Nikkei rides the Wave app into the future with increased engagement

By Paula Felps


Nashville, Tennessee, USA


Nikkei originally launched its mobile app in 2010. But with the app now a decade old, stakeholders realised there were functionalities and features they should be offering but weren’t able to provide through the original app.

With 750,000 paid subscribers and 4 million registered users accessing the app, Nikkei recognised the importance of offering new features and set out to create a better, more modern mobile app experience.

“We wanted to create a smartphone app that would allow users to have the same reading experience that was possible with a paper newspaper,” said Yosuke Suzuki, general manager at Nikkei America, Inc. “With Tinder and TikTok, it takes about .2 seconds to make a concise decision on whether or not you are interested in a person or content. If you are not interested, you are shown the next item; if you are interested, you can continue browsing. We wanted to implement the same user experience as a paper newspaper.”

Nikkei Wave is designed to provide the same user experience as a print publication.
Nikkei Wave is designed to provide the same user experience as a print publication.

With ideas for a fresh new product in mind, the company began a nine-month development journey that resulted in Nikkei Wave, an experimental app designed to provide users with an improved recommendation algorithm, auto-generated video view, and a simpler user interface.

A development team of five engineers and three student interns worked to create a completely new recommendation engine on a different platform than the existing app.

“The auto-generated video function is a completely new initiative and is being developed by engineers and student interns who are strong in natural language processing,” Suzuki said. “The auto-generated video view function is designed to work independently of the Wave app and will be rolled out to other platforms in the future.”

Building an app for the future

Nikkei Wave uses the SwifitUI as its development framework, which only works in iOS13 or later, and the application can be used as a testbed for developing new technologies, Suzuki said. Developers have been able to improve the productivity of the app thanks to robust user testing within the user group.

The Nikkei Wave pushes auto-recommended articles to readers at 8 a.m. and 4 p.m. daily, which has achieved one of the primary goals: to increase the number of weekend visits and improve user engagement.

Nikkei Wave pushes auto-recommended articles to users twice daily and has increased the amount of weekend engagement by 15%.
Nikkei Wave pushes auto-recommended articles to users twice daily and has increased the amount of weekend engagement by 15%.

“The low number of visitors to the Nikkei Online Edition on weekends has been an issue for us since launch, but with Wave, the number of visitors on weekends is about 15% higher than on weekdays. This is the result of pushing recommended articles every day, including weekends.”

Without the app, he said they never would have known how valuable and effective the push notifications are.

“Until now, there have been no effective measures to achieve this goal [of increased weekend traffic],” Suzuki said. “Nikkei Wave allowed us to confirm that using push notifications of recommended articles is highly effective in driving user engagement; they are being used more than we thought.”

The auto-generated videos are only displayed on the Wave app, which also contributes to an increased amount of average time spent on the app.

Early adoption, ongoing adjustments

So far, more than 21,000 users have downloaded Nikkei Wave, even as tweaks and discoveries continue. The app’s support page includes verbiage that some features may become unavailable as work continues on the app. For example, the original positioning of the menu at the bottom of the app has changed.

There is much more in store for users, Suzuki said: “We are looking into recommendations containing numerical data and statistics. For example, we will recommend numerical information such as trade statistics. We also want to develop a feature to recommend topics that users are interested in, although right now we only recommend articles.”

About Paula Felps

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