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

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

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

3. Definition of variables 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

4. 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

5. Stage 1 : shortage of lines 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

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

7. 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

8. 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

9. 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

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

11. 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

12. 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

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

14. 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

15. 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

16. 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

17. Fixed lines : examples of evolution ITU/BDT/ HRD Fixed lines forecasting

18. Fixed lines examples of evolution ITU/BDT/ HRD Fixed lines forecasting

19. Fixed lines examples of evolution ITU/BDT/ HRD Fixed lines forecasting

20. actual 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

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

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

23. 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

24. 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

25. 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)

26. S D = 1 + e - k (T - T0) 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) ITU/BDT/ HRD Fixed lines forecasting ITU/BDT/ HRD Marketing and Revenue Forecasts 28 February, 2006 Lecture 06 slide 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

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

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

30. 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