maximizing data profitability
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Maximizing Mobile Broadband Data Profitability
Next Generation Telecoms Summit - Portugal
30. September 2009
Kim Kyllesbech Larsen (T-Mobile).
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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 ….
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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↑
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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.
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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
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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)
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-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.
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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)
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>
>
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.
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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
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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).
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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
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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
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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
Pull
Peer 2
Pee
rM
IM
Confin
ed C
onne
ctivit
y
Bus. C
onfin
ed C
onne
ctivit
y
Bus. E
Pus
h
Mac
hine
2 M
achin
e
Real R
ing-to
nes
Pull
MM
S out
going
MM
S inco
ming
BH Throughput in Mbps
Volume in MBHome surfing (wireless DSL)
Conceptual
view
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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
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Time
Bandwidth
“Unlimited”
Gold
Silver
Bronze (best effort)
ConceptualviewQoS implemented.
Normal network expansion
Normal cost & cash impact
5% of base 20% of BW
15% of base 40% of BW
30% of base 25% 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)
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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)
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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)
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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.
- Data & Voice customer base.
- GSM & UMTS sites.
- Bandwidth demand.
- GSM & UMTS sites.
- Data & Voice customer base.
- Alternative 50:50 between data & voice.
- GSM & UMTS sites.
- Data & Voice customer base.
- Bandwidth demand.
(Illustration)
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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.
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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
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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
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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.
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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
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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
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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
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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
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Thank you for your attention.
Contact details:kim.larsen@t-mobile.nlM +31 6 2409 5202L http://www.linkedin.com/in/kimklarsen
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