1. 1
GSMA mHealth
Country Feasibility Report Methodology
Table of contents
Methodology summary.................................................................................................................................................................1
Opportunity indicator metrics ..................................................................................................................................................2
Output categories....................................................................................................................................................................2
Health burden .....................................................................................................................................................................3
Reach and growth potential................................................................................................................................................3
Ability to pay .......................................................................................................................................................................3
Justification for use of selected indicators ...................................................................................................................................4
Example of opportunity matrix indicator output ...........................................................................................................................5
Total addressable market forecast methodology………………………………………………………………………………..……..6
Methodology summary
The intricacies of market evaluation are such that no single data source should be relied on in isolation. The most robust
approach is to consider an amalgam of qualitative and quantitative inputs.
For the GSMA mHealthCountry Feasibility Reports the primary qualitative inputs have come from the GSMA, in-country
projects and initiatives and include multiple face-to-face interviews with all applicable mHealth value chain members. The
GSMA has madediscoveries and investigated assertions in-country, in the process of forging relationships with mHealth
stakeholders andbuilding scalable and sustainable business cases amongst public and private stakeholders. This data has
been combined with the modelling of quantitative data inputs including socio-demographic indicators and expert opinion
using Likert scaling and weighted decision matrices, considering the multiple criteria for success and bias of involved
stakeholders. This weighted view allows each individual stakeholder and value chain player to independently gauge the
viability of mHealth within the target country and thus reduces the potential for extrapolation of data bias. The overall aimof
this approach was to take a balanced view leveraging analytical evaluationand striving for a methodological triangulation
method.
The overall feasibility of mHealth is gauged using a combination of quantitative opportunityinputs based on a list of
comparable data metrics across all of the 10 target countries combined with quantitative and qualitative metrics derived
from in-country GSMA programmes, monitoring and evaluation and product and service development initiatives.
The mNutritioncountry feasibility reports concentrate on eight defined target countries. Including, Ghana, Nigeria, Malawi,
Mozambique, Rwanda, Uganda, Mozambique and Zambia. The modelling considers 10 sub Saharan African in order to
provide a statistically valid data-set that is broadly comparable across SSA.

2. 2
DRIVERSCATEGORIES
• Maternal mortality rates
• Infant mortality rates
• Child mortality <5 rates
• Child aged <5 stunted rates
• No. pregnant mothers
• No. mothers with children <5
• Unique mobile subscriber penetration
• Mobile sub penetration 5-year growth rates
• Mobile Geographical coverage
• Per capita income
• Percentage above poverty line
• Percentage of out-of-pocket spending health care
• ARPU divided by per capita income (12mnth period)
• GINI coefficient
• Income held by top 10% of population
• Percentage of government health spending per capita
• Percentage of health services funded by NGO
Maternal mortality
Child mortality
Incidence of stunting
Target audiences
Market growth indicators
Business to consumer potential
Business to business potential
Health burden
Available market
Market potential to pay
PROXY INDICATORS
For modelling purposes comparable data points where selected across the 10 indicator countries. In some instances this
was not the most up-to-date information available.
Opportunity indicator metrics
The opportunity matrix indicator considers a number of data points categorised into specific indicators relating to core
mHealth drivers.
Figure 1– Constituent data inputs and how they combine to define anmHealth driver
Source: GSMA M4D
The majority of data inputs considered are positive but there are a number of important indicators that are detrimental to
market opportunitiese.g. a high Gini coefficient or high percentage of income held by the top 10% of any given population.
The inclusion of both positive and negative indicators are important factors toward modelling actual, real-world scenarios
incorporated into the analysis.
The overall output from this reduction model approach isa comparative index scoredabove or below the ideal market
conditionsaveraged across the 10 Sub-Saharan African countries that the GSMA nutrition initiative considered.
Output categories
As markets evolve with time, indicator data points change, particularly as commercial propositions propagate. The metrics
selected for the opportunity indicator matrix were chosen as the most revealing for showing the current status of mHealth in
the countries we are considering. As such they should be seen as a snapshot of the market rather than a long-term forecast.

3. 3
Health burden
Metrics considered for health burden where selected to give an indication of the potential addressable market defined
against the parameters of improving nutrition for under-serviced markets and in particular for women and young children.
Both addressable market and potential loss of economic units are considered as indicators of health burden.
Addressable market
The overall addressable market for mHealth is considered within the parameters of maternal and under-five child health in
combination with mortality and stunting indicators.
Mortality and stunting indicators
The mortality rates of women at childbirth, infants and newborns are considered as indicators of health burden in particular
as a grievous loss to society from a social and economicperspective. The logic of targeting these segments is clear and is
based on their socio-economic potential and the impact for developing nations being effectively cut off before they reach
optimal age to fulfil this potential. The impact of ongoing health burdenis also evaluated in terms of care requirements by
considering stunting in children under-five (nutritional indicator).
Reach and growth potential
The overall size of the target addressable markets was cross referenced with the potential growth of the primary delivery
medium (mobile devices) to give an indication of the longer term viability of mHealth services.
Combining market potential indicators
A market’s potential is defined by considering a combinationof factors incorporating features that are positive and negative
to growth. As a consequence the market potential indicators for the comparative index were combined to better emulate this
market feature. Penetration rate, unique subscriber penetration rate and subscription growth over a preceding five year
period gave an indication of the market potential and impetus behind mobile growth. Geographic mobile coverage was also
incorporated in this calculation, indicating the available audience’s ability to access mobile. Alow geographic coverage acts
as a counterbalance to high penetration (population divided by number of active SIMs) and/or high growth, to give a truer
indication of market potential and the addressed market. This is an important consideration with mHealth due to the lack of
access to health services in rural and remote regions.
Ability to pay
The ability to pay considers demand-side indicators including consumption patterns and available resources to purchase
goods from a commercial B2C perspective and combines these with expenditure levels on comparable B2B indicators.
B2C category definition
The commercial proposition for a given service is made up of a large number of factors that influence propensity and
potential to spend. Some of these indicators are clear e.g. available dispensable income, but there are also less definable
indicators such as propensity to spend or where wealth is situated within a society. As a result the number of data points
considered in the B2C category are correspondingly large.
Consumption factors are considered through gross domestic product (GDP)per capitaand the number of available potential
audiences, defined as those above the poverty line. Propensity to spend indicators include percentage of GDP spent on
mobile combined with propensity to spend on health out of the consumer’s own pocket. Less definable factors, including the
way money is distributed and allocatedare captured by considering Gini coefficient and percentage of wealth inequality (the
degree of total wealth held by the top 10% of society). The higher the inequality the greater the obstacle to a volume-based
market opportunity resulting from asmaller addressable consuming audience. The assumption made here isthat a volume-
based business proposition is a better long-term opportunity. With these indicators ranking is reversed – the higher the
figure/inequality the lower the score.
B2B category definition
The data points considered to illustrate the opportunity for B2B mHealth are quite narrow. The metrics effectively identify
mHealthas a subsidisation opportunity to be financed by either governments, NGOs or other funders. The revenue
opportunity comes as part of a wholesale or B2B relation between the constituent providers (mobile operators, value added
service (VAS) providers) and wholesale consumers (government, NGO and other provisioning agents).

4. 4
These metrics are valid at this point based on the industry’s limited options for funding in developing Sub-Saharan African
countries and the as yet unproven commercial proposition for mHealth in the region.
Justification for use of selected indicators
In order to make a valid estimation across the target countries comparable metrics were required. Within Sub-Saharan
African data point methodological rigour, frequency of collection and replicability differ widely. The indicators chosen for the
opportunity indicator metrics were selectedbased on their equivalence originating from single sources and time periods.
Figure 2- mHealth opportunity demonstrated by selected metrics
Source: GSMA M4DmHealth
Figures 1 and 2 provide the justifications from a mHealth service feasibility perspective for selection of the various indicator
metrics.

5. 5
Figure 3 - Data selection & justification
Source: GSMA M4D mHealth
Example of opportunity matrix indicator output
Figure 4 shows the type of indicator output from the opportunity matrix indicator. The target initiative countries are on the
left, and in this instance show reach/addressable market. The 10 countries are ranked within this scale by providing an index
score for each country, applicable to the particular driver in consideration. An index on or around 1 is desirable. An index
higher or lower than one denotesan opportunity or absence respectively.
Figure 4 - Example output opportunity matrix indicator reach/addressable market

6. 6
Source: GSMA
Example of combined opportunity matrix indicator output
The opportunity matrix outputs are combined to produce an overall indicator for mobile health feasibility in each of the 10
mNutrition countries.
Figure 5–Example output combined opportunity matrix indicators
Source: GSMA M4D mHealth
Figure 5 shows the combined outputs for health burden, reach and ability to pay. The combined score indicates the overall
score of all indicators as a collective ranked scale.
Comparative size of market opportunity
While the combined scale score output gives an idea of the overall feasibility of particular mobile health indicators it does not
show the relative size of the market opportunity being based on a 10 point scale. For example a country ranked at 10 and a
country ranked at a scale 9 are adjacent on the scale but the difference between these two points can be insignificant or
considerable. The 10 point scale approach does not capture this nuance. Nigeria, for example, is ranked (scaled) highest
across the evaluation indicators but also has a substantial market opportunity compared with ninth ranked Tanzania_
whereas Zambia and Cote D’Ivoire, which rank seventh and eighth respectively, are relatively similar in regard to the size of
their market opportunity.
Figure 6 - Example output combined indicator rank and comparative size of marketopportunity
Ghana Kenya
Uganda Cote D'Ivoire
Nigeria Malawi Rwanda Mozambique
1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1 0.9 0.8 0.7 0.6 0.5 0.4 0.2 0.1 0
Tanzania
Zambia
INDEX
Nigeria 1.6
Ghana 1.1
Uganda 1.1
Malawi 1.1
Tanzania 1.0
Zambia 1.0
Kenya 0.9
Cote D'Ivoire 0.9
Rwanda 0.9
Mozambique 0.6
Index percentage share of overall opportunity
0% 100%
* Index data has been rounded up

7. 7
Source: GSMA M4D mHealth
Figure 6 compare the particular ranking of the country and the size of that opportunity as a combined feature of all the
indicator categories. The relative combined opportunity is an indication of the capacity of a country to be developed and
provides additional detail for the country evaluation process. The respective countries position on the scale gives an
additional indication of the ease with which mobile health services might be launched (degree of obstacle vs opportunity).
VAS and mHealth market evolution methodology
The mobile value added service evolution indicator shows the specific point of vaue added service development and the
comparative state of mHealth status development within the 10 GSMA mNutrition comparison countries.
Figure 7–Example GSMA total addressable market forecast methodology
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0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
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0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
CharTitle
Good
Excellent
Poor
Overal position of country in scale
Indicates size of
opportunity for
combined health
burden, reach &
ability to pay
Zambia

8. 8
Source: GSMA M4DmHealth
Each of the comparator countries have been mapped onto the total addressable market service evolution curve (Sigmoid
Curve) with their respective positioning showing the status of VAS and mHealth service evolution (higher or lower on the
curve).
A number of indicator metrics where considered to make a comparative assumption. These included:
 ARPU and service data ARPUsplits (voice vs data) when available.
 Ratio of sophisticated versus simple mobile services
 Total number of mhealth services tracked by the GSMA,
 Number of persons using mhealth services,
 Analysis of smart phone VAS capable devices in-country compared against simple devices
 Number of mobile data users
 Penetration of data service use
 Qualitative insights from in-country interviews.
A large gap between the VAS data status point (red circle) and the mHealth status point(blue circle) indicates a gap in the
market and a potential opportunity. The development of VAS services enablesand encouragesthe development of mHealth
services. For example, commercial M2M needs to have developed to a certain point before advanced monitoring mHealth
services are possible. If there is a large gap between VAS and mHealthservice pointsthere is the potential for a service
provider to deliver the relativemHealth service and fill this gap.
Total addressable market forecast methodology
The total addressable market for mHealthdefined in the GSMA Country Feasibility Reports was generated out of a larger
mHealth addressable market. The particular sub-segments considered for evaluation were women with children under the

9. 9
age of five and pregnant women.These sub segments were further delineated into literate and illiterate mobile service users
to gauge the addressable market for SMS and IVR mobile health services.
The issue of double counting, those within the target segment that are both pregnant AND have a child under the age of
five,was tackled by removing a percentage of the addressable audience in each country considered to fall under this
definition.
Figure 8 - GSMA total addressable market forecast methodology
Source: GSMA M4DmHealth
Service use was defined in terms of ownership and/or access to a mobile device.
Existing forecasts for literacy rates were taken from WorldBank data and proprietary GSMA Intelligence mobile access
forecasts were used to generate forward looking indicators.
The total addressable market will be slightly higher than shown in the Country Feasibility Reports as some from the literate
segment will use both SMSandIVRand these individuals have not been counted. The forecast considered individual use of
either SMSorIVR.
In order to avoid research bias and exploit cumulative knowledge a conservative forecast was opted for. Only women who
are pregnant or with a child under 5 are considered as addressable units. Moreover women who are pregnant and with a
child are counted only once despite different services being offered (effectively two separate service streams). The
addressable market is likely to be larger when women of a child bearing age and those who have a child and are pregnant
are included in the calculation.
The conservative approach is designed to considerearly stage mHealth services that align with the generic GSMAmHealth
service product; staged-based, aggregated MNCH messaging delivered over SMS and IVR.
The target segment of the GSMA nutrition initiative isitself part of a larger total addressable market for mHealth services.