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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: [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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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.
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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 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
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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 + 0.0471R2 = 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
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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 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
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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
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
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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 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: [email protected]