1. Strategic Management – Part II Forecasting
Amman, Jordan, 4 – 7 December 2006
Strategic Management – Part II
Forecasting
Lecture 5
Fixed lines Forecasting
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

2. 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
1 June, 2018

3. Definition of variables
New
unexpressed
demands
UNX
New
expressed
demands
EXP
New satisfied
demands
SAT
Cancellations
CAN
Unexpressed
demands UD
Waiting
list WL
Main lines
in service ML
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
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

4. Different methods depending on the network development
telephone
density
(%)
stage 3
stage 4
stage 1
stage 2
potential
in service
years
shortage
of lines
network
extension
maturity
decline
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

5. Stage 1 : shortage of lines
New
unexpressed
demands
UNX
New
expressed
demands
EXP
New Satisfied
demands
SAT
Cancellations
CAN
Unexpressed
demands UD
Waiting
list WL
Main lines
in service ML
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
1 June, 2018

6. Stage 2 : network extension
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
1 June, 2018

7. 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
1 June, 2018

8. Stage 2: Forecast of total demand when 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,
Population P
in 2007,
Total demand, ML+WL
in 2007,
= D * P
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

9. Main lines in service ML 2006
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,
Total demand, ML+WL
2007,
Main lines in service ML 2007,
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

10. 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
1 June, 2018

11. Stage 3 : demand satisfaction
New satisfied
demands
SAT
Cancellations
CAN
Main lines
in service ML
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 ignored
New expressed demands = New satisfied demands
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

12. Stage 3: Forecast of total demand when 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 every future year
Density, D= ML / P
in 2007,
Population P
in 2007,
Lines in service, ML= D * P
in 2007,
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

13. 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
1 June, 2018

14. 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
1 June, 2018

15. Stage 4: decline Churn 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
1 June, 2018

16. The « last mile » of the fixed lines:
The replacement of the fixed lines by cellular networks could be faster than expected !!!
Poor maintenance,
Lack of competencies
No compliance with engineering rules.
Lack of tools and connecting devices
Lack of control by the management
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
1 June, 2018

17. Fixed lines : examples of evolution
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

18. Fixed lines examples of evolution
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

19. Fixed lines examples of evolution
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

20. ITU/BDT/ HRD Fixed lines forecasting
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 ?
Telephone numbers
mobiles
actual
forecast
Prepaid effect
Mobiles effect
Internet effect
fixed ?
years
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018 16

21. Percentage of mobiles / total subscribers (fixed+mobiles) 2004
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018

22. 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
1 June, 2018

23. Trends Formula for 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
1 June, 2018

24. 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
1 June, 2018 10

25. Adoption Probability over Time
1.0
Cumulative Probability of Adoption up to Time t
F(t)
Introduction of product
Time (t)
(b)
Density Function: Likelihood of Adoption at Time t
f(t) = d(F(t)) dt
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018
Time (t)

26. Definition of the logistic curve
Where : D = Telephone density at time T
S = density saturation, (=asymptotic value of D at infinity)
k = parameter
T0 = parameter (symmetry center) S
D =
1 + e
- k (T - T0)
ITU/BDT/ HRD Marketing and Revenue Forecasts
ITU/BDT/ HRD Fixed lines forecasting
1 June, 2018
28 February, 2006
Lecture slide 11 11

27. 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
1 June, 2018 12

28. 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
1 June, 2018 13

29. 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
1 June, 2018 14

30. General approach 1
Potential demand at the national level for fixed and mobiles
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
1 June, 2018