UNDERSTANDING WHERE TO PLAY

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UNDERSTANDING WHERE TO PLAY

markets

How income distribution data are key to capturing opportunity in emerging markets

Although emerging markets present large opportunities for multinational companies, uncertainty and high variability pose
challenges to developing confident growth strategies. This EIU Canback special report shows how income distribution data provide
a solution to these challenges by offering a detailed and nuanced understanding of market dynamics. We show that subnational
differences and the composition of income brackets across geographies play a key role in identifying market opportunity.
Furthermore, we show how predictive models can leverage income distribution data to provide more accurate and actionable
results for multinational companies looking to capture potential in emerging markets.

EMERGING MARKETS: OPPORTUNITIES
AND CHALLENGES

Understanding where to play remains a significant challenge
for companies. As growth has stagnated across many
developed consumer markets, multinational consumer goods
companies are pushed to look further afield. But the search for
growth can be fraught with danger. HSBC’s withdrawal from
Brazil and Turkey in 2015 and Nestlé’s scaling back in Africa
in the same year are two notable examples. So is there an
approach that can help to more accurately assess the market
potential for international expansion?
The answer lies with income distribution data, which
allow companies to accurately assess potential opportunities
and prioritize market expansion. Unlike national averages,
distribution data provide a nuanced picture of both
socioeconomic and regional differences across markets.
Income distribution models can be further augmented
to include additional factors, which allow for a deeper
understanding of market dynamics.
To properly assess market potential in emerging markets,
companies must move beyond simple averages and analyse
income distribution data in the context of a globally
relevant framework.

Quantifying the opportunity

Assessing market opportunity requires an understanding
of the Triple-A framework: the addressable market, which
contains all consumers within the relevant demographic
profile and income bracket; the available market, which
represents the market potential in areas where the product
is distributed; and the actual market, which is what people
truly consume.
To first understand market potential, a company needs
to be able to quantify the addressable population. Income
distribution is far more useful than simple averages for
this purpose. In emerging economies, a large subset of the
population cannot afford to purchase a product or service
regardless of whether they would like to. This contrasts with
consumers in affluent countries, who can largely choose to
purchase any good if they accept trade-offs.
In addition, emerging countries tend to see a larger
income disparity between rural and urban areas than affluent
ones, with a greater share of higher-income households
concentrated in cities. An example is provided in Figure 1,
which shows a heat map of India’s income per head by region
and city. Individuals in the areas closest to Delhi and Mumbai
have the highest income, while individuals in the rural north-

west earn much less. These subnational differences are crucial
for companies looking to strategically expand within a market
or optimize their in-country portfolio.
Subnational differences also matter when considering the
available market, as accessibility of branded consumer goods
is closely tied to the development of modern retail trade,
which in turn is highly correlated with the number of people
above a certain income (Traill, 2006). For these reasons,
income distribution rather than average income is key to
understanding market opportunity in emerging markets.

Table 1. Explanatory power of income on select products
and services

The importance of income distributions

Using income distribution data increases predictive accuracy
for consumer demand models. Analysis across 12 goods and
services shows that income distribution data have more
explanatory power than average income data in predicting
demand (Canback & D’Agnese, 2008).
Figure 2 shows the relationship between internet use and
different explanatory variables of demand. As expected,
countries with a greater number of households have more
people using the internet, and number of households explains
۶۲% of variation in global internet use.
In addition, average income (the average annual income
per household, per country) is a significant factor in predicting
internet use, explaining 75% of variation.
However, the approach that explains the greatest variation
in global internet use (88%) is the number of households
with annual income above a certain threshold, identified
through income distribution data. This threshold represents
the minimum income required for members of a household to
be able to afford internet use (in purchasing power terms).
In this way, income distributions allow us to estimate the
addressable market for a given good or service.
This analysis was repeated for 11 other goods and services
(Table 1). For each of these products or services, income
distribution proved a better predictor of demand than average
income. The same method can be used for any good or service,
highlighting the importance of income distribution data for
understanding demand.

Understanding the global consumer landscape

Income distribution data are very useful for companies
seeking to understand how to prioritize and capitalize
on market opportunities. The relative growth of different
socioeconomic classes can vary greatly from overall economic
growth, a fact that can have profound effects on market and
growth strategies.

The relationship between demand and income changes
as countries develop. Demand tends to follow an S-curve
as income rises, with three distinct phases (Figure 3).
Stage 2 markets will see demand rise faster with economic
development than Stage 1 or Stage 3 markets. These
differences in demand will reflect different strategic plans for
companies as they consider their focus across markets.
In the example in Figure 3, Mexico is above the curve for
consumption and on the cusp of high growth, suggesting
a strong opportunity as the market develops. This is not
uniform across cities, however. Mexico City’s Distrito Federal
and Monterrey have much higher income per capita and
correspondingly higher consumption, although they are below
the curve for their income level. This could be due to poor local
distribution, increased regulatory hurdles or competition
from a substitute good not available in other areas.
Whatever outside factors influence a market, understanding
subnational differences in income is an important first step to
understanding demand.
The dynamics across income segments within a market
also change. For example, although the total population
in Vietnam grew at a modest 1.1% between 2006 and 2016,
the upper and middle classes grew far more rapidly while the
marginalized class experienced a decline (Figure 4).
This has important implications for companies not only
in terms of the addressable market, but also with regard to
what type of product offerings is likely to be successful. In
the Vietnam example, the lower class saw a large absolute
increase in population in 2006-16 while the upper classes
saw a much smaller increase over the same period.
As higherincome consumers make up the potential market for premium
products, these changes in consumer demographics suggest
that the potential for premium goods and services in Vietnam
remains small despite high growth and that the opportunity
for affordable products is much larger, a realization that has
direct implications for portfolio strategy.
Average economic growth can mask dramatic changes
in a country’s consumer landscape. Companies should look
to changes in income distribution to understand how to
prioritize markets and capture opportunity.
Predictive models in emerging markets
Executives and marketers often look to predictive modelling
to combine information about trends and achieve a cohesive
picture of the market landscape. These models provide a
detailed, quantified view of where and when opportunities
will arise in the future, providing a springboard for executive
action.
Income distribution data can be incorporated into
predictive models for a more detailed understanding of market
dynamics. At the highest level, income distributions alone
are an effective approach for estimating category demand.
However, these market sizing models can be augmented to
include factors outside income. These additional variables
come in two layers: the first includes other macro data

covering industry and trade dynamics such as marketing
spend or distribution coverage and the second covers micro
data such as insights generated from consumer surveys, for
example, usage and attitude surveys.

The proof is in the pudding: Canback’s “Cheese in
China”

The results of these models can translate into real value for
clients. In 2005 EIU Canback (formerly Canback & Company)
used income distribution data to estimate the potential for
cheese in China. The approach was a Golder Tellis model,
a statistical demand model that identifies affordability
and availability as the key drivers of demand. Canback also
included factors such as price and milk consumption per capita
to capture additional variation in demand.
The results were striking. Contrary to existing projections
from industry experts, Canback estimated much stronger
growth and the development of a sizable cheese market by
۲۰۱۵٫ In addition, Canback identified the specific geographic
regions along the coast that would be responsible for this
growth. Actual growth rates proved consistent with these
estimates (Figure 5). By coupling income distribution data
with other relevant drivers, Canback was able to identify a US
$۵۰۰m ten-year opportunity experts had not yet recognized,
proving cheese in China was a worthwhile investment.


The Canback Global Income Distribution Database
(C-GIDD)

EIU Canback is uniquely able to leverage the incredible power
of income distribution data through its proprietary Canback
Global Income Distribution Database (C-GIDD). C-GIDD is the
world’s most comprehensive and detailed database for GDP,
population and income distribution data. The dataset covers
۲۱۳ countries, 697 regional subdivisions and 997 major cities
from 2001 to 2026, providing fully harmonized data for any
income bracket or socioeconomic class down to the city level.
C-GIDD allows Canback to model with confidence and
compare target demographics at the national, regional and
city level, unlocking actionable market strategies for clients.

CONCLUSION

How to identify, quantify and capture market potential in
emerging economies is a key question for multinational
companies. Although tapping into these opportunities can be
challenging, companies can look to income distribution data
for a deeper understanding of emerging-market dynamics.
Income distribution data allows companies to identify and
prioritize opportunities by providing an accurate view of the
addressable population, in terms of both socioeconomic level
and subnational distribution. The approach provides insights
into changing consumer dynamics within a market, allowing
for targeted portfolio strategies. Income distribution data
provides the foundation for demand models that then take
into account additional macro and micro factors, informing
the development of a coherent global strategy.
The world‘s only income distribution database, C-GIDD
provides the foundation for a comprehensive understanding
of changing consumer dynamics across a broad range of
geographies.
With the power of income distribution data—both as
income brackets and as subnational data—companies
can accurately devise strategies to capture the enormous
opportunity in emerging markets, in terms of both existing
potential and that which has yet to come.

REFERENCES

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About EIU Canback

The Economist Intelligence Unit’s consumer practice – EIU Canback – is a management consulting firm
working with senior management of the world’s largest consumer-facing companies. We offer a type of
management consulting deeply rooted in science and predictive analytics. Our ability to apply scientific
theory from economics, statistics and other fields, is married with deep in-country experiences. This
creates a competitive advantage for our clients.
At EIU Canback the aim of being “managerially relevant–analytically robust” is central to what we
do. We are experts in translating the results of complex models into strategic recommendations that
challenge the norms. We help to create a compelling, data-driven narrative for change, and typically
convey the results at the most senior level within our clients’ organisations.
We travel the globe to understand markets and deliver our work. Our consultants have worked on the
ground in more than 80 countries. In our analyses, we never look at markets or categories in isolation.
Instead we always place our findings in a regional or global context. We also draw on the heritage of
the EIU to understand the economic and political forces that shape our world.
Our work is underpinned by our own proprietary data. The Canback Global Income Distribution
Database covers GDP, population, household income, socioeconomic class sizes, and income
brackets for every country (213), large subdivisions (697) and all cities with more than half a million
inhabitants (997) in the world.
We work with our clients on multi-year efforts to enhance strategies and build organisational
capabilities. From a growth perspective, this includes geographic expansion, identifying new markets,
optimising portfolios, and improving revenue and profits through pricing decisions. We also provide
operational due diligence support when growth opportunities are inorganic.
EIU Canback was acquired by the Economist Intelligence Unit in 2015 and is based in Boston. As part of theEconomist Group, we share the principles of independence and intellectual rigour.

About the author

Nicole Boyd is a consultant in our Boston office. She has worked on numerous engagements across
Africa, Asia, and North America, with a focus on growth and portfolio strategy.
تینا شاهپوردوست
تینا شاهپوردوست
سرمایه گذار و تحلیل گر بازار سرمایه ، مدیر مالی در صنعت دارو و دانشجو سطح استراتژیک دوره بین المللی حسابداران مدیریت CIMA

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