Mobile Demand

8/8/2019 Mobile Demand

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Developing a Practical Forecast Methodology to

Produce a Ten-Year Subscriber and Revenue Forecastfor Mobile Markets

Coleago Consulting Ltd

IBC Conference - Boston, MA - September 1999

Stefan Zehle, Director, Coleago Consulting LtdMobile: +44 7974 356258 Fixed: +44 20 7221 0094 e-mail: [email protected]

www.coleago.com


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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


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Coleagos consultants have worked on mobile projects in 28

countries. Coleagos consultants have advised clients with regards to

all issues of mobile business development includingdemand 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, acquisitionevaluation, etc.Austria, Belgium, Denmark, Finland,

France, Ireland, Italy, Netherlands,Norway, Poland, Sweden, UK

Australia, China, HongKong, Korea, Malaysia,

Taiwan, Singapore

Argentina, Brazil,Venezuela

Israel, KuwaitCaribbean, El Salvador,

Mexico

Canada


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Typical forecast objectives.

Generate reliable forecasts of market demand for amobile telephony business.

Forecast based on primary data gathered through

market research interviews with an integratedapproach.

The forecast must be presented in form of aspreadsheet model to to enable the running of

sensitivities and interface directly with financialand engineering models.

The forecasting methodology and model mustdeliver results quickly and must be easy to use,

while also producing excellent quality of analysis.


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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


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Determine potential demand for mobile services.

The main objective of market sizing is to determinepotential demand.

Ideally a large scale quantitative survey amongst arepresentative sample of the population provides themain input into a forecast.

In a questionnaire based survey demand is likely to be

be underestimated - develop a questionnaire structurethat compensates for this.

Primary market research will underpin any assumptionsmade using economic analysis or benchmarks.

Use a mixture of primary market research, economicanalysis, 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

NoInterest

in Mobile

SuggestPrices

Fall

FMCPropo-sition

MobilePropo-sition

NoInterest

in Mobile

Interestin

FMC

NoInterestin FMC

Rejecters:Classifi-

cation Data

Mobile PotentialDemand

FMC PotentialDemand

Determine How Much

Willing to Pay to Adopt &Use per Month

Price Elasticity of Mobile &FMC Demand as Function of

Potential Demand

Proposition PreferenceMobile v.s. FMC

ExistingMobile

User

DontUse

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 willingand able to pay a minimum amount comparable to thecost of PSTN usage.

Demand is demand in the economic sense, because it isbacked 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 awindow to the future.

Factors other than age are important, in lower incomecountries 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 + 0.9686R

2= 0.9333

0%

10%

20%

30%

40%

50%

60%

70%

80%

17 27 37 47 57 67 77

Age of Potentail Adopters

Potent

ialAdopters


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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 + 0.4019

R2= 0.684

0%

10%

20%

30%

40%

50%

60%

70%

0 100 200 300 400 500 600 700 800 900

Annual Household Income '000

PotentialAd

optersinSampl

e


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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 + 0.0471

R2 = 0.9818

0%

10%

20%

30%

40%

50%

60%

110

Monthly Net Income

PotentialAd

optersinSample


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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 otherpurposes.

The potential demand assumptions should be linked tochanging 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

Penetration

ofPopulation

Penetration

Potential Demand Ceiling

The potential demand

sets a penetrationceiling, conceptuallythe maximum potentialpenetration is the level

at which the productlife cycle curve reachesits 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 andothers) on the use of curve fitting to forecast the product

life cycle for telecoms markets and similar marketsunderpins the validity of this approach.

A penetration curve whichfits well with historicmarket data determines atrend based on the productlife cycle. There is anelement of self-validation.

If historic data is notavailable benchmarks canbe 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

Subsc

ribers'000

Rest of World

Low Income Australasia

High Income Australasia

Central & South America

North America

Western Europe


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Product Life Cycle Model Formula

Pt = (1 + a * e -b*t )-1where:

Pt = % of the maximum potential penetration year tt = years from launcha = a factor skewing the curve

b = a constantand

b = 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.

Pearls equation logistic curve has advantages in termsof 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

Subscr

ibers'000

Installed Base Model '000

Installed Base Actual '000


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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

IncreaseinPenetration

ofPops%Points

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

RealGDPC

hange

Mobile Penetration Increase

Real 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

Incre

aseinPenetration

ofPops%Points

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

RealGDPC

hange

Mobile Penetration Increase

Real GDP Change


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Typical mobile penetration forecast.

Curve only useful if average monthly bill level at anyforecast 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

P

enetrationofPopulation


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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 elasticitytesting: What is the highest price which you would

consider paying in respect of your average monthlybill?

Most respondents think in monthly budgets rather thanminutes 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 payand 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 ofmoney.

Marginal Monthly Bill & Installed Base of Cellular Subscribers USA

19941993

19921991

1990

1989

1988

1995 1996

1997

y = -28.632Ln(x) + 335.49

R2 = 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

MonthlyBillMarginalSubscribers1997Rea

l

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) + 95.529

R2= 0.9832

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

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

% of Maxiumum Potential Demand

Monthly

BillIncl.VAT


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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

MonthlyB

illIncludingTax

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 averagemonthly bill plus the sum of marginal bills.

Adjust for price elasticity of demand for existingsubscribers - how do they react to tariff changes.

Output is a forecast of the average monthly bill as afunction of the penetration forecast. A particular ventures forecast of the average monthly

bill should be in line with the market average, but there

can be differences to compensate for other elements ofthe marketing mix.


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Price elasticity of demand of existing subscribers.

Conventionally the price elasticity coefficient indicatesthe effect a change in the price of a good will have onthe quantity demanded.

In the Coleago model the price elasticity coefficient isapplied to the monthly bill instead of quantitydemanded.

Values for the price elasticity coefficient are similar to

the conventional method, it is essentially the sameconcept.

Avoids some of the complexities (e.g. differentelasticities 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 - Conventional

Q2 = 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 2Price Elasticity - Applied to Monthly Bill

B2 = = B1 * ( 1 + P * ( 1 - E ))where:

E = the price elasticity coefficientB1,2 = 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

Penetration

0

10

20

30

40

50

60

70

80

90

MonthlyBill1

996RealIncl.T

ax

Penetration

Average Bill

Marginal Bill

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


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A range of factors impact on the average monthly bill andrevenue.

Lower spendingmarginal users

subscribing to mobileservices

Lower spendingmarginal users

subscribing to mobileservices

Additional use fromfixed mobile

convergence users

substituting fromfixed network

Additional use fromfixed mobile

convergence users

substituting fromfixed network

Additional spend fornon-voice services, e.g.

Internet access

Additional spend fornon-voice services, e.g.

Internet access

Decline in mobile perminute tariffs

Decline in mobile perminute tariffs

AverageMobile

Bill

AverageMobile

Bill

IncreasingTrend

Price elasticity:Existing subscribersincrease use due to

decline in per minutetariffs

Price elasticity:Existing subscribersincrease 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, internationalcalls, roamed calls.

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

Decline in tariff will drive increase in usage.

Price elasticity also depends on mobile tariffrelative to fixed. Substitutional usage is generatedas 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

AverageM

onthlyBill

0

20

40

60

80

100

120

140

160

180

Av.MonthlyMinutesofUse-Outbo

und

Minutes

Voice 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

AveragePric

eperMinute Vodafone & Cellnet

Orange

One2OneBT Fixed Network

Source: Oftel Stats 1994-97


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

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

Assumptions forpotential demand andspend to generate user& revenue forecast.

Difficult to research,use existing spend fore.g. for Internet accessas benchmark.

Paradigm of digitaleconomy: A shift inspending 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

DataSpeeds

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

DataSpeeds

DataSpeeds

Source: Nokia, adapted by Coleago


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Revenue per subscriber is likely increase because priceelasticity coefficient for voice may be greater than 1, anddue 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

rageMonthlyBi

llperSubscriber

Voice Bill - Marginal Users

Voice Bill - Existing UsersVoice Bill - Average User

Data Bill - Average User

Total Bill - Average User


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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 ofsubscriber 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

FinancialModelFinancialModel

Leverage the advantage of spreadsheet based market

forecast modelling. The linking of a spreadsheet based marketing model to

the financial and engineering models allows therunning 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 businessplan for running of sensitivities and scenarios.

Additional revenue from data services can be comparedto 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 approvalfor investment decisions.


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Visit our website to obtain further information.

You can obtain this paper and other Coleago conferencepapers and articles from our website.

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

Stefan Zehle, Director, Coleago Consulting Ltd, 47 Holland Park, London W11 3RS, UKTel: +44-7974-356 258 e-mail: [email protected]

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