14 Patrick Bell en Baum - Solon Management Consulting

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Optimizing your contract renewal offers making a case for a new way of

segmentation

Budapest, December 2008


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Page 2Source: Solon

Topic of this presentation

How to decide upon which postpaid

customer should be offered whichlevel of subsidy for his potentialcontract renewal?

This presentation is about one of many details around retention businessin postpaid mobile

Not todays topics

How to optimize retention in prepaid?

How to optimize the approach to end customer at

the end of the contract duration in order to balance

churn against sleeper potential?

Whom to address when?

How to leverage direct mail versus outbound

versus reactive? How to deal with partners especially retail?

How to incentive organization?

How to optimally score churners/sleepers in order

to avoid waking up potential sleepers?

How to leverage retention business with upselling?

Etc.


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Optimization of contract renewal offers is one of the most relevant levers in order to optimize churn as well as investments

into existing customers

In order to optimize offers for contract renewals, MNOs follow a segmented approach

At the end of their contract duration, customers are divided into several classes

Level of renewal offer vary along these classes from 75 EUR to 270 EUR

Currently the decision criteria for the segmentation of customers is their ARPU/margin

So, MNO is willing to invest more in valuable customers

However, in order to optimize payback, the level of offer should be based on individual willingness to accept the renewal,

i.e. one should pay as much subsidy to the end customer necessary to make him accept the offer - but not more

Unlike sales, the retention business would allow for consequent price discrimination

Already today, market accepts different offers to end customers also different to sales offers

Infrastructure to guarantee consistent offers along all channels is already established

Data gathered over customer lifecycle provides possibility to score for willingness to accept renewal offers

Thus decision on level of renewal offer should be based on individual minimum level to avoid churn, rather than on

ARPU/margin

Source: Solon

Key hypothesis in a nutshell: Retention business in mobile industry wouldallow for offer discriminations

Key hypothesis

Status quo


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Price discrimination is

to offer customer specific prices

based on individual willingness to pay

in order to maximize return per customer

Price discrimination in sales does not work,

because

Offerings can not vary for differentcustomers due to marketing &

communication

Offerings are transparent among

competition

There is only limited to no information about

the willingness to pay of specific customers

Offer discrimination for contract renewals is

to offer segment specific level of subsidy

based on customers level of acceptance

in order to optimize retention investment and

reduce churn

Offer discrimination in retention business works,

because

infrastructure to segment specific offerings is

in place

market is already accepting different

offerings (due to limited transparency)

Due to existing relationship there is

information to score individual acceptance

levels

Source: Solon

Analogy of price discrimination and offer discrimination

Offer discrimination in retention businessPrice discrimination in sales

Defining the renewal offer to customers based on their ARPU is like defining your salesoffering based on costs


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There are three hypothesis to verify EBITDA-potential of offerdiscrimination

Source: Solon

Compared to sales offers, renewal offers are rather low

Payback over 24 month would allow for significantly higher offers for most customers, if

thereby single churn is avoided

Based on a single-customer payback logic, higher renewal offers are justified

Willingness to accept even lowest renewal offer is widespread along all ARPU classes

Increased renewal offers effect churn behavior, but of very few customers only

Offer elasticity is existent, but limited

Currently, scoring methods are used to define churn/sleeper probabilities

Scoring methods can be enhanced to define level of necessary renewal offer to avoid churn

Scoring methods based on customer specific information can identify level of renewaloffers for specific customers

1

2

3


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Page 6Source: Solon

If avoiding churn of a single customer, significant higher renewal offers arejustified along almost all customer segments

0

50

100

150

200

250

300

350

400

450

500

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120

Current renewal offer

Sales offer

Renewal offer that

allows for payback

after 12 months

End

customer

invest

End customer invest vs. ARPU / month

(in EUR)

ARPU per month

1

E X E M P L A R Y


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Page 7Source: Solon

Customer behaviour along levels of renewal offers show upside potentialof offer discrimination

Renewal offer

2Customer behavior vs. renewal offer

(in % of potential-to-churn, renewal offer in EUR)

0%

10%

20%

30%

40%

50%

50 100 150 200 250 300

Significant part of the customer base is

accepting even lowest renewal offers

Comprehensive increase of renewaltoo costly due to limited churn

reduction effect

Assumed customer behavior to be validated in comprehensive testing

If individual acceptance is determined,

savings on anyway-prolongators can

be used to increase offerings to low

number of tough nuts

Renewals

Churn

Sleeper

E X E M P L A R Y


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Page 8* Calculation based on 5 m relevant customersSource: Solon

Replacing retention classes by offer acceptance classes to determine therenewal offer will significantly reduce overall churn

Invest / renewal

Customer behavior by retention classes vs. customer behavior by offer acceptance classes

(in % of potential-to-churn, Invest in EUR)

Churn

Sleeper

Renewals

24%

5%

7%

8%

5%

25%

26%

75

125

175

210

Sleeper

Churn

Invest in m EUR 209 327

240

Invest /

customer

75 125 175 210 240 270

270

2

E X E M P L A R Y


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Page 9Source: Solon

Scoring methods are already used today to identify churn/sleeperprobabilities and can be adopted to scoring for acceptance levels

3

E X E M P L A R Y

0%

25%

50%

75%

100%

0% 25% 50% 75% 100%

Baseline for successful scoring (i.e.

availability of data, relationship with

data mining provider, etc.) already in

place

Effort to setup scoring algorithm fairlylimited

Open issues: Realization of multi-

dimensional scoring

Scoring methods based on existing customer data are

already successfully implemented to decide on prevention

activities as well as upselling activities

Status quo

Example: Quantification of sleeper probability

(Size of sample versus sleeper probability in sample)


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Today

(Customer behavior in %, Invest & ARPU in m EUR)

Calculation based on 5 million customers Potential-to-churnSource: Solon

Using the new churn management method will significantly reduce churnand increase the cumulated ARPU contribution

30%

29%

41%

100%

Churn

Sleeper

Renewals

209

781

49%

25%

26%

100%

Churn

Sleeper

Renewals

327

907

An optimized model based on perfect knowledge

(Customer behavior in %, Invest & ARPU in m EUR)

Customer

behaviour

Invest ARPU /

12 months

Significantly reduced churn figures

Comparable invest per contract

renewal

Customer

behaviour

Invest ARPU /

12 months

P R E L I M I N A R Y


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Page 11Source: Solon

Next steps: What to do on Monday morning

Business Case

Update of Business Case on the

basis of company actuals(Customer behaviour along

product structure)

Verification/assessment of

assumptions on scoring

efficiency (potential with data

mining partner)

Setup scoring algorithm for

testing (based on historic data) Setup comprehensive testing

(including control groups)

Rollout of offer discrimination

Ongoing optimization of scoringalgorithm based on tight

reporting and control groups

ImplementationTesting of scoring method

Tasks

PotentialSolon

Support

Updated model to calculate

overall financial potential of offer

discrimination (based on

companies assumptions)

Verified model based on test

results

Decision paper including rollout

plan and timetable

Change management/project

office

In parallel: initiate quick-wins on offer discrimination (e.g. incentives to outbound

agents/partners to lower investment budget)


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Budapest

Andrssy t 2.+36 1 88033-00

Solon Management Consulting Bt.

Munich

Kardinal-Faulhaber-Strae 6

+49 89 210388-0

London

2nd Floor, Berkeley Square House

Berkeley Square W1

+44 20 7887 6596

www.solon.hu

14 Patrick Bell en Baum - Solon Management Consulting

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