APAC tourists remain active on their mobile devices whilst traveling. Among common activities on mobile are messaging and research among tourists. These findings are part of the latest APAC Mobile Advertising Statistics and Trends report by Vpon Big Data Group, an APAC-based big data and tech company.
The report also found that domestic tourists are more likely to plan their journeys using mobile devices, particularly between Tuesday and Thursday. Weekend usage of mobile devices to conduct travel research fell below average.
The report has been compiled to help advertisers target tourists for promotion more adequately by exploring the “mobile behavior of Greater China tourists in Thailand and South Korea, and [showing] an overview of the latest Asia Pacific mobile programmatic advertising market.”
Vpon also took a closer look at the mobile programmatic ad trends in the region. It noted that India and China were generating the most biddable inventory making up over a third of APAC inventory.
Mobile adverts tend to be more dominantly deployed across mobile apps compared to the mobile web. Macau, Hong Kong and Greater China were the only regions with a slightly larger percentage of mobile web ads.
Banner ad formats were still the most common type of advertising formats (53%) followed by interstitial formats (36%).
Victor Wu, CEO at Vpon Big Data Group, explains that data has driven transactions for big data to be used in order to boost competitiveness.
“With that being said, big data allows brands to gain a comprehensive understanding on customer behavior through mobile and to predict the trend. To gain such customer insights into tourists from Japan and even entire APAC region, Vpon has long strived to build the biggest Asian tourist’s mobile behavior database in the market through the continuous accumulation, consolidation, and data analysis over the years. Moreover, given the fact that the nature of data is interconnected, Vpon helps brands intersect and analyze the tourists’ data through multiple dimensions in order to discover the underlying information and mobile behavior on a deeper level.”