Mobile Broadband Rollout Business Case: Risk Analyses of the Forecast Uncertainties

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1 ISF 2009, Hong Kong, 2-24 June 2009 Mobile Broadband Rollou Business Case: Risk Analyses of he Forecas Uncerainies Nils Krisian Elnegaard, Telenor R&I

2 Agenda Moivaion Modelling long erm forecass for MBB Marke concenraion Risk framework Case sudy assumpions Resuls Conclusions and furher work

3 Moivaion Mobile broadband (MBB) has jus recenly enered he scene Evoluion in number of subscripions and raffic is very uncerain The radio access and backhaul elemens are he mos cosly pars of mobile (broadband) neworks Coss of hardware and sofware upgrades, and backhaul are heavily driven by BH raffic (which is uncerain) Undersanding he impac of uncerainies on he profiabiliy of Mobile Broadband is key How robus is he business case?

4 MBB profiabiliy is under pressure Will coss follow he raffic curve? Traffic (volume) Traffic Revenue Revenue Voice dominaed Daa dominaed Time

5 MBB profiabiliy is under pressure Cos efficiency Decreasing subscripion prices and increasing MBB volumes requires lower price per produced bi I is expeced ha he new echnologies will have reduced cos per bi Srong link o he increased capaciy driver Producion cos per bi Cos per Mbye 3G HSPA HSPA+ LTE

6 Demand forecas modelling A four parameer logisic model is used o describe he evoluion of he MBB poenial adopion rae a 00% coverage S ( ) = M ( + exp( α + β ) ) γ A marke demand concenraion funcion deermine he peneraion, where ς ( ) ( ( ) ) f ς is used o is he coverage a ime NB: Peneraion only equals adopion rae a full coverage

7 Parameer esimaion procedure ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) = + + = + + = ln exp, exp γ γ γ γ β β α β α S M S M M S M S ( ) ( ) β γ +α = = = ln : 0, 2 2 d S d ( ) = + ln γ β α i i S M

8 Marke demand concenraion I is ypical for an operaor s cusomer base o be geographically concenraed. For example, by covering 40% of he populaion, 80% of he addressable marke can be reached 00% Concenraion Facor Marke concenraion formula: Marke Demand Covered % 90% 80% 70% 60% 50% 40% 30% 20% 0% Marke Covered C Marke Covered C2 Marke Covered C3 f ( ) min( c,) ς ς = ς c : Marke concenraion facor ς : MBB nework coverage 0% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 00% Populaion Covered %

9 Marke demand concenraion Marke demand concenraion facor is ime dependen Price erosion of PCs (lapops) and mobile handses, economic growh (increase in GDP per capia) Differen regions will in general have differen ses of marke concenraion facors Example: Year Region 5, 4,5 4,2 3,8 3, 2,3,9,8,7,5 Region 2 3,7 3,2 2,8 2,4 2,,7,5,3,2, Region Region 2 00 % 90 % 80 % 70 % 60 % 50 % 40 % 30 % 20 % 0 % 0 % Coverage Demand covered 00 % 90 % 80 % 70 % 60 % 50 % 40 % 30 % 20 % 0 % 0 % Coverage Demand covered

10 Realised demand Coverage Rollou Marke Concenraion 00% 90% Concenraion Facor Effecive Demand Covered 80%, Marke Demand Covered % 70% 60% 50% 40% 30% 20% 0% 0% 0% 0% 20% 30% 40% 50% 60% 70% 80% 90% 00% Populaion Covered % Realised demand Demand poenial 45 % 50 % 40 % 45 % 35 % 40 % 30 % 35 % 25 % 20 % 5 % 0 % 30 % 25 % 20 % 5 % 0 % 5 % 5 % 0 % %

11 Risk framework Define uncerainies for seleced variables 200 and 205 forecass for poenial demand (large and small screen) 200 and 205 forecass for raffic volumes (large screen and small screen) Selec probabiliy densiy funcions for Mone Carlo simulaion The bea disribuion has been seleced Bea disribuion is flexible, can have differen shapes Bea disribuion has a lower as well as an upper limi Run Mone Carlo simulaion rials by using commercial simulaion sofware wih Excel Excrac resuls Uncerainy in oupu e.g. NPV, ranking of uncerain inpu variables

12 Case sudy A Mobile operaor owning 2G and 3G neworks sars offering mobile broadband services in Main assumpions: Generic counry of mill. inhabians 3G/HSPA in 200 MHz band Coverage mainly based on exising 2G and 3G sies 5 MHz paired specrum for 3G/HSPA 40% marke share HSPA upgrades (sofware and hardware) and LTE (Long Term Evoluion) in 2600 MHz band a a laer sage LTE assumed available in 202

13 Uncerainy variables Traffic large screen GB/monh/subscr.,32,5,7,9 2,2 2,4 2,7 3,0 200,70 min mean max µ ν σ 205 3,0,2,7 2, 2,22, , ,55556 Μ 6,0 min mean max µ ν σ γ 0,5 2,5 3,0 5,0 2,02 8,09 0, ,2 α 3,0770 β -0,259 min mean max µ ν σ 5,5 6,0 8,0 2,02 8,09 0, ,2 Traffic small screen GB/monh/subscr. 0,005 0,0 0,02 0,04 0,08 0,2 0,3 0, ,02 min mean max µ ν σ 205 0,6 0,0 0,02 0,03 2,63 2,63 0, ,5 Μ,0 min mean max µ ν σ γ 0,3 0,2 0,6 0,7 2,40 0,60 0,00 0 0,8 α 9,4943 β -2,2553 min mean max µ ν σ 0,8,0,3 2,00 3,0 0,00 0 0,4

14 Uncerainy variables Demand large screen , % 4 % 8 % 4 % 22 % 3 % 39 % 45 % % min mean max µ ν σ % 5 % 8 % 0 % 3,00 2,00,0 % 0 0,6 Μ 55 % min mean max µ ν σ γ,3 35 % 45 % 50 % 3,04,52 3,0 % 0 0,66667 α 2,9647 β -0,5983 Demand small screen % 3 % 5 % 9 % 4 % 20 % 25 % 30 % % min mean max µ ν σ % 4 % 5 % 7 % 2,33 4,67 0,50 % 0 0,33333 Μ 40 % min mean max µ ν σ γ 2,0 20 % 30 % 35 % 3,04,52 3,00 % 0 0,66667 α 2,0853 β -0,4939

15 Resuls NPV disribuion

16 Resuls ROI disribuion

17 Resuls sensiiviy ranking Inpu variables Conribuion o variance in NPV Demand poenial Small screen 0,49 Demand poenial Large screen 0,27 Demand poenial Large screen 0,3 Demand poenial Small screen 0,08 Large Screen raffic per sub ,02 Large Screen raffic per sub ,0 Heavy raffic scenario Demand poenial Small screen 0,43 Large Screen raffic per sub ,24 Demand poenial Large screen 0,20 Demand poenial Small screen 0,07 Demand poenial Large screen 0,04 Large Screen raffic per sub ,02

18 Conclusions The profiabiliy of mobile broadband is sensiive o he number of subscripions and raffic Long demand erm forecass (large and small screen are he mos sensiive variables measured by rank correlaion Long erm forecas of small screen subscripions has heavy impac on revenue Long erm forecas of large screen subscripions has heavy impac on boh revenue and coss Long erm raffic forecass of large screen raffic has heavy impac on coss The uncerainy in he forecass of marke concenraion facors was minimal for he given assumpions compared o he uncerainy in demand poenial and large screen raffic

19 Furher work Long erm MBB forecass in specific markes Long erm forecass of MBB marke concenraion facors in specific markes Deermined by income levels, demography, compeing infrasrucure, Furher sudies on he impac of differen price plans Dependencies beween large screen and small screen demand forecass Impac on rollou risk

20

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