maximizing data profitability

Maximizing Mobile Broadband Data Profitability

Next Generation Telecoms Summit - Portugal

30. September 2009

Kim Kyllesbech Larsen (T-Mobile).

T-Mobile Confidential and ProprietaryAll rights reserved. No part of this report may be reproduced in any material form without the written permission of the copyright owner.

2

Mobile broadband data is profitable (or can be made so).

(data) Profitability can be optimized (but how?).

Cost distribution & allocation is an important remedy for broadband data profitability assessment (though in the end total cost is what matters).

Technology is important for data profitability with focus on cost & performance (e.g., network sharing & traffic management).

In the end what really matters is overall profitability.

Some discussion points ….


3

Top internet applications going mobile – the demand for mobile broadband.

E-mail / browse

Play on-line

Share photo

Download music

Stream music

Social networks

Video shows

Watch movie

Up-to 11 hours per week

ca. 5 min per day (Free 100MB/month)

35+ min per day, 800+ mio photo & 8+ mio videouploads per month, 30+ mio mobile users

3.3 mio iPhone users↑ (July 2008)64 - 128 kbps stream

30+ mio accounts & 2.8+ bn songs↑

7 mio Hulu users (Apr’09), 325 min / viewerand an average Hulu video 10+ min

10 mio users & 700k instant watch (Jan’09)

100+ bn emails/day, 40+% > 5 MByte↑


4

Voice ARPU expected to decline with 5% pa over the next 5 years1.

Data ARPU expected to increase with 4% pa same period1.

Data ARPU is a factor of 3 lower than Voice ARPU.

Data growth is not likely to compensate the decline in Voice revenue.

Most Western European markets have reached customer saturation with little organic growth and with revenue decline.

Decreasing prices and shrinking margins are putting substantial pressure on cost reduction innitiatives.

Nevertheless, WEU EBITDA margin is anticipated to increase slightly over the next 5 years1

Mobile profitability – Western Europe.

1 Pyramid Research Western Europe June 2009.


5

Western European profitability expectations.

Commentary

Service revenue is projected to decline in most of WEU.

Opex measures are anticipated to provide margin growth compensating the revenue decline.

HSPA+ / LTE will be deployed over the same period providing high-quality (i.e., high speed) mobile broadband to the WEU mobile subscribers.

Though, operators will be running 3 technologies in parallel hence likely Opex (and Capex) increase.-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

-2 -1.5 -1 -0.5 0 0.5 1

EBITDA versus Opex CAGR 2008 - 2012

Source: Merrill Lynch European Telecoms Matrix Q3 2009.

EBITDA CAGR

Opex CAGR

Cost efficiency

compensates

revenue decline

Green diamond: revenue increase

Red diamond: revenue decrease


6

FinancialsWEU market dynamics

Western Europe mobile environment is a study in cost optimization, while exploring new growth opportunities with mobile broadband data.

Revenue

Opex

Ebitda

GROWTH

(APAC / CEE)

OPTIMIZATION

(WEU)

Years

Customersubscriptions

ARPU

Years

-2.0% pa

+2.4% pa

Steady-state like

(WEU)


-2.0% pa

7

The service revenue distribution is expected to undergo significant differences with mobile broadband deployment.

2008(HSPA)

2012(HSPA+/LTE)

2020+(LTE)

Revenue1,2

1 Pyramid Research Western Europe June 2009, 2 Country by Country extrapolated and aggregated from Pyramid data.

Voice & SMS80%

Data20%

Voice & SMS90%

Data10%

Mobile broadband data revenue will become increasingly important component of the total revenue.


Data35%>

Voice & SMS65%<

100% Data

8

Mobile broadband traffic is expected to grow explosively over the next 10 years.

Stationary70%

Nomadic20%

Mobile10%

Stationary79%

Nomadic20%

Mobile1%

Today 2020+

Throughput 1 100+ (CAGR 50% pa)

Volume 1 300+ (CAGR 70% pa)


>

>

Can technology cost be controlled under such conditions?

9

The structure of profitability.

Total Revenue

Technology cost (ca. 15% – 20% of Opex)

Usage cost−

Market invest

−

= EBITDA

Personnel

Other cost

−

−

−

Network depreciation (ca. 10% to 20%)−

= Contribution Margin (after depreciation)

Spectrum amortization−

= EBIT

Total Opex

WEU between 22% and 44% with an

average of 35%2

Data revenue share1

15% and 35% incl. msg.

1 Pyramid Research Western Europe June 2009, 2 Merrill Lynch European Telecoms Matrix Q3 2009.


Conceptual

view

10

Average WEU mobile operator anno 20091.

1 As can be estimated from Merrill-Lynch WE Telecom Matrix, business modeling, financial analysis, 2 Excluding SMS

Total Revenue

EBITDA

+ 35%

10% – 15%

15% - 20%

10% - 15%

10% - 15%

<15%

% of total Revenue

ca.10%+

Usage cost

Market invest

Personnel cost

Technology cost

Other cost

(Illustration)

Depreciation would take another 10% to 20% off the EBITDA. Amortization depends largely on individual spectrum acquisition cost and

varies between countries and individual operators.

Data revenue 2


11

Take an WEU mobile Operator with an EBITDA margin of 35%.

Technology cost contribution of 20%.

Operator reduces its Technology cost base with 35%.

Examples of cost reduction initiatives Network sharing. Access technology consolidation. Cost distribution changes (i.e., Opex to Capex shifts for EBITDA

improvement). Outsourcing / managed services.

With everything else being equal an operator could gain a margin improvement 1 of ca. 5% saving 35% on its cost base.

Cost efficiency and its impact on margin – a 35% cost saving translate to a 5% margin improvement.

1 mnew = 1- ( 1 - oi ) (1 – mold ), with mnew and mold being the new and old margins, the cost reduction of cost component o i (which is taken relative to the total cost base).


12

It is compelling to use volumetric-based costing and pricing rationale – though it might be wrong

Volumetric based costing (€ per Byte) is often evoked to safe-guard profitability. While the concept is straightforward to use, it is also flawed due to wrong assumption that data traffic is voice-like.

Minimum Cost per ByteVolumetric network

capacity

Maximum Cost per Byte

Volumetric customerdemand

Profitability can be “safely assumed” if the “Price per Byte” is chosen

somewhere in-between

Vastly different data-traffic profiles (and resulting cost) can result in the same volumetric demand.

Conceptual

view


13

Main cost and investment driver for network capacity is busy hour traffic throughput – managing broadband traffic and quality of service levels will be essential to managing technology cost.

Same volumetric demand can cause vastly different networkcost and invest levels.

TRAFFIC PROFILE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour of Day

Throughput

9 HOURS

4 HOURS

11

22

Traffic profile 2 same volume as 1 but 40% higher busy hour

throughput

To keep same user experience in busy hour more network capacity needed

Higher invest level and network OPEX

required

Conceptual

view


14

Illustration of service BH throughput contribution. Every data service has a different network impact and in principle a different cost impact.

Mobile TV high BW impact but relative little volume in this illustration.

60%50%

40%

20%

8% 7%3% 2% 2% 1% 1% 0.5% 0.5% 0.5% 0.4% 0.2% 0.2%

100%

Mob

ile T

V Pre

mium

Ser

vice

Mob

ile T

V Bas

ic Ser

vice

Bus. F

ull C

onne

ctivit

y

Full C

onne

ctivit

y

Bus. A

t Hom

e Sur

fing

Mus

ic do

wnload

Bus. E

mail

Pull

Peer 2

Pee

rM

IM

Confin

ed C

onne

ctivit

y

Bus. C

onfin

ed C

onne

ctivit

y

Bus. E

mail

Pus

h

Mac

hine

2 M

achin

e

Real R

ing-to

nes

Email

Pull

MM

S out

going

MM

S inco

ming

BH Throughput in Mbps

Volume in MBHome surfing (wireless DSL)

Conceptual

view


Home

Surfin

g (w

ireles

s DSL)

15

Today’s mobile broadband customer gets the maximum possible service irrespective of price paid.

Most of today’s mobile broadband offerings have no speed (i.e., quality) versus price structure. “Fair use” policy address volumetric use and curbs speed after a volumetric limit has been reached.

Bandwidth

Time

Spectrum limit

Installed base limit

Un-controlled demand

All customers having same experience irrespective of

price they pay

Today’s situation – no QoS.

- Poor quality for all customers

- Uncontrollable Opex and Capex demand

- Service (price) differentiated quality

- Better control of network Opex & Capex


Time

Bandwidth

“Unlimited”

Gold

Silver

Bronze (best effort)

ConceptualviewQoS implemented.

Normal network expansion

Normal cost & cash impact

5% of base 20% of BW



50% of base up-to 20% of BW

High network expansion pressure

High cost & cash impact

16

RAN is the dominant technology cost element.

60%(10% - 12%) Radio

Core Network

15%

IT & Platforms

25%

Services,

10%

Maintenance & Repair

Other Costs

15%

Rental & Leasing

40%

Leased Lines

20%

Personnel Costs

15%

(Illustration) driver: (Sites)

driver: (Sites, traffic)

driver: (Sites, nodes)

driver: (Sites, nodes)

driver: (Sites, nodes, traffic)

driver: (customers, traffic)

driver: (customers)


17

UMTS36%

GSM64%

The cost distribution challenge is how to distribute the various cost elements across voice and data services – finding the key(s) (1 of 2)

RAN60%Core

15%

IT25%

Technology CostRAN Cost

(2G & 3G sites as key)

Ex. 10k locations, 6k 3G Nodes

with 80% co-location ratio.

UMTS25%

GSM75%

IT Cost(2G & 3G customers as key)

Core Cost(2G & 3G customers as key)

UMTS25%

GSM75%

(Illustration)


18

Voice – Data cost distribution keys

RAN Cost distribution keys Site-related cost split according

with data & voice subscribers. Transmission & Energy split

according with the bandwidth demand respective for data and voice.

Core Network distribution key(s) Cost split according with data &

voice subscribers. A possibility to split transmission

cost according with data bandwidth demand.

IT distribution key(s) Cost split according with data &

voice subscribers.

Technology cost split in access technology

The cost distribution challenge is how to distribute the various cost elements across voice and data services – finding the key(s) (2 of 2)

RAN68%

Core12%

IT20%

RAN56%

Core17%

IT27%

UMTS Cost

(32%)

GSM Cost

(68%)

(Illustration)


19

The technology cost distribution keys.

Personnel

RentalTransmisson

Services

Other

- GSM & UMTS sites.

- Data & Voice customer base.

- Possible to weight capacity sites on data cost.

- GSM & UMTS sites.


- GSM & UMTS sites.

- Bandwidth demand.

- GSM & UMTS sites.


- Alternative 50:50 between data & voice.

- GSM & UMTS sites.


- Bandwidth demand.

(Illustration)


20

Data profitability – the lower the data revenue share of the total revenue the higher is the likelihood for poor data profitability.

Data Revenue

ca. 20%

of Total Revenue

ca. 0%

15%

50%

30%

35%

20% EBITDA

Usage cost of data

Market invest related to data

Personnel cost related to data

Technology data cost

Other cost related to data

% of total Opex

category

(Illustration)

Considering depreciation and spectrum amortization would only make the profitability more negative.


21

Data profitability – once data revenue reaches a critical “mass” of the total revenue (in general) mobile broadband data will become profitable.

Data Revenue

ca. 30%

of Total Revenue

EBITDA

+ 28%

20%

50%

30%

35%

30%

% of total Opex

category

Usage cost of data

Market invest related to data

Personnel cost related to data

Technology data cost

Other cost related to data

(Illustration)

Depreciation would take another 10% to 20% off the EBITDA. Depending on spectrum acquisition cost, spectrum amortization might

make profitability problematic


22

The mobile data evolution towards introduction of true broadband data access and services.

Multimediacellular

Enhancedmobile services

Enhanced mobile multimedia

Mobile broadband communication

Optimisedmobile multimedia

Year

DL Throughput

2G / GSMGPRS /EDGE / EDGE 2.

3G / UMTSIntroduction of WCDMA / FDD.

3G / HSDPADownlink enhanced WCDMA/FDD

HSPA / HSPA +Downlink / Uplink enhanced with overall HSPA improvements.

Mobile broadband (LTE / NGMN)Broadband radio, IP based widebandPeer to Peer.Near-future wireless cellular.

1 Ultimate performance will depend on available spectrum bandwidth and link-budget.

2000 - 2003 2003 - 2004 2005 - 2006 2007 - 2012 2010+

32 - 128kbps 64 – 384kbps 0.384 - 4 Mbps1 0.384 – 14.4+ Mbps1 30+ (AVG) to 200+ (PEAK) Mbps1

GSM (GPRS / EDGE)

3G - UMTS

Enhanced UMTS

Mobile broadband

Based on “NGMN” paper by J. Horn

Optimised UMTS


Conceptualview

23

Ideal view – the ultimate data profitability – full network sharing scenario with 1 access technology (all voice considered data).

Total Revenue

=

Data Revenue

EBITDA

40%

to

55%

< 7%

15% - 20%

6% - 10%

8% - 12%

< 10%

% of total Revenue

Usage cost

Market invest

Personnel cost

Technology cost

Other cost

(Illustration)

EBITDA

+ 35%

10% – 15%

15% - 20%

10% - 15%

10% - 15%

<15%

The average WEU Mobile Telco anno 2009.


24

Network sharing and in particular RAN sharing can provides substantial technology cost savings

Shared backbone

Shared backhaul

Fewer sites

BSC : Base station controller (2G)

BTS : Base transceiver station (2G)

RNC : Radio network controller (3G)

nodeB

BTS

RNC

BSC

Network sharing target

Substantial savings from combining existing 2G, 3G and next-generation networks and jointly extending network coverage.

Fewer stations

IT1

Core1

IT2

Core2


25

Network sharing is in particular attractive in the initial deployment and modernization phase.

Network sharing is an important remedy in optimizing cost and cash and thereby improve over all profitability.

Initial deployment Steady State Wear-out /

Modernization

Capex prevention. Opex prevention. Cash optimized

startup. Best network.

Little Capex benefits. Opex savings. Significant write-off. Re-structuring cost. Better network.

Capex savings. Opex savings. Opex prevention. Minor write-off. Re-structuring cost. Better network.

UMTS LTE GSM / UMTS

WiMax


26

Commentary

Network sharing is an important remedy for cost and cash optimization.

Network sharing can be costly to implement.

Termination & restructuring cost.

Write-off exposure.

Organizational complexities.

Network strategic lock-in – radio network no longer differentiator.

Long-term engagement and complex exit scenarios.

More aggressive network sharing scenarios are likely to materialize in order to further bring Technology cost down.

RAN sharing cost benefits

Network sharing expectations and the BIG picture.

(Illustration)

35% Opex

saving on RAN network sharing

ca. 20% Opex

saving on

overall TechOpex

< 4%

On Total

Corporate Opex


27

Mobile broadband data is profitable (from an EBITDA perspective at least).

The on-set of broadband data profitability depends on data revenue reaching a critical level of the total turnover.

The degree of mobile broadband profitability depends on the cost distribution and cost allocation strategies.

Technology is one of the most important factors in optimizing (data) profitability.

Network sharing, traffic management and outsourcing/managed services are important remedies in cost, cash and hence (data) profitability optimization.

Summary


Thank you for your attention.

Contact details:[email protected] +31 6 2409 5202L http://www.linkedin.com/in/kimklarsen