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Consumer smartphone analytics 2016: applications and services

Consumer smartphone analytics 2016: applications and services

Market Study
Published: April 2017
Pages: 33 slides
Research from: Analysys Mason
Sector: Mobile

From: GBP 3845.00
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"Operators should design mobile plans that encourage user engagement and data generation."

A deeper understanding of how users engage with their smartphones can help telecoms operators, vendors and software developers make more-informed strategic and tactical decisions. In this report, we analyse data collected in real time from more than 8000 smartphones, and provide insights into how people use and interact with key applications and services in Germany, India, the UK and the USA.

 

This report provides answers to the following questions:

  • How much time is spent on smartphones on average and how is this distributed across various apps and services?
  • How much data is generated by each app or service?
  • Which networks are used for which apps and services, and to what degree?
  • When is usage more active and why?
  • What is the overall penetration of various app types and services?
  • How many distinct app types and services does an average smartphone user use?
  • How does this vary across the panel?

 

Survey data coverage

The analysis is based on data provided by Verto Analytics, and was collected using a passive on-device monitoring app called Smart Panel. The app runs in the background of the device. It registers actions undertaken by the user and relevant activity on the device, namely:

  • app download and usage
  • system processes
  • data traffic by each app/process
  • voice traffic
  • web browser activity.

 

Geographical coverage

  • Germany
  • India
  • United Kingdom (UK)
  • United States of America (USA).

 

TV/video apps and services generate the most traffic per unit of engagement time

 

TV/video and social networking are the two main app types and services that generate engagement and data traffic.

The combination of apps for TV/video services and social networking account for more than half of the traffic. These two app types also generate more traffic per time unit spent on them, as shown by their presence on the left side of the dotted line. Both are also likely to appear in browsing, as some video and social networking usage is likely to occur there.

Gaming, one of the more engaging app types in terms of time spent, generates disproportionately low data traffic. Within this sub-category, many individual games have low penetration levels but a lot of time is spent on each. This contrasts with social networking in which often one app, namely Facebook, accounts for most of both the active smartphone time spent and data traffic.

IM and chat apps also generate disproportionately low data traffic compared to the engagement time, although they include apps, such as WhatsApp and Facebook Messenger, both with relatively high penetration rates on smartphones.

The ‘other utility’ sub-category is influenced heavily by some users, especially in India, who use smartphones for file sharing and torrent downloads, as well as connecting to VPN networks.

 

 

Time spent on smartphones spikes in the evening in all four countries

 

The time spent on smartphones is distributed across the hours of a single day in relatively predictable ways, but some differences are noticeable between countries.

The most common observation is that, in each country, the engagement time spikes at night before falling to its lowest level after midnight and during the early morning hours. In the four countries, between 26% to 30% of total smartphone time occurs after 19:00. The peak hours are 21:00 and 22:00.

§The high proportion of smartphone usage in the evening is most visible in India and the UK.

§US users are more uniform in how terms of how much time they spend on their smartphones during the day. That being said, the evening spike in the USA amounts to a total of 51 minutes after 19:00.

§Between 09:00 and 17:00, the share of time spent on smartphones does not change radically across the hours. However, there are some small local peaks, such as those around lunchtime and end-of-work.

 

Between 09:00 and 17:00, smartphone data traffic is more biased toward the cellular network than at other times; Wi-Fi becomes prevalent during the evenings (see next slide).

 

 

 

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