Know your customers by knowing who they know, and who they don'tLeveraging The Power Of Social InteractionsTim Manns

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Overview

Brief Definition Of Data Mining

Data Processed Within The Teradata Warehouse

Main Types of Customer Analysis Performed

ROI Justification For Customer Analysis

Identification Of Social Groups

We Can Leverage The Social Interactions Of Our Customers

Business Focused Examples And Benefits

Some Generalisations For Other Industries

Brief Introduction To Optus

• Telco ‘Challenger’ To Australia’s Incumbent• Part Of SingTel Group• Truly Convergent Telco

– Mobile (cell phones)– Fixed (land phones)– Fixed Broadband – Mobile Broadband– Business Networks– Subscription TV – Satellite

Definition Of Data Mining

Jimi Hendrix”Come on (let the good times roll)”

• Definition:–The processing of large amounts of data

in order to extract useful and insightful information

• Quote:–People talking but they just don't know, What's in my heart & why I love you so. I love you baby like a miner loves gold. Come on sugar, let the good times roll!

• To understand the customer

communications are targetedbehavioural driverswhat customers want

happy customers = high value

Data Storage = 100% Data Warehouse

• Transactional Level DataCall Detail Records (cdr’s)

• Some Fictitious Numbers– 5 million customers– 20 calls per day – 100 million rows per day

• Enterprise Data Warehouse– Usage, Downloads, Handset – Billing, Product, Payments– Point-Of-Sale, Contract – Name, Address, Age, Tenure– Customer Service, On-line

Business Data Mining Problems

• Predicting Churn/Attrition• Customer Segmentation• Revenue Stimulation (up-sell)• Product Stimulation (cross-sell)• Ad-Hoc Customer Profiling• Product/Rateplan Pricing • ‘Deep-Dives’• Business Decision Evaluations• Fraud • Credit Risk

?

Bigger Piece Of The $ Pie

• Cost Of Losing Customers (Example)– 5 Million Customers– Average Monthly Spend $50– Voluntary Churn Is Approx 25k(0.5% Of Base Per Month)

– Revenue Loss Due To Churn $1.25 Million Per Month

• Increase Customer Value– New Plan Or Offer Increases Spend To $55 (10% increase)

– In Just 10% of Customers – Increase Revenue By $2.5 Million Per Month

Why Piece Together Social Groups?

• Identify Friends and Family• Measure ‘word-of-mouth’ Influence• Of Customers and Individuals • Of Outbound Communications• Measure Impact Of ‘refer-a-friend’ Behaviour In Customer Acquisition

• Better Understand Customer Value And…Value Of Prospects!

• Be Ahead Of The Shift Away From Brand Driven TV Commercials

• Because Friends Are Many Times More Influential Than Corporations

We Built An In-House Solution

• Process Weekly History Of All Customer Calls (approx 600 million records for 4 weeks)

• String Cleaning Of Phone Numbers• SQL Queries Written To Summarise Every Customer Calling Relationship

• Outbound Calls To ‘Other’• Inbound Calls From ‘Other’• Join Inbound and Outbound To Confirm Reciprocal Relationship (And Select Frequency)

• Six Weeks. Project Conception To Completion• Focus Is Customer Analysis (for legal reasons)

Ant sized view… Data Manipulation!

sms

sms

voice

Call Type (voice, sms, picture)

…

…

…

x 20 columns info..

inbound

inbound

outbound

Inbound / Outbound

2009-10-20012345678046666666

2009-10-200123456780403203383

2009-10-190123456780403203383

Date time‘friend’customer

• Transactional level (weekly approx 600 million rows)

• Main Result Table (approx a few million rows)

5

30

Inbound Voice Call Count

15

5

Inbound SMS Call Count

…

…

x 20 columns info..

20

35

Inbound Call Count

012345678046666666

0123456780403203383

‘friend’customer

Keeping It Simple Proved A Success

• Identify The Nature Of Customer Relationships • Many Factors Can Be Considered

– Time Of Day, Day Of Week– Voice, SMS, Picture Calls– Voice Call Duration

45yr WifeNew Phone

Work ColleagueUses Email And Data

Weekend BuddyNot A Customer

Moving To Optus? John Doe

Customer Insights Gained Through Social Analysis

Optus YNetworker

Age 17 yrs (f)

John DoeNetworker

Age 47yrs?(m)

John DoeData User

Age 47yrs? (m)

Jane Smith-DoeAlways In Touch

Age 46 yrs (f)

• Exchange total 25 SMS per week• Most calls made approx 5pm – 7pm

same accountsame address

• Exchange total 5 SMS per week• 20 Voice Calls between 9am and 6pm• Most calls made approx 3.45pm

• Minor communication

• Exchange total 10 SMS per week• Exchange total 5 voice per week• Most calls made approx 9am

Optus XSaver

Age 18 yrs (m)X 20

Cheerleader (17 yrs)Computer Programmer

Save The Cheerleader. Save The World.

• The purchaser is not necessarily your customer• Identify your leaders, your cheerleaders!• A multi-million $ TV advertising campaign can be wreaked by a 17 yr old cheerleader…

• Identify and target key influencers in your customer base and disproportionately benefit your brand

• The problem of customer churn is far worse than you think;– Churned customer tells friends (prospects)– Friends get influenced to churn also– Prospects go to a competitor– Win-back campaigns a wasted cost

Churn Is More Than Predictive Problem

• Customers In Social Groups With Recently Churned Customers Are More Likely To Subsequently Churn

• Reactive ‘Trigger Event’ Campaign To RetainCustomers When A Friend Churns

• Have A Voice In The BBQ Chat!

Our Predictive Churn Models

• Our Ability To Predict Churn In The Subsequent Month Using Social Groups, Usage, Billing, Demographics, And Contract Data

• 5% of Customer Base Achieves Lift Of 10

0

5

10

1 5

20

1 1 0 20 30 40 50 60 70 80 90 100P o p u l a t i o n %

Magn

itude

Increa

se

C h u r n M o d e l P e r f e c t M o d e l

Conclusions

• Analysis Of Social Groups Using Our Detailed Transactional Data Has Enabled New Customer Insights

• Unparalleled Customer Targeting• Improved Predictive Churn Analysis• A Reduction In Churn Saves $m• Enabled Better Family Identification• Sell Household Products To Families• Greater Share Of Family Wallet• Measure Viral Impact Of Direct Marketing

Questions…

tim . manns optus . com . au