amman, jordan, 4 – 7 december 2006 strategic management – part ii forecasting lecture 5

24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 1

Amman, Jordan, 4 – 7 December 2006

Strategic Management – Part IIForecasting

Lecture 5

Fixed lines Forecasting


Fixed lines forecasting

• Forecasting methods for fixed lines demand depend on several factors:– Satisfaction rate (waiting list, network capacity)– Competition level between fixed operators

• Global approach fixed + mobiles + Internet is necessary taking into account different interaction effects:– Substitution– Stimulation– Complementary role with converged services


Definition of variables

Main linesin serviceML

New satisfied demandsSAT

CancellationsCAN

ML Dec year n = ML Dec year n-1 + SAT year n - CAN year n

WL Dec year n = WL Dec year n-1 + EXP year n - SAT year n

UD Dec year n = UD Dec year n-1 + UNX year n - EXP year n

New demands, satisfied demands, cancellations are flows data : given for a period, annual value = sum of 12 monthly values

Main lines in service and waiting list are stock data : given for a precise date, annual value = last monthly value

New expresseddemandsEXP

WaitinglistWL

Newunexpressed demandsUNX Unexpressed

demandsUD


Different methods depending on the network development

stage 1 stage 2stage 3

years

telephonedensity(%)

shortageof lines

networkextension

maturity

potential

in service

decline

stage 4


Stage 1 : shortage of lines


New Satisfied demandsSAT

CancellationsCAN

Potential Demand = POT = UN + WL +WLHigh unexpressed demand is caused by long waiting time and high tariffsHigh waiting list is caused by network saturation in some placesFew cancellations.The main issue is to optimize ML number with limited resources,Check occupancy rate in every local area for switches and outside plantImportance of localized demand for a right planning

New expresseddemandsEXP

WaitinglistWL

Newunexpressed demandsUNX Unexpressed

demandsUD


Stage 2 : network extension


New satisfied demandsSAT

Cancellations CAN

New demands and cancellations characterize customers behavior,Operator attract new demands by better tariffs.Unexpressed demand disappears and waiting list is decreasing.Satisfaction rate = ML / (ML + WL) is a strategic objectiveCancellation rate (CAN / ML) progressively increase.

Recommended method: forecast total demand ML+WL, and then split ML and WL.

New expresseddemandsDEM Waiting

listWL


Stage 2 : (continued)

New satisfied demands is controlled by operator depending on the extension of the network capacity (concept of total system ready to sale, usual bottleneck in outside plant).

A continuous monitoring of waiting list for every elementary area is necessary, with the root of the problem: switch, main cable, distribution.Coordination between commercial and technical units is crucial.

Waiting time (in months) = Waiting list * 12 / Annual new satisfied demand

Objective: to increase: Delta ML = ML Dec year n – ML Dec year n-1


Stage 2: Forecast of total demandwhen the waiting list is still high

Population P 2006

Population P in 2007,...2006

Total demand, ML+WL 2006

Density, D=(ML+WL)/P in 2006

Density, D=(ML+WL)/P in 2007,...2006

Total demand, ML+WLin 2007,...2006

= D * P

extrapolation


Stage 2 : continued

Main lines in service ML 2006

Main lines in service ML 2007,...2006

Total demand, ML+WL 2006

Satisfaction rate ML / (ML+WL) 2006

Satisfaction rate ML / (ML+WL) 2007,...2006

Total demand, ML+WL 2007,...2006

extrapolation


Stage 2 : continued : other future data

WL = waiting list = (ML+WL) - ML

percentage of cancellation at the base year = PCCAN : extrapolation of the value PCCAN n at future years

CAN n= ML n * PCCAN nSAT n = MLn - ML n-1 + CAN nDEM n= MLWLn - MLWL n-1 + CAN n

average waiting time (in months) = WL*12 / SAT


Stage 3 : demand satisfaction


New satisfieddemandsSAT

CancellationsCAN

ML Dec year n = ML Dec year n-1 + SAT year n - CAN year n

Delta ML = SAT year n - CAN year n

Network is fully available everywhere, average waiting time is so short that waiting list is ignoredNew expressed demands = New satisfied demands


Stage 3: Forecast of total demandwhen there is no waiting list

Population P 2006

Population P in 2007,...2006

Lines in service, ML 2006

Density, D= ML / P in 2006

Density, D= ML / P in 2007,...2006

Lines in service, ML= D * Pin 2007,...2006

extrapolation

Current situation at the base year

Forecast situationat everyfuture year


New jargon with the mobiles

• Churn = cancellationsCancellations are much higher in competitive markets(sometimes > 15%)

• Net adds = Delta lines, increase of mobiles in service

• Gross adds = New satisfied demands or new mobiles put in service

Gross adds = Net adds + churn


Churn

Churn means the percentage of subscribers who cancel their subscription for a service,

either they give up this service or they move to another supplier:

•for a better quality•for a lower price•for a better image / reputation.

Churn becomes higher :• when the global customer density increases• when the effective competition increases.

Churn is higher:• for new services• for some categories of customers


Stage 4: declineChurn becomes higher

than new satisfied demands

Factors to be investigated• Impact of connection fee and monthly rental fee• Substitution effect (mobiles instead of fixed lines)• Competition effect (aggressive competitors with new

technologies, quality of service, brand image)• Saturation of the whole market• New demand for Internet access and applications


The « last mile » of the fixed lines:

Poor maintenance,Lack of competenciesNo compliance withengineering rules.Lack of tools andconnecting devicesLack of control by themanagement

It is necessary to improve skills and to ensure an effective field management before constructing new networks in order to avoid to get the same results.Important factor for the evaluation during the privatisation process.

The replacement of the fixed lines by cellularnetworks could be faster than expected !!!


Fixed lines : examples of evolution

0

2 000 000

4 000 000

6 000 000

8 000 000

10 000 000

12 000 000

14 000 000

16 000 000

18 000 000

1998 1999 2000 2001 2002 2003 2004

Algeria

Bahrain

Djibouti

Egypt

Iran

Iraq

Jordan

Kuw ait

Lebanon

Libya

Malta

Morocco

Oman

Palestine

Qatar

Saudi Arabia

Syria

Tunisia

U.A.Emirates

Yemen


0

500 000

1 000 000

1 500 000

2 000 000

2 500 000

3 000 000

1998 1999 2000 2001 2002 2003 2004

Algeria

Bahrain

Djibouti

Iraq

Jordan

Kuwait

Lebanon

Libya

Malta

Morocco

Oman

Palestine

Qatar

Syria

Tunisia

U.A.Emirates

Yemen

Fixed lines examples of evolution


Fixed lines examples of evolution

0

200 000

400 000

600 000

800 000

1 000 000

1 200 000

1 400 000

1 600 000

1998 1999 2000 2001 2002 2003 2004

BahrainDjiboutiIraqJordanKuwaitLebanonLibyaMaltaMoroccoOmanPalestineQatarTunisiaU.A.EmiratesYemen


Will the fixed lines decrease in long term ?(impact of high density of mobiles)

?

Telephonenumbers

fixed

mobiles

years

The logistic curve is no longer appropriate for fixed lines, but it should be used for total number of telephone: fixed +mobiles

Internet effect

Mobiles effectPrepaid effect

actualforecast

?


Percentage of mobiles / total subscribers (fixed+mobiles) 2004

0 20 40 60 80 100

Iraq

Egypt

Syria

Yemen

Lebanon

Malta

Algeria

Saudi Arabia

Qatar

Jordan

Palestine

Tunisia

Emirates

Djibouti

Oman

Bahrain

Kuwait

Morocco


Extrapolation methods

Extrapolation of numbers of subscribers is carried out by using the penetration rate of a socio-demographic group, which is:

• population : very general• households : for residential subscribers• employees : for business subscribers

The choice of the formula to use depends on • the market segment,• the level of development• the specific constraints in the local environment.


Trends Formulafor density extrapolation

Linear formula y = M+ a * t

Parabolic formula y = M+ a * t + b * t2

Exponential formula y = M+ a * ebt

Logistic curve y = S / (1 + e –k * ( t – t0) )

Exponential logistic curve y = S / (1 + a * e b* t )m

Gompertz curve y = S / (1 + e –e ( a + b* t) )


Trends Formula

Formula used for monthly forecasts, at short term • Linear formula y = M+ a * t• Parabolic formula y = M+ a * t + b * t2

• Exponential formula y = M+ a * ebt

Formula for fixed lines at medium and long term•Logistic curve y = S / (1 + e –k * ( t – t0) )•Exponential logistic curve y = S / (1 + a * e b* t )m

•Gompertz curve y = S / (1 + e –e ( a + b* t) )

Formula for mobiles•Bass curve N(t) = N(t-1) + p * (M - N(t-1) ) + q * (N(t-1) /M) * (M-N(t-1) ))


Adoption Probability over Time

Time (t)

Cumulative Probability of

Adoption up to Time t

Introduction of product

(a)

Time (t)

Density Function: Likelihood of Adoption

at Time t

(b)

1.0

F(t)

f(t) = d(F(t))dt

24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 2628 February, 2006 Lecture 06 slide 11ITU/BDT/ HRD Marketing and Revenue Forecasts

Definition of the logistic curve

SD =

1 + e- k (T - T0)

Where :D = Telephone density at time TS = density saturation, (=asymptotic value of D at infinity)k = parameterT0 = parameter (symmetry center)

Telephone density

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010


Definition of the logistic curve

The formula of the logistic curve corresponds to the differential equation :

dD k * D * (S – D)dT S

Where dD/ dT represents the growth of the density D,

It means this growth is proportional both • to the number of people already equipped (D)

(pulling effect of the existing subscribers)• and to the number of people not yet equipped (S – D)

(when all people are equipped, saturation)


Use of the logistic curve (1)

The saturation is assumed to be : STwo points are necessary to define the parameters of the curve

-the initial point : year T1, density D1

-The target point : year T2, density D2

-The parameters k and T0 can be calculatedk = LN((S/T1 – 1) / (S/T2 – 1)) / (T2 – T1)T0 = T1 + LN (S/T1 – 1) / k

The intermediary points between T1 and T2 are carried out with the formula of the logistic curve


Use of the logistic curve (2)

Logistic curve is not suitable for specific services in a decline stage when churn is high.

Use logistic curve for an overall service at the national level or for a high level for all operators, taking into account the potential demand and the Internet effect.Estimate the substitution effect.Then split forecasts between fixed operators depending on assumptions of their respective attractiveness for new customers and the loyalty of their respective current customers.


Operator fixed F3

Operator fixed F2

General approach

Forecastsfor all fixed operators

Forecastsfor all mobiles operators

Operator fixed F1

Operator mobile M3

Operator mobile M2

Operator mobile M1

Potential demand at the national level for fixed and mobiles

Sharing between operators

1

2

churn

amman, jordan, 4 – 7 december 2006 strategic management – part ii forecasting lecture 5

Documents

year n wl

year n ud

waiting list

fixed lines demand

wl wlhigh unexpressed

satisfied demands

satisfaction rate

continuedmain lines