The liberalisation of the Myanmar telecoms market presents an opportunity for the international telecoms community to address the largest unconnected market in the world. The country has potentially 60 million new telecoms subscribers, but highly restricted access to services has limited the penetration rate to just 11% at the beginning of 2013. This report, which features the results of our major on-the-ground survey, as well as forecasts and insight, provides a unique view of the business opportunity in this formerly closed market. http://www.analysysmason.com/Research/Content/Reports/Myanmar-telecoms-market-report-2013/#.UeZo39Lql8E

1. © Analysys Mason Limited 2013
Myanmar mobile telecoms market:
forecasts 2013–2027 and consumer and SME survey
Analysys Mason report
Tom Mowat, Tom Rebbeck and Nuno Afonso
June 2013

2. © Analysys Mason Limited 2013
List of figures [1]
General background on the Myanmar market
Myanmar’s GDP nominal annual and per capita (PPP)
Myanmar’s total population by age and urban residents
Average monthly household income (2012)
Map of Myanmar and major cities
Share of total exports by sector (2011)
Telecommunication services’ subscribers and penetration
Deployed base transceiver stations by mid 2012
Payment method for mobile services by user type
Postpaid penetration by monthly household income
Overview of results
Implied mobile penetration in Myanmar
Implied mobile penetration from the survey results
Current adoption of mobile services
Urban mobile penetration by gender
Average monthly income per household by number of SIMs used
Average monthly income per household by number of SIMs used by city
Urban mobile users penetration by age
Outgoing minutes (% of respondents)
VAS, data usage and SMS usage (% of respondents)
VAS usage by age
Mobile services usage by business size
2
Interest in mobile services
Residential consumers for whom a mobile phone would fulfil a need
Likelihood that respondent would start using a mobile phone in the next year
Residential consumers for whom a mobile phone would fulfil a need by
income
Likelihood that respondent would start using a mobile phone in the next
year by income
Business consumers for whom a mobile phone would fulfil a need or
solve a business problem
Likelihood that respondent would start using a mobile phone in the next year
Business consumers for whom a mobile phone would fulfil a need or
solve a business problem by business size
Likelihood that respondent would start using a mobile phone in the next
year by business size
Drivers and barriers for adoption of mobile services
Factors considered when selecting a mobile operator (% respondents)
Reasons for not purchasing a mobile phone
Factors considered when selecting a mobile operator (% of respondents)
Market forecast
Mobile subscribers and population penetration (2012-2027)
Comparison of our forecast for penetration in Myanmar and growth in
similar markets
Service revenue in USD by consumer type (2012-2027)
Monthly ARPU in USD by consumer type (2012-2027)
Service revenue in USD by location (2012-2027)
Monthly ARPU in USD by location (2012-2027)

3. © Analysys Mason Limited 2013
List of figures [2]
Competitive landscape: Residential
Current satisfaction level (% of respondents)
Most important reason to churn (% of respondents)
Current satisfaction level by age
Churn among very and somewhat dissatisfied users by age
Distribution of spend per month on mobile services of existing users
Distribution of expected spend per month on mobile services of non-users
Price paid per device, users (% of respondents)
Expected price per device, non-users
Price paid per SIM, users
Expected paid per SIM, non-users
Price per minute (% of respondents)
Price per SMS (% of respondents)
Where mobile users bought the SIM and mobile handset (% of respondents)
Competitive landscape: Business
Current satisfaction level (% of respondents)
Most important reason to churn (% of respondents)
Current satisfaction level by number of employees
Most important reason to churn by number of employees
Average spend per month, per employee/mobile connection
Average expected spend per month, per employee/mobile connection
Price paid per SIM, users
Expected paid per SIM, non-users
3
Annex: Market forecast assumptions
India’s ARPU and penetration comparison
Population split by location (2012)
Assumptions for estimation of mobile spend
Van Westendorp price sensitivity: illustration of intersections

4. © Analysys Mason Limited 2013
The average monthly household income in urban areas is three times the
value in rural areas
▪ The income values for urban areas are based on the survey
results
– Our sample size per city was:
▪ Mandalay - 200 respondents
▪ Mawlamyne - 200 respondents
▪ Taunggyi - 200 respondents
▪ Yangon – 400 respondents
▪ The estimate for the income in rural areas assumed a value
of USD270 for the country
▪ In this report we provide further detail on how the income
levels vary within urban and rural areas
– Urban population: using mobile services and not using
– Rural population: living in the borders and living inside the
country
10
Average monthly household income (2012)
Background
476
270
161
0
50
100
150
200
250
300
350
400
450
500
Urban Country Rural
USD

5. © Analysys Mason Limited 2013
The results in our survey provide a reasonable representation of mobile
phone users in urban areas
▪ The penetration implied by our survey is in line with mobile
penetration figures we have seen, providing comfort that the
sample is reasonably representative
▪ Our survey implies a mobile penetration figure of around 11-
13%
– The survey questioned adults between the age of 18 and
65 years old in urban areas
– Of these, 49% indicated they own a mobile phone (or
around 11% of the population)
– In rural areas, mobile coverage is almost non-existent. We
assume that mobile penetration in these rural areas is
close to zero
– Mobile penetration of people under 18 and over 65 will not
be zero, but can be assumed to be low.
19Results
Implied mobile penetration in Myanmar
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
%ofpopulation
Rural population
Urban population
(not surveyed as
<18 or >65 yrs)
Urban population
with no mobile
phone
Urban population
with a mobile
phone

6. © Analysys Mason Limited 2013
About the authors
Tom Mowat (Principal Analyst) Tom is the lead analyst for Analysys Mason's Asia–Pacific research programme and
is based in our Singapore office. Tom recently rejoined Analysys Mason from PwC, where he was a Manager in the
TMT Consulting team. He has extensive expertise in fixed and mobile broadband services, strategy and forecasting,
regulatory issues, cost-modelling and the particular issues affecting telecoms markets in developing regions. His
previous role at Analysys Mason was within the Custom Research team, developing project-based analysis for key
industry clients. Tom's experience includes leading numerous projects for telecoms service providers, vendors, and
regulatory and industry bodies, as an analyst and consultant. Tom has degrees in mathematical and particle physics
from the University of Nottingham and the University of Durham.
Tom Rebbeck (Head of Custom Research) leads Analysys Mason’s Custom Research team, drawing on his more
than 12 years of experience in the telecoms sector. He rejoined Analysys Mason from Telefónica, where he was a
Global Project Manager in the Mobile Data Group. In this role, Tom was involved in the launch of a number of
principal services across Telefónica’s worldwide footprint, including Telefónica-branded applications, application
stores and mobile advertising. Prior to this, he was a Principal Analyst for Analysys Mason, involved in numerous
strategic projects for clients.
Nuno Afonso (Consultant, Custom Research) joined Analysys Mason in 2010, having participated in a wide variety
of projects across different areas. These included mobile backhaul, dark fibre, spectrum, telecoms software and
CSPs' operational efficiency. As part of the project scope, Nuno conducted market analysis, market sizing and
forecast as well as benchmarks. Prior to joining Analysys Mason, Nuno led numerous projects with an international
scope, including the optimisation of performance management methods for a leading financial services provider in
Belgium; the assessment of market entry strategy in Latin America for a key player in the water and waste
management sector; and strategic planning for an organisation in the FMCG sector. Nuno has a Business
Administration degree from Universidade Nova de Lisboa and an MBA from Manchester Business School.
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