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Market Potential Estimation in International Markets: A Comparison of Methods

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Title: Market Potential Estimation in International Markets: A Comparison of Methods


1
Market Potential Estimation in International
Markets A Comparison of Methods
  • Waheeduzzaman (2008), Market Potential
    Estimation in International Markets, Journal of
    Global Marketing, Vol. 21 (4), 307-320.

2
Presentation Outline
  • Introduction
  • Objectives of the Study
  • Literature Review
  • Research Methodology
  • Findings of the study
  • Conclusion and Future direction

3
Objectives of the Study
  • Discuss various approaches to demand or market
    potential estimation
  • Test the models/methods for demand estimation
  • Compare/evaluate the methods
  • Provide direction for future research

4
Literature Review
  • Market Potential Estimation Approaches
  • Time series growth models
  • Stock adjustment models
  • Diffusion models
  • Consumer behavior studies
  • Market potential estimations in marketing

5
Evaluation of Estimation Methods
  • Estimation Methods
  • Method of Analogy
  • Proxy Indicators
  • Chain Ratio Method
  • Time Series Modeling
  • Multiple regression modeling
  • Criteria for evaluation
  • Precision
  • Prediction
  • Price
  • Pragmatism

6
A Research Methodology
  • Durables Washing Machine
  • Emerging country markets Argentina, Brazil,
    Chile, China, Colombia, Egypt, Hungary, India,
    Indonesia, Israel, Malaysia, Mexico, Peru, The
    Philippines, Poland, Singapore, South Africa,
    Thailand, Turkey and Venezuela. (20 countries)
  • Variables in the study Income (INCOME), Per
    Capita Energy Consumption (ENERGY), Life
    Expectancy (LIFEXP), Female Labor (FEMALE),
    Urbanization (URBAN)
  • Time period 1977-2006 (30 years).
  • Data analysis Correlation, Regression Analysis
  • Sources The World Bank, Euromonitor, Freedom
    House, Central Intelligence Agency, and
    Globaledge of Michigan State University

7
Findings of the Study
  • Table 1 Evaluation of Estimation Methods
  • Table 2 Time Series Model Results
  • Table 3 Variables in Multiple Regression
  • Table 4A Correlation Matrix
  • Table 4B Regression Analysis
  • Table 5A Basic Statistics for Thailand
  • Table 5B Comparison of Different Methods
  • Table 6 Literature Review (Approaches in the
    Study of Durables)

8
Table 1 Evaluation of Estimation Methods
Criteria/ Precision Prediction Price Pragmatism
Method of Analogy Not so precise as it depends on simple analogy Very robust estimation Inexpensive using secondary data Very convenient and can be estimated in a short time
Proxy Indicators Depends on choice of variables Very robust but can be accepted Reasonably inexpensive Convenient to measure if data are available
Chain Ratio Method Reasonably precise if right variables are use Robust, yet can be very close to real data Relatively inexpensive Convenient and not very complex
Time Series Modeling Depends on the quality of the series data Good as it uses scientific techniques Usually expensive since data need to be purchased from consultants Relatively easy to implement if researcher has the knowledge and data are available in the right format
Multiple Regression Modeling Depends on the model and data quality Good in situations where causality is preferred Usually expensive to buy data Model building requires specific knowledge and skill
9
Table 2 Time Series Model Results (Washing
Machine)
Country F-Ratio R-Square Intercept ß1 ß2
Argentina 4580.07 0.9972 40.48 0.51 -0.007
Brazil 1495.22 0.9914 6.17 1.09 -0.015
Chile 12828.3 0.9990 9.96 1.99 -0.023
China 3260.83 0.9971 -1.37 0.23 -0.003
Colombia 7230.70 0.9982 10.80 1.60 -0.021
Egypt 10847.50 0.9989 -0.32 0.16 0.001
Hungary 6468.58 0.9980 20.55 0.23 0.009
India 154.03 0.9333 -1.56 0.22 0.001
Indonesia 420.49 0.9745 -1.52 0.28 -0.001
Israel 2865.06 0.9955 62.75 2.22 -0.040
Malaysia 5696.94 0.9977 67.17 0.82 -0.013
Mexico 2098.1 0.9938 15.91 1.80 -0.033
Peru 12318.1 0.9989 10.55 0.43 0.006
Philippines 3450.36 0.9962 4.61 0.25 -0.004
Poland 541.62 0.9766 11.52 0.45 0.045
South Africa 16651.9 0.9993 -4.38 1.13 -0.001
Singapore 357.44 0.9649 67.47 0.48 0.021
Thailand 716.13 0.9822 3.99 0.06 -0.001
Turkey 981.43 0.9869 8.74 0.93 -0.008
10
Table 3 Variables in Multiple Regression
Variables Studies
INCOME Income is the most influential variable affecting our consumption. Higher income indicates higher aspiration and better quality of life. Income favorably affects the consumption of durables. Alessie et al. 1997, Besley and Levenson 1996, Freedman 1970, Grieves 1983, Mishkin 1976, and Ruiter and Smant 1999.
ENERGY Per capita energy consumption as an indicator technological growth in a society. Technology shapes the social, cultural, economic behavior of the people. It indicates modernization and is related to the consumption of durables. Armour 2002, Conrad and Schroder 1991, Irwin 1975, Waheeduzzaman 2006.
LIFEXP Life expectancy is commonly perceived as an indicator of quality of life. Higher life expectancy means more savings and contribution to family wealth. It favorably affects the consumption of durables. Ballew and Schnorbus 1994, Brandstatter and Guth 2000, Inglehart 2005, Inkeles and Smith 1974, Shaw et al. 2005, Sirgy et al. 2006
URBAN Urbanization indicates the industrial growth of a society. Consumption pattern changes with industrial growth and economic development. Fast urban lifestyle demands various durables. Bollen and Appold 1993, Bradshaw 1987, Rostow 1961, Schnaiberg 1970, and Timberlake and Kentor 1983.
FEMALE Participation of women in the workforce significantly changes the social and family relationship The use of timesaving appliances like microwave, dishwasher and washing machine become a necessity. Also, dual income raises the aspiration level of the family and contributes to the acquisition of durables. Bryant 1988, Chia et al. 2001, Freedman 1970, Inkeles and Smith 1974, and Sumer 1998.
11
Table 4A Correlation Matrix
INCOME ENERGY LIFEFX URBAN FEMALE
INCOME 1.00
ENERGY 0.39 1.00
LIFEXP 0.53 0.37 1.00
URBAN 0.63 0.51 0.64 1.00
FEMALE 0.10 0.01 0.21 -0.30 1.00
12
Table 4B Regression Results
F-ratio R-square Intercept INCOME ENERGY LIFEFX URBAN FEMALE
Refrigerator 73.7 0.51 -12.08 0.001 0.001 -0.50 0.76 1.45
Dishwasher 131.0 0.65 9.31 0.001 0.0001 -0.41 0.12 0.28
Washing machine 128.8 0.64 -124.27 0.002 0.0001 1.78 0.32 0.04
Microwave oven 45.16 0.38 -72.20 0.001 0.001 1.29 -0.27 -0.05
Television 126.9 0.64 -71.67 0.001 0.001 0.83 0.51 1.09
VCR 92.05 0.56 -114.70 0.002 0.001 1.25 0.03 1.05
13
Table 5A Basic Statistics for Thailand
Variable 2006 2007 2008 2009 2010
GDP measured at PPP (Million US ) 597380.0 640120.0 685621.0 733182.9 775523.9
Per capita income 9553.8 10138.6 10758.8 11402.5 11957.0
Population (000) 62527.9 63136.7 63726.7 64300.3 64859.5
Family size 3.6 3.6 3.5 3.5 3.5
Total households (000) 17514.8 17785.0 18052.9 18319.2 18584.4
Ownership of WSM per 100 households 43.1 46.2 48.4 50.8 52.7
Life expectancy at birth 71.3 71.7 72.0 72.3 72.5
Female labor at as percentage of total () 45.9 45.9 45.9 45.9 46.0
Percentage of urban population () 32.9 33.3 33.6 34.0 34.4
Per capita energy consumption (KWH) 1526.0 1500.9 1469.9 1435.6 1400.8
Households with electricity (), 3 growth 82.1 84.56 87.1 89.71 92.4
Households with resident telephones () 42 42.47 42.29 42.14 41.87
Households with water supply () 49.76 50.47 50.74 52.35 52.16
14
Table 5B Comparison of Methods
Year/Method 2006 2007 2008 2009 2010
Method of Analogy 5523.87 5741.98 5994.23 6179.19 6332.85
Proxy Indicators 7356.22 7552.69 7634.94 7720.48 7780.49
Chain Ratio Method 7155.32 7590.18 7978.39 8602.68 8956.22
Time Series Analysis 4324.41 4469.37 4614.32 4757.49 4949.02
Multiple Regression Modeling 5996.17 6439.20 6884.52 7339.03 7756.98
Average of all five methods 6071.20 6358.68 6621.28 6919.77 7155.11
Yearly growth of avg. market potential 287.48 262.60 298.49 235.34
Market potential as per Euromonitor data 7548.89 8216.66 8737.60 9306.14 9793.97
15
Conclusion and Future Direction
  • Summary of results
  • Managerial Implications
  • Future Direction
  • Questions?

16
Table 6 Approaches in the Study of Durables
17
Table 6 Approaches in the Study of Durables
18
Table 6 Approaches in the Study of Durables
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