data mining & campaign management the maxis experience · data mining & campaign management...

Data Mining & Campaign ManagementThe Maxis Experience

SAS Forum International 200616-18 May 2006

Geneva, Switzerland

Evelyn JimenezHead of Customer Lifecycle ManagementMaxis Communications Berhad, Malaysia

®

1

Presentation Agenda

• Maxis and the Malaysian mobile industry

• Customer Lifecycle Management

• Sample Campaigns

• Best Practices

2

Malaysia at a glance

• Located in Southeastern Asia, Malaysia is basically divided into 2 regions: Peninsular Malaysia and East Malaysia

• Land area: 328,550 sq km

• Population: 26.7 million (2005 est.)

• Median age: 24 years

• Multi-cultural society: Malays, Chinese and Indians

• GDP (purchasing power parity):$249 billion (2005 est.)

• Internet users: 10 million (2005)

• Mobile users: 19 million (2005)

3

Malaysia Penetration Rates

31%37%

44%

56%

73%

0%

25%

50%

75%

100%

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Pen

etra

tion

Rate

(%)

Maxis is the leading mobile service provider in Malaysia with a cumulative base of 8M subscribers

Maxis, 40%

Celcom, 35%

DiGi, 25%

Y2005 Market Share

• Malaysian mobile penetration rate reached 73% at the end of 2005 with 19.5 million subscribers

• 85% are predominantly prepaid subscribers

Maxis, 46%

Celcom, 33%

DiGi, 21%

Y2005 Revenue Share

Prepaid launch spawned

mobile growth

Lowering of prepaid starter

pack prices

• Maxis is the market leader with 40% market share or 8 million subscriber base

• Maxis also leads in financial performance with share of Revenue and EBITDA at 46% and 49% respectively

• Maxis ARPU registered at 9% above industry average

4

Maxis on its way to becoming the preferred mobile service provider in the region

ASIAN MOBILENEWS AWARDS 2005Mobile Operator of the Year, Malaysia Award

FROST AND SULLIVAN Malaysia Telecom Awards 2005

- Service Provider of the Year- Mobile Service Provider of the Year

MALAYSIA BRAND EQUITY AWARD 20051st – Brand Visibility Award

BEST MOBILE BROADBAND SERVICE PROVIDERPC.com Best Product Award 2005

BEST POSTPAID TELCO SERVICE PROVIDERPC.com Best Product Award 2005

SUPER BRANDS 2005Mobile Service Provider - Platinum

Telecom Company – Gold

Awards won in 2005:

India – Aircel (Dec’05)

International ventures:

• Aircel: Number 1 operator in Tamil Nadu and Chennai

• India’s population of 1.1billion currently at a low penetration of 7.4% and at the inflexion point of growth

Indonesia – NTS (Apr’05)

• NTS: Scheduled nationwide launch for GSM1800 and 3G in 2006

• Market with high potential for growth

5

While acquisition is still needed, customer retention becomes more critical in a maturing market

Environment Challenges

Campaign Challenges

• Market reaching maturity with 73% penetration rate• Intense competition with much lower cost of entry:

cheaper starter packs and further reduction in tariffs• Mobile service now a commodity, no longer a luxury• Subscriber stickiness is a critical business driver• Growing value-added services and mobile data

business

• Aggressive acquisitions expand the market, but it also creates dual-SIM phenomenon and phantom churn

• Targeted CLM campaigns are more efficient and sustainable on a long-term basis

• Millions of daily CDRs processed to timely, meaningful and actionable information

• Speed to market• Quantity and quality of campaign executions• Campaign effectiveness monitoring

6

Presentation Agenda




• Best Practices

7

Focusing on churn reduction and revenue enhancement within the customer’s lifecycle

Time / insights

Cus

tom

er V

alue

Acquisition

Ana

lytic

al

insi

ghts

WelcomeProgram

Target/acquire subscriber

Pro-activity based on “If”events:

- Lifetime- Usage/ purchase- Behaviour

Churn Prevention/ Reduction

Expiration

Revenue Enhancement

Migration

Customerdevelopment Harvest Win Back

• Behaviour Scoring• Response rates• Entry Scoring

• Contact Policy• Fraud Detection• Segmentation (Value/Needs)

• Tariff Plan Optimisation• Cross Sell / Up Sell• Credit / Collections

• Churn Propensity• Churn Segmentation• Satisfaction score

8

Data mining and below-the-line channels are our enablers toward an effective lifecycle management

From…. To….

Standard Data Reports

Traditional Marketing

Above-the-line Advertising

Data Mining

Target Marketing

Below-the-line channels

• Segmentation

• Predictive Modeling

• Customer Profitability

• Churn management

• Usage stimulation

• Plan migration

• SMS

• Outbound calls

• Direct mailers

9

Deactivesegment ARPU>RM45150K subs

+ no offer

To-date:

25 campaigns tested

Group A• Time to

churn <35 days

• 30K subs

Group B• Time to

churn 35-70 days

• 60K subs

Group C• Time to

churn 70-85 days

• 60K subs

Offer variants RM20

bonus for RM10 top-up 3 times

RM60bonus for RM30 top-up in 7 days

100% Bonus if next top-up in 7 days

101 102 103

+ Control groups 60K subs

Deactivesegment ARPU>RM45150K subs

+ no offer

To-date:

25 campaigns testedTo-date:

25 campaigns tested

Group A• Time to

churn <35 days

• 30K subs

Group A• Time to

churn <35 days

• 30K subs

Group B• Time to

churn 35-70 days

• 60K subs

Group B• Time to

churn 35-70 days

• 60K subs

Group C• Time to

churn 70-85 days

• 60K subs

Group C• Time to

churn 70-85 days

• 60K subs

Offer variants RM20

bonus for RM10 top-up 3 times

RM20bonus for RM10 top-up 3 times





101 102 103

+ Control groups 60K subs + Control groups 60K subs

Uplift*: 25%Cost**: RM12ROI: 700%

Uplift: 67%Cost: RM30ROI: 1,000%

Uplift: 25%Cost**: RM7ROI: 1,300%

Uplift*: 6%Cost**: RM12ROI: -66%

Uplift: 90%Cost: RM30ROI: 2400%

Uplift: 2%Cost: RM6ROI: -130%




A

B

C

Segm

ent

101 102 103

Offers

Uplift*: 25%Cost**: RM12ROI: 700%

Uplift: 67%Cost: RM30ROI: 1,000%

Uplift: 25%Cost**: RM7ROI: 1,300%







A

B

C

Segm

ent

101 102 103

Offers

Offer

No.103

No.102

No.102

Target

150K/month

240K/month

210K/month

600K/month

Expectedsaves*

2,500

4,000

3,500

10,000

A

B

C

Total

Segment

Today: >10 offer variants tested each week across different segments – have built the ability to execute 3-4 campaigns/week• 1 scaled campaign (100-300K)• 2-3 tests / refinements (100-150K)

Offer

No.103

No.102

No.102

Target

150K/month

240K/month

210K/month

600K/month

Expectedsaves*

2,500

4,000

3,500

10,000

A

B

C

Total

Segment



The Maxis Customer Lifecycle Management Process

Multiple segments + multiple offers

Test offers + measure impact (ROI, saves)

Scale successful campaigns

• Define segments based on customer usage and recharge behavior

• Define multiple offer types and variants

• Use control groups to measure impact

• Test different offers against different segments

• Calculate net save rates (target group vs. control group)

• Measure ROI of each offer-target combination

• Identify offers which resulted in the highest save rate and scale up to the whole segment (BTL)

10

Presentation Agenda




• Best Practices

11

Subscribers get hit several times with CLM offers while in the churn pipeline

Segment

77-83

63-69

49-55

35-41

21-27

7-13

SMS Offer 1

SMS Offer 2

SMS Offer 3

SMS Offer 4

SMS Offer 5

SMS Offer 6

Offer Control Take Net Take

• Presence of a “sweet spot” for saves within the lifecycle

•Certain value segments have higher save rates

Net Save

Reactivate Broad Launch to Deactive Segment

12

Revenue enhancement from upfront revenues and resulting increase in usage

SMS Offer 1

SMS Offer 2

SMS Offer 3

SMS Offer 4

SMS Offer 5

SMS Offer 6

Offers Tested Net Take rate

• Negligible take rate from very low SMS users

• Reprice offset by increase in usage of about 30-50%

Offer SMS Offer 1

Expected 30-50% upliftincremental usage (based on CLM Test)

Expected Upfront revenuetotal revenue + incremental

usage

TEST BROAD LAUNCH

Target: SMS usage-tiered segments

13

Predictive churn model looks at past 3 months behavior and predicts behavior for the next month

Churn

Last 1Month

Last 2 Month

Last 3Month

Next Month

HistoryHistoryHistory

Looking back at behavior before actual churn

ThisMonth

History

Step 1 Step 2 Step 3

Build Model based on

random sample behavior

Determine significant

variables from variables list

Generate high-propensity churner

segments

Apply Model to score whole

postpaid base on monthly basis

Perform Save Offer on monthly

basis

Step 4 Step 5

14

9%

91%

The Model produced 13 groups of subscribers with different

churn probability and behavior

It can be broadly classified into High Score (9%) and Low Score

(91%)The Major Profile:• Significant Reduction in bill size• Significant reduction in voice calls’ amount• SMS usage level• Overall incoming call level• Less outgoing calls vsincoming calls

Churn Attributes Used:• 358 Original and Transformed Variables• 5 Significant Variables

There are 5 significant variables that predict postpaid attrition with churn probability score as high as ~ 90%

Churner 51.2%

Churner 90.5%

Churner 71.6%

Churner 55.1%

Churner 60.4%

The high score segment consists of 5 distinct groups of

behavior

High Score

Model lift value = 7.3

Base= 9,629Churners = 0.3%

Base= 9,629Churners = 2.2%

Non Churners

Churners

Churners

Non Churners

Churners

Non Churners

The Model identifies 7 times more

churners!

1% Randomly Selected

Top 1% Model Predicted Churners

15

Identifying potential churners

Test Offers

Gross Takes

Churn Model Segments• High & medium value customers

• High propensity to churn

Offer 1 Offer 2 Offer 3

7 % take up 60 % take up 20 % take up

Task Criteria

Top

Control, no offer

Active : 91%

Suspend : 1%

Deactive : 7%

Net save – 13%Usage Band %Increased Usage 36%Maintain 16%Reduced Usage 48%

Active : 75%

Suspend : 5%

Deactive : 20%

Usage Band %Increased Usage 9%Maintain 36%Reduced Usage 55%

Outbound Calls to Target List, whilst control group is isolated from campaigns

Post Campaign analysis

Status 1 month after launch

Saved potential churners

Analysis of usage of customers in the contact list & control group showed improvements

The predictive churn campaign yielded not only a net save of 13% but also a net increased usage incidence of 27%

16

Presentation Agenda




• Best Practices

17

Based on our experience, it takes several components to make the CLM operations successful

Top Management support

Unified CLM datamart

Skills development, training & consultancy

Ownership by the business, not IT

Focus first on critical business issues

Analytical minds & lots of creative brainstorming

Dedicated CLM team

Effective execution via tools, processes &

quality controls

Continuous campaign monitoring and transfer

of learnings

18

Full campaign automation and real-time, event-based marketing are our key next steps

Churn Forecasting

Data mining: Predictors, Customer Lifetime Value

ROI tools & CLM processes

Market testing of new products & services

Standard analytics

Profile indicators & personalized offers in all

touch points

Full campaign management automation

Churn management, usage stimulation, migration

Real-time, event-based marketing

Thank You

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