What is Mobile Analytics?
Mobile analytics is the process of collecting and analyzing data from mobile devices, such as smartphones and tablets, in order to gain insights into user behavior, app performance, and business metrics. Mobile analytics tools are used to track and measure various aspects of mobile app usage, including app downloads, user engagement, retention, in-app purchases, and other key performance indicators.
Table of Content
- 1 What is Mobile Analytics?
- 2 Goals of Mobile Analytics
- 3 Mobile Analytics and Web Analytics
- 4 Types of Results from Mobile Analytics
- 4.1 Total time spent
- 4.2 Visitors’ location
- 4.3 Number of total visitors
- 4.4 Click paths of the visitors
- 4.5 Pages viewed by the visitor
- 4.6 Downloading choice of users
- 4.7 Type of mobile device and network used
- 4.8 Screen resolution of the mobile phone used
- 4.9 Performance of advertising campaigns
- 4.10 Advertising/marketing analytics
- 4.11 In-App analytics
- 4.12 Performance analytics
- 5 Types of Applications for Mobile Analytics
- 6 Web Mobile Analytics
- 7 Mobile Application Analytics
- 8 Challenges of Mobile Analytics
We are now using fourth generation (4G) wireless mobile technologies. When you look at the past, you will see that wireless mobile technologies have shown a steady growth, evolving from 1G to 4G. With every major shift in the technology, there has been a corresponding improvement in both the speed and efficiency of mobile devices.
First generation (1G) mobile devices provided only a “mobile voice”, but in second generation (2G) devices, larger coverage and improved digital quality were provided. Third generation (3G) technology focused on multimedia applications like videoconferencing through mobile phones. 3G opened the gates for the mobile broadband, which was seen in fourth generation (4G) devices. 4G provides wide range access, multi-service capacity, integration of all older mobile technologies, and low bit cost to the user.
Figure 10.1 shows the evolution of different generations of mobile technologies:
“Forget what we have taken for granted on how consumers use the Internet,” said Karsten Weide, research vice president, Media and Entertainment. “Soon, more users will access the Web using mobile devices than using PCs, and it’s going to make the Internet a very different place.”
According to International Telecommunication Union (ITU), an agency of the United Nations (UN) responsible for information and communication technologies-related issues, the number of mobile users in 2008 was 548 million. This number increased to 6835 million in 2012.
According to Informa, a research firm in the US, there were over 3.3 billion active cell phone subscriptions in 2007 all over the world. This effectively means that around half of the total population of the earth is using mobile devices.
Given the above statistics, it imperative for organisations to find ways of analysing data related to mobile devices, and use the data to market and sell their products and services through these devices. Mobile analytics is one tool that organisations have to do this.
Goals of Mobile Analytics
Marketers want to know what their customers want to see and do on their mobile device, so that they can target the customer.
Similar to the process of analytics used to study the behaviour of users on the Web or social media, mobile analytics is the process of analysing the behaviour of mobile users.
The primary goal of mobile analytics is to understand the following:
These are users who have just started using a mobile service. Users are identified by unique device IDs. The growth and popularity of a service greatly depend on the number of new users it is able to attract.
These are users who use mobile services at least once in a specified period. If the period is one day, for example, then the active user will use the service several times during the day. The number of active users in any specific period of time shows the popularity of a service during that period.
Percentage of new users
This is the percentage of new users over the total active users of a mobile service. This figure is always less than 100%, but a very low value means that the particular service or app is not doing very well.
When a user opens an app, it is counted as one session. In other words, the session starts with the launching of the app and finishes with the app’s termination. Note that a session is not related to how long the app has been used by the user.
Average usage duration
This is the average duration that a mobile user uses the service.
This refers to the total number of users (old as well as new) who have used an app before a specific time.
The bounce rate is calculated in percentage (%). It can be calculated as follows: Bounce rate = Number of terminated sessions on any specific page of an app/Total number of sessions of the app* 100. The bounce rate can be used by service providers to help them monitor and improve their service so that customers remain satisfied and do not leave the service.
After a certain period of time, the total number of new users still using any app is known as the user retention of that app.
Mobile marketers receive useful data regarding who uses their apps and how do they do it through the way the reports are designed. Marketers are left to make educated guesses without any hard data therefore;
The following are listed as the goals of mobile analytics:
- Helps building an efficient mobile marketing strategy: Without verifying the content or functionality which the customers respond to, the marketers have no ground at which they could strategise and this is where analytics come into play. Analytics assist in defining a measurable goal.
- Discovering the popular feature of the app and the ones people are not using: Analytic tracking demonstrates the various screens and menus the users visit while navigating throughout your app. Tracking the screens, they spend most of their time and often return to, helps obtain a fair amount of understanding of the content users look for and finding a better way to make them reach there is definitely worthwhile.
- Determine which segment of your app is converting most: Mobile analytics help the marketers to identify the segments in their app which result in more conversions than rest of the sections.
- Observe in case people use the app at all: Basis research, only 25% of the business apps are continually used by people. Because it’s crucial to have repeat use of the apps towards building a relationship with your users, the analysis helps tracking whether the repeated users invest time on your app and also provides significant insights on whether the app adds value for users to give them enough reasons to return.
- Detection of the mobile device: It is crucial for increasing the overall efficiency of your strategy for mobile marketing and analytics, to help find mobile devices on which your app is getting downloaded most and assists in determining the devices required to be prioritised.
- Realizing the complete mobile app user experience: By providing the ability for data-driven decisions at every stage of the app life cycle, the analytics help marketers and developers create such an app experience that proves to be more useful and appealing to their users and the overall strategy for marketing.
Mobile Analytics and Web Analytics
Mobile analytics has several similarities with Web and social analytics, such as both can analyse the behaviour of the user with regard to an application and send this information to the service provider. However, there are also several important differences between Web analytics and mobile analytics, which we will discuss in this section.
Some of the main differences between Web analytics and mobile analytics are as follows:
Mobile analytics works on the basis of location of the mobile devices. For example, suppose a company is offing cab service in a city like New York. In this case, the company can use mobile analytics to identify the target people travelling in New York. Mobile analytics works for location-based segments while a Web analytics works globally.
Complexity of code
Mobile analytics requires more complex code and programming languages to implement than Web analytics, which is easier to code.
Network service providers
Mobile analytics is totally dependent on Network Service Providers (NSPs) while Web analytics is independent of this factor.
Sometimes, it is difficult to measure information from the mobile analytics apps because they can run offline. Web analytics always runs online so we can easily measure vital information with it.
To do the ultimate analysis on data, we require some other tools of Web analytics with mobile analytics tools. Web analytics, on the other hand, does not require any other tool for analysis.
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.
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.
Three major types of results from mobile analytics are explained below:
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:
- Content viewed
- Level achieved
- Custom events
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)
- In-App Behaviour (Event Tracking (i.e., buttons clicked, ads clicked, purchases made, levels completed, articles read, screens viewed, etc.)
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:
Most apps depend on various third-party services hence the speed of such services directly impacts its performance
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.
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
Types of Applications for Mobile Analytics
This data is used by companies in order to figure out the need of the users for delivering a further satisfying user experience. Through this data the analytics shows following information:
- The reason the visitors are drawn to the mobile site or app
- The duration visitors generally stay
- The features visitors are interacting with
- The problem areas for the visitors within the site or app
- Factors instigating purchases
- Factors responsible for higher usage and user retention
There are two types of applications made for mobile analytics. They are:
- Web mobile analytics
- Mobile application analytics
Web Mobile Analytics
Mobile Web refers to the use of mobile phones or other devices like tablets to view online content via a light-weight browser for the mobile. The name of any mobile-specific site can be the form of m.example.com. Mobile Web sometimes depends on the size of the screen of the devices.
For example, if you design an application for a small screen, then its images would appear blurred on a big screen; similarly, if you make your site for the big screen, then it can be heavy for a small screen device.
Some organisations are starting to build sites specifically for tablets because they have found that neither their mobile-specific site nor their main website ideally serves the tablet segment.
To solve this problem, mobile Web should have a responsive design. In other words, it should have the property to adapt the content to the screen size of the user’s device.
Figure 10.2 shows the difference between a website, a mobile site and a responsive-designed site:
In Figure 2, you can see that a website can be opened on both computers and mobile phones, while a mobile site can be open only on the mobile phones; responsive-designed sites, on the other hand, can open on any device like a computer, tablet and mobile phone.
Mobile Application Analytics
The term mobile app is short for the term mobile application software. It is an application program designed to run on smartphones and other mobile devices.
Mobile apps are usually available through application distribution platforms like Apple App Store and Google Play. Application distribution platforms are typically operated by the owner of the mobile operating system.
Examples of mobile operating systems include the Apple App Store, Google Play, Windows Phone Store, and BlackBerry App World. Some mobile apps are freely available, while others must be bought.
Depending on the objective of analytics, an organisation should decide whether it needs a mobile application or a mobile website. If the organisation wants to create an interactive engagement with users on mobile devices, then mobile apps is a good option; however, for business purposes, mobile websites are more suitable than mobile apps.
Table 10.1 lists the main differences between mobile app analytics and mobile Web analytics:
Table 10.1: Differences Between Mobile App Analytics and Mobile Web Analytics
|Factors||Mobile App Analytics||Mobile Web Analytics|
|Screen and Page||Mobile app analytics does not have pages. The user can interact with various screens.||Mobile Web analytics has pages like normal websites, and users do interact with various pages.|
|Use of builtin features of mobile devices||Mobile app analytics can access built-in features such as gyroscope, GPS, accelerometer, and storage.||Mobile Web analytics does not use built-in features like gyroscope, GPS, accelerometer, etc.|
|Session time||Mobile app analytics has shorter session timeouts (around 30 seconds).||Mobile Web analytics has a longer session timeouts. In general, a session will end after 30 minutes of inactivity for websites.|
|Online/ Offline||Depending on how it was developed, mobile app analytics may not require to be connected to a mobile network.||Mobile Web analytics requires an Internet connection and can run online only.|
|Updates||App owners provide frequent updates and new versions of the apps.||Updates are not that frequent.|
Challenges of Mobile Analytics
Mobile analytics has its own challenges. Some of the main challenges can be listed as follows:
Random change in subscriber identity
TMSI (Temporary Mobile Subscriber Identity) is the identity of mobile devices and can be known by the mobile network being used. This identity is randomly assigned by the VLR (Visitor Location Register) to every mobile in the area as it is switched on. This random change in the subscriber id makes it difficult to gather important information such as the location of user, etc.
Some mobile devices do not support redirects. The term ‘redirect’ is used to describe the process in which the system automatically opens another page.
Special characters in the URL
In some mobile devices, some special characters in the URL are not supported.
The mobile connection with the tower is not always dedicated. It can be interrupted when the user is moving from one tower to another tower. This interruption in the connection breaks the requests sent by the devices.
Together with generalised issues mentioned above, mobile analysts are also facing the following critical issues, which discourage mobile analytics marketing:
Limited understanding of the network operators
Network operators are unable to understand the business processes happening outside the carrier’s firewall.
True real-time analysis
True real-time data analysis is not always possible with mobile analytics due to various reasons such as signal interruption, variation in technology used in mobiles, random change in subscriber id, etc.
Mobile technology has various important features but some of these features, such as GPS, cookies, Wi-Fi and beacons can disclose important information of the user. Information like details of credit cards, bank accounts, medical history, or other personal content can be easily misused. Some techniques like Deep Packet Inspection (DPI), Deep Packet Capture (DPC), and application logs can increase security threats.
To cope with such security threats, business organisations must intelligently monitor all communications in real time and make sure that personal data is not accessible to everyone.