation.

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Summary (1)

Those organisations which are open to finding previously unknown but useful information are well placed to take advantage of the wealth of data in the modern business environmentThe power of customer analytics is only applicable

when an organisation is receptive to the underlying principles of this process

“It is hard to look at both sides of the story when you are coming from one perspective”

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Summary (2)

Insight leads to innovation only if action is taken.Action can only occur if insights are robust and

practical.Insights are only robust and practical if suitable

tools are used, right personnel are involved and there is frequent and open dialogue between all parties.

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http://www.aclu.org/pizza/images/screen.swf

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