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Amman, Jordan, 4 – 7 December 2006 Strategic Management – Part II Forecasting Lecture 5

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Amman, Jordan, 4 – 7 December 2006

Strategic Management – Part II

Forecasting

Lecture 5

Fixed lines Forecasting

ITU/BDT/ HRD 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

ITU/BDT/ HRD Fixed lines forecasting

New

unexpressed

demands

UNX

New

expressed

demands

EXP

New satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

Unexpressed

demands

UD

Waiting

list

WL

MLDec year n= ML Dec year n-1+ SAT year n- CANyear n

WLDec year n= WL Dec year n-1+ EXPyear n- SATyear n

UDDec year n= UD Dec year n-1+ UNXyear n- EXPyear 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

ITU/BDT/ HRD Fixed lines forecasting

Different methods depending on the network development

telephone

density

(%)

stage 3

stage 4

stage 2

stage 1

potential

in service

years

shortage

of lines

decline

maturity

network

extension

ITU/BDT/ HRD Fixed lines forecasting

New

unexpressed

demands

UNX

New

expressed

demands

EXP

New Satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

Unexpressed

demands

UD

Waiting

list

WL

Potential Demand = POT = UN + WL +WL

High unexpressed demand is caused by long waiting time and high tariffs

High waiting list is caused by network saturation in some places

Few cancellations.

The main issue is to optimize ML number with limited resources,

Check occupancy rate in every local area for switches and outside plant

Importance of localized demand for a right planning

ITU/BDT/ HRD Fixed lines forecasting

New expressed

demands

DEM

New satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

Waiting

list

WL

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 objective

Cancellation rate (CAN / ML) progressively increase.

Recommended method:

forecast total demand ML+WL, and then split ML and WL.

ITU/BDT/ HRD Fixed lines forecasting

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

ITU/BDT/ HRD Fixed lines forecasting

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

Population P 2006

Total demand, ML+WL 2006

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

extrapolation

Density, D=(ML+WL)/P

in 2007,...2006

Population P

in 2007,...2006

Total demand, ML+WL

in 2007,...2006

= D * P

ITU/BDT/ HRD Fixed lines forecasting

Stage 2 : continued

Main lines in service ML 2006

Total demand, ML+WL 2006

Satisfaction rate ML / (ML+WL) 2006

extrapolation

Satisfaction rate

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

Total demand, ML+WL

2007,...2006

Main lines in service ML 2007,...2006

ITU/BDT/ HRD Fixed lines forecasting

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 n

SAT n = MLn - ML n-1 + CAN n

DEM n= MLWLn - MLWL n-1 + CAN n

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

ITU/BDT/ HRD Fixed lines forecasting

Stage 3 : demand satisfaction

New satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

MLDec year n= ML Dec year n-1+ SAT year n- CANyear n

Delta ML= SAT year n- CANyear n

Network is fully available everywhere,

average waiting time is so short that waiting list is ignored

New expressed demands = New satisfied demands

ITU/BDT/ HRD Fixed lines forecasting

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

Current situation at the base year

Population P 2006

Lines in service, ML 2006

Density, D= ML / P in 2006

extrapolation

Forecast situation

at everyfuture year

Density, D= ML / P

in 2007,...2006

Population P

in 2007,...2006

Lines in service, ML= D * P

in 2007,...2006

ITU/BDT/ HRD Fixed lines forecasting

New jargon with the mobiles

- Churn = cancellations

Cancellations 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

ITU/BDT/ HRD Fixed lines forecasting

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

ITU/BDT/ HRD Fixed lines forecasting

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

ITU/BDT/ HRD Fixed lines forecasting

The « last mile » of the fixed lines:

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

Poor maintenance,

Lack of competencies

No compliance withengineering rules.

Lack of tools andconnecting devices

Lack 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.

ITU/BDT/ HRD Fixed lines forecasting

Fixed lines : examples of evolution

ITU/BDT/ HRD Fixed lines forecasting

Fixed lines examples of evolution

ITU/BDT/ HRD Fixed lines forecasting

Fixed lines examples of evolution

ITU/BDT/ HRD Fixed lines forecasting

forecast

Will the fixed lines decrease in long term ?(impact of high density of mobiles)The logistic curve is no longer appropriate for fixed lines, but it should be used for total number of telephone: fixed +mobiles

?

Telephonenumbers

mobiles

Prepaid effect

Mobiles effect

Internet effect

fixed

?

years

ITU/BDT/ HRD Fixed lines forecasting

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

ITU/BDT/ HRD Fixed lines forecasting

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.

ITU/BDT/ HRD Fixed lines forecasting

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) )

ITU/BDT/ HRD Fixed lines forecasting

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) ))

ITU/BDT/ HRD Fixed lines forecasting

Adoption Probability over Time

(a)

1.0

Cumulative Probability of Adoption up to Time t

F(t)

Introduction of product

Time (t)

(b)

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

Density Function: Likelihood of Adoption at Time t

ITU/BDT/ HRD Fixed lines forecasting

Time (t)

D =

1 + e

- k (T - T0)

Definition of the logistic curveWhere :D = Telephone density at time T

S = density saturation, (=asymptotic value of D at infinity)

k = parameter

T0 = parameter (symmetry center)

ITU/BDT/ HRD Fixed lines forecasting

ITU/BDT/ HRD Marketing and Revenue Forecasts

28 February, 2006

Lecture 06 slide 11

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)

ITU/BDT/ HRD Fixed lines forecasting

Use of the logistic curve (1)

- The saturation is assumed to be : S
- Two 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 calculated
- k = 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

ITU/BDT/ HRD Fixed lines forecasting

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.

ITU/BDT/ HRD Fixed lines forecasting

General approach

Potential demand at the national level for fixed and mobiles

1

churn

Forecasts

for all fixed operators

Forecasts

for all mobiles operators

2

Sharing between operators

Operator fixed F3

Operator mobile M3

Operator fixed F2

Operator mobile M2

Operator fixed F1

Operator mobile M1

ITU/BDT/ HRD Fixed lines forecasting

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