A Deeper Look at Mobile User Behavior

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As we begin to look into the mass of mobile data, there are clear examples of how mobile user behavior is influenced by culture and user surroundings. Such behaviors are changed and amplified as we further define unique user groups for study and benchmark each group for comparison.

We looked deeper into these user clusters to get a better idea of the differences and similarities in user behavior in order to showcase the power and potential of mobile data to transform the mobile experience.

We all know that every user is different, but how do we scale each user’s uniqueness across a broad audience?

Let’s begin with the simplest example: geographical differences among users.

To focus our insights, we looked at American, French, and Japanese users.

The radar chart shows affinities and interests that are common to American, French and Japanese mobile users with no other segmentation except country of origin.

user behavior radial graph

  • This represents an index from the average global user, highlighting the attributes that are unique for each of these nationalities.
  • The examined affinities are custom-made categories that classify activities for entertainment, business, and social/communication, among others.

Local App Preferences:

Communication

Communication is one of the most important categories among apps; their presence can be found on the devices of most mobile users. But, there are distinct patterns among users from different cultures ranging from the type of communication formats they prefer to which apps dominate the local market.

  • Compared to the global average user, American users have a strong preference for Kik, a service that has a weak presence among the French and is almost non-existent for the Japanese.
  • Despite being the leading global communications app, WhatsApp usage is below average among the 3 nationalities studied, indicating that America, France, and Japan are not WhatsApp’s main sources of users.
  • Line commands a large audience in the Asian market. This is corroborated in the graph above which shows Japanese affinity for Line as being eight times higher than global average users; the service is barely present in America and France.
  • In France, Japan, and America, Skype usage most closely aligns with the global average, though there is a slightly higher affinity found among French users.

Country and culture are essential to determining a user’s preferences and understanding their behavior. It should be applied as a basic segmentation layer when analyzing category profiles.

Geographical segmentation also implies many more variants beyond just culture and country.

Geographical segmentation also implicitly includes differences in economy, mobile penetration, internet availability, leisure time, demographics, and more. Because of this, geography segmentation can reveal a generalized “user lifestyle

American Users:

  • Strong interest in following sports and listening to music on their mobile devices;
  • Highest enthusiasm for a universal mobile experience; they use their devices for many lifestyle activities such as mobile banking, food delivery, medical research, and more
  • Highest intent for online dating

French Users:

  • Most health-conscious; they utilize mobile devices more often than Japanese or American users for fitness and dieting purposes
  • High interest in cooking
  • Strong interest in sports, but still lag behind the interest shown by Americans

Japanese Users:

  • Most business-oriented geographical profile; they use mobile for productivity, remote office access, document editing, and more.
  • Highest interest in news
  • Heavy reliance on transportation and navigation tools, with a high affinity for traveling.
  • Most interested in mobile photography and photo editing

The true competitive advantage in the mobile app industry will be wielded
by those who fully understand user behavior: their interests, affinities, and
intentions. When this information is combined along with perfectly-timed
context, companies will be able to anticipate and fulfill any future user needs
increasing user satisfaction, retention, and loyalty.

This study of user behavior is published in full in our whitepaper: Decoding Mobile User Behavior. Read the paper here

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