Types of Results From Mobile Analytics

Types of Results From Mobile Analytics

The study of consumer behaviour helps business firms or other organisations to improve their marketing strategies. Nowadays, every organisation is making that extra effort to understand and know the behaviour of its consumers.

Mobile analytics provides an effective way of measuring large amounts of mobile data for organisations. It also shows how useful marketing tools such as ads are in converting potential buyers to actual purchasers. It also offers deep insight into what makes people buy a product or service and what makes them quit a service.

The technologies behind mobile analytics like Global Positioning System (GPS) are more sophisticated than those used in Web analytics; hence, compared to Web analytics, users can be tracked and targeted more accurately with mobile analytics.

Mobile analytics can easily and effectively collect data from various data sources and manipulate it into useful information.

Mobile analytics keep track the following information:

• Total time spent: This information shows the total time spent by the user with an application.
  
  
• Visitors’ location: This information shows the location of the user using any particular application.
  
  
• Number of total visitors: This is the total number of users using any particular application, useful in knowing the application’s popularity.
  
  
• Click paths of the visitors: Mobile analytics tracks of the activities of a user visiting the pages of any application.
  
  
• Pages viewed by the visitor: Mobile analytics tracks the pages of any application visited by the user, which again reflects the popular sections of the application.
  
  
• Downloading choice of users: Mobile analytics keeps track of files downloaded by the user. This helps app owners to understand the type of data users like to download.
  
  
• Type of mobile device and network used: Mobile analytics tracks the type of mobile device and network used by the user. This information helps mobile service provider and mobile phone sellers understand the popularity of mobile devices and networks, and make further improvements as required.
  
  
• Screen resolution of the mobile phone used: Any information or content that appears on mobile devices is according to the screen size of these devices. This important aspect of ensuring that the content fits a particular device screen is done through mobile analytics.
  
  
• Performance of advertising campaigns: Mobile analytics is used to keep track of the performance of advertising campaigns and other activities by analysing the number of visitors and time spent by them as well as other methods.

Types of Results From Mobile Analytics

Three major types of results from mobile analytics are explained below:

Advertising/marketing analytics

Despite developing an outstanding app, its chances of getting identified among a million other apps is too low these days unless marketing campaigns attract the appropriate type of users to make them install, stay engaged contributing to the app’s financial components.

The most generic route to market an app is partnering with various ad networks but even so, a trustworthy channel to determine which ad networks and publishers are delivering results is difficult to find without marketing analytics.

Commonly the following marketing analytics data are collected:

• Installs
• Opens
• Clicks
• Purchases
• Registrations
• Content viewed
• Level achieved
• Shares
• Invites
• Custom events

In-App analytics

Whether an app delivers content, or sells products, or gaming experience, the app must be able to satisfy the user expectations to be successful. Providing users to achieve the objectives for which the apps are designed in the simplest manner, is every app’s goal. Without a user or in-app behaviour data it is difficult to make a wild guess in the area for improvements and this is where In-App Analytics play its role.

Being an “in-session” analytics, this analyses what users are doing inside an app and the way they are interacting with it. The major focal areas are, conversion funnel, pathway and feature optimisation which are majorly used by the product managers. Commonly the following in-app analytics data are collected:

Device Profile (Mobile phone, tablet, etc., Manufacturer, Operating system)

User Demographics (Location, Gender, New or returning user, Approximate age, Language)

n-App Behaviour (Event Tracking (i.e., buttons clicked, ads clicked, purchases made, levels completed, articles read, screens viewed, etc.)

Performance analytics

This involves the actual performance of the app. The two major measures for performance analytics are: App uptime and App responsiveness. The factors which can impact the performance of an app irrespective of how well it was coded include:

App complexity

Most apps depend on various third-party services hence the speed of such services directly impacts its performance

Hardware variation

Apps available on both iOS and Android platforms should be compatible with device specification variations on both the platforms, which is majorly the varying hardware environment across the phone models. This incompatibility impacts the app performance heavily

Available operating systems

An app developed just for Android and iOS, would definitely have impact on its performance when installed on other available operating systems like, Blackberry, Windows, Bada, Symbian, Firefox, Ubuntu, Palm, TIZEN, Sailfish, etc.

Carrier/network

Most of the major networks are expanding their technology and coverage providing better access to users with faster data speeds but, many still have vital issues in their latest technological coverage, due to which the users fall back on older standards, directly impacting the app performance.

Users expect to have an app working efficiently and are getting impatient towards underperformance as overall technology gets faster and better therefore identifying the root cause of issues and prioritising solutions would get difficult in absence of performance analytics. Commonly the following performance analytics data are collected:

• API latency
• Carrier/network latency
• Data transactions
• Crashes
• Exceptions
• Errors