1

Developing a Practical Forecast Methodology to Produce a Ten-Year Subscriber and Revenue Forecast

for Mobile Markets

Coleago Consulting LtdIBC Conference - Boston, MA - September 1999

Stefan Zehle, Director, Coleago Consulting LtdMobile: +44 7974 356258 Fixed: +44 20 7221 0094 e-mail: stefan.zehle@coleago.com

www.coleago.com

2

Agenda

• Introduction to Coleago Consulting Ltd• Forecast objectives• Determining potential demand• Generating a penetration forecast• Price elasticity of demand• Voice revenue and usage forecast• Revenue for data services

3

Coleago’s consultants have worked on mobile projects in 28countries.

• Coleago’s consultants have advised clients with regards to all issues of mobile business development including demand assessment, tariffing, positioning, interconnect, coverage roll out, business planning, market forecasting, etc.

• We have carried out market studies and advised clients on licence bids, business planning, due diligence, acquisition evaluation, etc.

Austria, Belgium, Denmark, Finland, France, Ireland, Italy, Netherlands,

Norway, Poland, Sweden, UK

Australia, China, Hong Kong, Korea, Malaysia,

Taiwan, SingaporeArgentina, Brazil,

Venezuela

Israel, KuwaitCaribbean, El Salvador,

Mexico

Canada

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Typical forecast objectives.

• Generate reliable forecasts of market demand for a mobile telephony business.

• Forecast based on primary data gathered through market research interviews with an integrated approach.

• The forecast must be presented in form of a spreadsheet model to to enable the running of sensitivities and interface directly with financial and engineering models.

• The forecasting methodology and model must deliver results quickly and must be easy to use, while also producing excellent quality of analysis.

5

Main forecast outputs required.

• Penetration & total subscribers• Price elasticity of demand• Average monthly voice bill• Interconnect revenue and costs• Minutes of use• Revenue for data services• Data traffic

6

Determine potential demand for mobile services.

• The main objective of market sizing is to determine potential demand.

• Ideally a large scale quantitative survey amongst a representative sample of the population provides the main input into a forecast.

• In a questionnaire based survey demand is likely to be be underestimated - develop a questionnaire structure that compensates for this.

• Primary market research will underpin any assumptions made using economic analysis or benchmarks.

• Use a mixture of primary market research, economic analysis, benchmarks and vision.

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Potential demand is a sub-set of the addressable market. Example of a developing country:

Total Population

100%

60% Old Enough

to Own

Mobile Phone

50% With Sufficient Income

Addressable Market 30%

80% Expresses Interest: Potential Demand 24% of Population

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Questionnaire to determine potential demand and price elasticity of demand.

Sampleof Population

Interestin

Mobile

No Interest

in Mobile

SuggestPrices

Fall

FMCPropo-sition

MobilePropo-sition

No Interest

in Mobile

Interestin

FMC

No Interestin FMC

Rejecters:Classifi-

cation Data

Mobile PotentialDemand

FMC PotentialDemand

Determine How Much Willing to Pay to Adopt &

Use per MonthPrice Elasticity of Mobile & FMC Demand as Function of

Potential Demand

Proposition PreferenceMobile v.s. FMC

Existing Mobile

User

Don’tUse

Mobile

Maximum Potential Mobile & FMC Demand Willing to Pay

Minimum

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Potential demand for mobile operators.

• All those who are interested and who are also willing and able to pay a minimum amount comparable to the cost of PSTN usage.

• Demand is demand in the economic sense, because it is backed by willingness and ability to pay.

• Some people, regardless of price have no interest in mobile.

• Maximum potential demand ceiling is likely to change over time due to the bandwagon and age shift effects.

• Looking at the propensity to adopt by age provides a window to the future.

• Factors other than age are important, in lower income countries income is a discriminator.

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Propensity to adopt mobile by age - example from Western European country: Age is an important discriminator.

A Western European country, sample 1,000 interviews 1997

Propensity to Adopt by Age

y = -0.0106x + 0.9686R2 = 0.9333

0%

10%

20%

30%

40%

50%

60%

70%

80%

17 27 37 47 57 67 77Age of Potentail Adopters

Pote

ntia

l Ado

pter

s

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Correlation between propensity to adopt mobile & income - example high income country: Income does not matter.

A Western European country, sample 1,000 interviews 1997

Propensity to Adopt Mobile by Income

y = 0.0003x + 0.4019R2 = 0.684

0%

10%

20%

30%

40%

50%

60%

70%

0 100 200 300 400 500 600 700 800 900

Annual Household Income '000

Pote

ntia

l Ado

pter

s in

Sam

ple

12

Correlation between propensity to adopt mobile & income - example lower income country: Income matters.

A Far Eastern country, sample 1,500 interviews 1996

Propensity to Adopt Cellular by Income

y = 0.0852x + 0.0471R2 = 0.9818

0%

10%

20%

30%

40%

50%

60%

<30 30-50 50-70 70-90 90-110 >110Monthly Net Income

Pote

ntia

l Ado

pter

s in

Sam

ple

13

Maximum potential demand assumptions for each segment should be anchored in consumer and business demographics.

• Segmentation must be appropriate to long term forecasting. This may not be the same as segmentation for other purposes.

• The potential demand assumptions should be linked to changing demographic patterns and changes in income.

0%

10%

20%

30%

40%

50%

60%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

Pene

trat

ion

of P

opul

atio

n

Penetration

Potential Demand Ceiling

• The potential demand sets a penetration ceiling, conceptually the maximum potential penetration is the level at which the product life cycle curve reaches its upper limit.

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The mobile subscriber market penetration forecast can be based on the product life cycle concept and curve fitting.

• Curve fitting to forecast the penetration. An extensive body of research (Chambers, Jantsch, Bewley & Fiebig and others) on the use of curve fitting to forecast the product life cycle for telecoms markets and similar markets underpins the validity of this approach.

• A penetration curve which fits well with historic market data determines a trend based on the product life cycle. There is an element of self-validation.

• If historic data is not available benchmarks can be applied.

Introduction Growth Phase Maturity

Accelerating

Decelerating

The Product Life Cycle Curve

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Empirical evidence of s-shaped product life cycle curve.

World Mobile Telephony Subscribers

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Dec

-88

Dec

-89

Dec

-90

Dec

-91

Dec

-92

Dec

-93

Dec

-94

Dec

-95

Dec

-96

Dec

-97

Dec

-98

Subs

crib

ers

'000

Rest of WorldLow Income AustralasiaHigh Income AustralasiaCentral & South AmericaNorth AmericaWestern Europe

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Product Life Cycle Model FormulaPt = (1 + a * e -b*t )-1

where:Pt = % of the maximum potential penetration year tt = years from launcha = a factor skewing the curveb = a constant

andb = 1 / tm * ( ln ( (a / ( 1 / 0.99 )- 1)))

where:tm = total number of years to maturity

Various s-shaped growth curve functions are available, must be asymptotically bounded function.

• The upper asymptote is the potential demand identified in market survey.

• Pearl’s equation logistic curve has advantages in terms of manageability in the forecasting model.

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Select a penetration forecast curve which fits historic data.

Total Market Subscribers Model vs. Actual

0

100

200

300

400

500

600

700

800

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998

Subs

crib

ers

'000

Installed Base Model '000Installed Base Actual '000

18

Include factors in curve to model economic impact of speed of growth - example from a high income country.

Correlation Between Increase in Mobile Telephony Penetration and Macroeconomic Conditions in the UK

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Incr

ease

in P

enet

ratio

n of

Pop

s %

Poi

nts

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

Rea

l GD

P C

hang

e

Mobile Penetration IncreaseReal GDP Change

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Include factors in curve to model economic impact of speed of growth - example from a lower income country.

Correlation Between Increase in Mobile Telephony Penetration and Macroeconomic Conditions in Mexico

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

1989 1990 1991 1992 1993 1994 1995 1996 1997

Incr

ease

in P

enet

ratio

n of

Pop

s %

Poi

nts

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

Rea

l GD

P C

hang

e

Mobile Penetration IncreaseReal GDP Change

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Typical mobile penetration forecast.

• Curve only useful if average monthly bill level at any forecast level of penetration can be established.

Cellular Penetration - History & Forecast

0%

5%

10%

15%

20%

25%

30%

35%

40%

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Pene

trat

ion

of P

opul

atio

n

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Measuring price elasticity of demand with regards to monthly cost of ownership for potential users.

• Based on Van Vestendorp approach to price elasticity testing: “What is the highest price which you would consider paying in respect of your average monthly bill?”

• Most respondents think in monthly budgets rather than minutes of use, the monthly budget is in effect the monthly bill a new mobile subscriber is prepared to pay.

• Analyse data points to determine link between the monthly bill marginal subscribers are prepared to pay and penetration of maximum potential demand.

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Price elasticity of demand empirical evidence: Demand is driven by the value proposition, minutes for an amount of money.

Marginal Monthly Bill & Installed Base of Cellular Subscribers USA

19941993

19921991

1990

1989

1988

1995 1996

1997

y = -28.632Ln(x) + 335.49R2 = 0.9105

0

20

40

60

80

100

120

140

160

0 10,000 20,000 30,000 40,000 50,000 60,000

Cellular Subscribers '000

Mon

thly

Bill

Mar

gina

l Sub

scri

bers

199

7 R

eal

US$

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Marginal voice bill forecast as a function of penetration of potential demand - example high income country.

A Western European country, sample 1,000 interviews, 1997

Price Elasticity of Demand vs. Monthly Cost of Ownership

y = -312.3Ln(x) + 95.529R2 = 0.9832

0

200400

600

800

1,0001,200

1,400

1,6001,800

2,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

% of Maxiumum Potential Demand

Mon

thly

Bill

Incl

. VA

T

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Marginal voice bill forecast as a function of penetration of potential demand - example lower income country.

Price Elasticity of Cellular Demand Against Cost of Ownership

y = 947.34e-2.7861x

R2 = 0.9285

0

100

200

300

400

500

600

700

800

900

1,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%% of Maximum Potential

Mon

thly

Bill

Incl

udin

g Ta

x

A Central American country, sample 1,500 interviews, 1996

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The average monthly voice bill is forecast using a variety of parameters.• The starting point is the current average monthly bill in

the market.• The average monthly bill forecast is the current average

monthly bill plus the sum of marginal bills.• Adjust for price elasticity of demand for existing

subscribers - how do they react to tariff changes.• Output is a forecast of the average monthly bill as a

function of the penetration forecast.• A particular venture’s forecast of the average monthly

bill should be in line with the market average, but there can be differences to compensate for other elements of the marketing mix.

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Price elasticity of demand of existing subscribers.

• Conventionally the price elasticity coefficient indicates the effect a change in the price of a good will have on the quantity demanded.

• In the Coleago model the price elasticity coefficient is applied to the monthly bill instead of quantity demanded.

• Values for the price elasticity coefficient are similar to the conventional method, it is essentially the same concept.

• Avoids some of the complexities (e.g. different elasticities for line rental,. call charges, etc.) and produces a good result.

• Use benchmarks to determine coefficients.

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Price elasticity of demand formula.

Price Elasticity - ConventionalQ2 = Q1 * ( 1 + ∆P * -E )

where:E = the price elasticity coefficient

Q1,2 = the quantity demanded in year 1, 2∆P = the % change price from year 1 to year 2

Price Elasticity - Applied to Monthly BillB2 = = B1 * ( 1 + ∆P * ( 1 - E ))

where:E = the price elasticity coefficient

B1,2 = the average monthly bill in year 1, 2∆P = the % change in tariffs from year 1 to year 2

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Relationship between marginal and average monthly mobile voice bill and penetration.

Price Elasticity of Demand vs. Cost of Ownership

0%

2%

4%

6%

8%

10%

12%

14%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Pene

trat

ion

0

10

20

30

40

50

60

70

80

90

Mon

thly

Bill

199

6 R

eal I

ncl.

Tax

PenetrationAverage BillMarginal Bill

A Central American country, sample 1,500 interviews, 1996

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A range of factors impact on the average monthly bill and revenue.

Lower spending marginal users

subscribing to mobile services

Lower spending marginal users

subscribing to mobile services

Additional use from fixed mobile

convergence users substituting from

fixed network

Additional use from fixed mobile

convergence users substituting from

fixed network

Additional spend for non-voice services, e.g.

Internet access

Additional spend for non-voice services, e.g.

Internet access

Decline in mobile per minute tariffs

Decline in mobile per minute tariffs

Average Mobile

Bill

Average Mobile

BillIncreasing

Trend

Price elasticity: Existing subscribers increase use due to

decline in per minute tariffs

Price elasticity: Existing subscribers increase use due to

decline in per minute tariffs

DecreasingTrend

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Voice traffic forecast in minutes of use.

• Based on benchmarks make assumptions on the % split outbound vs. inbound traffic, international calls, roamed calls.

• The average monthly bill forecast divided by the average per minute price produces the average number of minutes per month per subscriber.

• Decline in tariff will drive increase in usage.• Price elasticity also depends on mobile tariff

relative to fixed. Substitutional usage is generated as people start using mobile as primary phone.

• Fixed voice minutes are higher than mobile minutes (400 per line/month is Europe, 1,000 in USA).

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Average monthly bill and mobile originated minutes forecast result based on survey & forecast model.

Average Voice Bill & Minutes of Use per Customer

0

100

200

300

400

500

600

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Ave

rage

Mon

thly

Bill

0

20

40

60

80

100

120

140

160

180

Av.

Mon

thly

Min

utes

of U

se -

Out

boun

d

MinutesVoice Bill

A Western European Country, 1,000 Interviews.

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Empirical evidence of price elasticity of demand and minutes of use.

UK Mobile and Fixed Spend per Minute & Usage per Line 1994 -1997

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0 50 100 150 200 250 300 350 400 450

Originated Minutes per Month

Ave

rage

Pri

ce p

er M

inut

e Vodafone & Cellnet

Orange

One2One BT Fixed Network

Source: Oftel Stats 1994-97

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Additional revenue from data services should be forecast separately to account for new revenue from data services.

• Mobile users pay for applications. Data e.g. Internet access is additional on top of voice usage.

• Assumptions for potential demand and spend to generate user & revenue forecast.

• Difficult to research, use existing spend for e.g. for Internet access as benchmark.

• Paradigm of digital economy: A shift in spending patterns.

Evolution of Data Speed in GSM

1997 Basic GSM data at 9.6 k/bits & SMS

1998 Landline modem speed high speed circuitswitched data services (HSCSD), 14.6-56.6 kbit/s

1999 Internet like IP packet services (GPRS), up to 171.2 kbit/s

2000 High rate data speeds and capacity (EDGE)

2001-2002 3rd generation wireless services,(WCDMA), up to 2 Mbit/s

Evolution of GSM Platform and Radio Technology

Dat

a Sp

eeds

Source: Nokia, adapted by Coleago

1997 Basic GSM data at 9.6 k/bits & SMS

1998 Landline modem speed high speed circuitswitched data services (HSCSD), 14.6-56.6 kbit/s

1999 Internet like IP packet services (GPRS), up to 171.2 kbit/s

2000 High rate data speeds and capacity (EDGE)

2001-2002 3rd generation wireless services,(WCDMA), up to 2 Mbit/s

Evolution of GSM Platform and Radio Technology

Dat

a Sp

eeds

Dat

a Sp

eeds

Source: Nokia, adapted by Coleago

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Revenue per subscriber is likely increase because price elasticity coefficient for voice may be greater than 1, and due to additional spend on data services.

Conceptual Trend in Average Monthly Bill per Subscriber

0

10

20

30

40

50

60

70

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Ave

rage

Mon

thly

Bill

per

Sub

scri

ber

Voice Bill - Marginal UsersVoice Bill - Existing UsersVoice Bill - Average UserData Bill - Average UserTotal Bill - Average User

35

Reasonableness checks increase confidence in results.

• Compare bottom up result with top down estimates.• Is the amount forecast for the average monthly voice bill

at maturity reasonable compared to PSTN bills today?• Calculate total mobile market value on basis of

subscriber and average monthly bill forecast.• Compare to GDP of the country. Is the result

reasonable?• If results are not reasonable lower penetration forecast

or decrease tariffs.

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Market ForecastModel

Market ForecastModel

TechnicalModel

TechnicalModel

FinancialModel

FinancialModel

Leverage the advantage of spreadsheet based market forecast modelling.

• The linking of a spreadsheet based marketing model to the financial and engineering models allows the running of scenarios in minutes.

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Conclusions

• An integrated methodology.• Demand and price elasticity of demand directly

traceable from market research through to the business plan.

• Demand and price elasticity easily modelled in business plan for running of sensitivities and scenarios.

• Additional revenue from data services can be compared to capex for data services.

• Inspires confidence of decision makers: Used in several successful licence bids, to obtain debt financing of new and existing mobile ventures, to obtain board approval for investment decisions.

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Visit our website to obtain further information.

• You can obtain this paper and other Coleago conference papers and articles from our website.

• Related papers on trends in mobile tariffs and fixed mobile convergence can also be downloaded.

Stefan Zehle, Director, Coleago Consulting Ltd, 47 Holland Park, London W11 3RS, UKTel: +44-7974-356 258 e-mail: stefan.zehle@coleago.com