Retail

Cross-Border Shopping Patterns Vary in Asia-Pacific

People from the same country, state or even the same city shop for different items at different times. While there can be some similar patterns within various areas of the globe, sharing borders doesn’t always guarantee similar shopping patterns. This was found out in a recent study from the other side of the world.

In the Asia-Pacific region specifically, there are varying degrees of shopping patterns. eMarketer’s latest research report titled Cross-Border Ecommerce 2017: A Country-by-Country Look at Consumer Behavior and Trends looked at cross-border shopping patterns across 16 markets globally. While China and Japan shoppers are doing an abundance of online shopping, the penetration rate hasn’t been quite as high for India.

With India’s government launching new tools to help aid online shopping via cashless shopping, that low penetration rate may soon see a change in the next few years.

The research highlights the varied shopping patterns in the Asia-Pacific. It shows that while Chinese digital buyers are more apt to make global cross-border purchases for hard-to-find items, 54 percent of India is purchasing clothing-related items and 82.5 percent of Japan’s online shopping is geared towards local retailers.

To help expedite and increase digital buying in China, Alibaba and Amazon helped move the ball in 2016 for eCommerce. While the Indian eCommerce giant Alibaba worked with various countries to sell on its many platforms, Amazon debuted a Chinese-language site with small shipping rates.

With cross-border payments increasing in the Asia-Pacific, it’s safe to say that e-commerce is responsible for removing barriers, for both retailers and consumers, for a truly connected global economy.

Although the Asia-Pacific region brings to light a varied eCommerce shopping pattern, there’s still a way that the region can be utilized for the retail industry. Given that 60 percent of the China population say they are early tech adopters, we may be seeing it used as an example for predicting eCommerce trends moving forward.

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