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논문 기본 정보

자료유형
학술대회자료
저자정보
MS Jai-sung Hwang (Seoul National University) MS Minhee Lee (Seoul National University) Yihun Jeong (Seoul National University) Yoo S. Hong (Seoul National University)
저널정보
대한산업공학회 대한산업공학회 추계학술대회 논문집 2019년 대한산업공학회 추계학술대회
발행연도
2019.11
수록면
653 - 658 (6page)

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BOP (base of the pyramid) market is defined as a market segment that consists of consumers who earn lower than $10 per day and it is known that over 60% of world population is account for this segment. Henceforth, in spite of low purchasing power and high risk, BOP market still retains high potential. The purpose of this study is to classify the characteristics of BOP market in each country and to suggest marketing strategies optimized for each market. To this end, based on real business cases (22 successes, 13 failures) in 35 BOP markets worldwide, 4 key factors (GDP growth rate, electricity usage per person, income rate of 20-40 percentiles of pyramid, and cultural distance) representing the characteristics of the market were extracted. And then, marketing strategies used by companies in BOP markets were classified into 4 categories (less quantities, business to business (B2B), civilizing education, and social needs). With these 4 factors and 4 strategies, patterns were derived between the market characteristics and marketing strategies for successful business using support vector machine. As a result, we found that, in the BOP market of countries with relatively high GDP growth rate and high electricity usage per person, it is effective to conduct civilizing education strategy for potential customers. But in the BOP market of countries with high GDP growth rate and low electricity usage per person, business strategy in the form of B2B (business to business) with a domestic company in a marketing target country is more favorable. The result of this study would provide new insights to understand the characteristics of BOP market for companies to enter BOP market successfully.

목차

Abstract
1. Introduction
2. BOP market and business activities classification based on 35 real cases
3. Method: Regression analysis based on 35 real cases
4. Result and discussion
5. Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2019-530-001293445