Mobile applications are web sites, and traditional web analytics are not appropriate for mobile applications. What you need is insight that will make your app more effective. You will not find this insight by tracking downloads and installs, phone platforms and versions, screen sizes, new users per day, frequency of use, or any of the traditional metrics. Many of these have been dragged over, kicking and screaming, from the world of web analytics. Yes, these numbers will give you surface measures of the effectiveness of your app. Yes, they are important to know. Yes, you can use them to make pretty charts. But they are all output measures. They measure the results of your app design, interaction model and service level. They do not tell you what to change to achieve your business goals.
To gain real insight into your app and its users, insight that you can use to make your app more effective, you need to measure inputs. There are the six key input metrics that we cover in this article. Funnel analysis tells you why users are failing to complete your desired user actions, such as in-app purchases, or ad clicks. Measuring social sharing tells you what aspects of your app are capturing the hearts and minds of your users. Correlating demographic data with user behaviour will tell you why your user base does what it does. Tracking time and location, together, gives you insights into the contexts in which your app is used. Mobile apps design naturally tends toward deeply hierarchical interfaces – how optimised is yours? Finally, the real business opportunity may be something you never even thought of, so capturing the emergent behaviours of your user base is critical. Let’s tale a closer look at each of these metrics, and then take a look at how you can get this data with today’s services.
Funnel analysis allows you to determine the parts of your application that are preventing your users from reaching your business goals. Let’s take a simple unit converter app as a example. The canonical unit converter app lets you convert between kilograms and pounds, or inches and centimeters, and so on. Let’s say one of your business goals is to get your users to sign up to a mailing list from within the app. If you look at the user journey this requires, you might have a call-to-action button on the main screen, followed by a form to capture the email, followed by an acknowledgment page telling people to check their email accounts to verify their subscription. Funnel analysis breaks this user journey down into discrete steps: the tap on the button, typing in the email address, submitting the email address, reading the acknowledgement page. You need to know the percentage of users you are losing at each stage. Probably more than 50%. Understanding this activity funnel to your desired business goal is critical to building an effective app. Perhaps the next version should drop the call-to-action button, or use better copy text. Use funnel analysis to measure this.
Social media are a key element in the promotion of your app. When you leverage these media, you need to track the viral spread of your app. This is more than simple counting the number of tweets or facebook likes. You need to understand the structure of the social network you are attempting to permeate. You need to find the highly interconnected individuals, those who recommendations are actively followed by their friends and acquaintances. In any social network there are always a small set of key individuals who know everybody. You need to identify these people and engage with them. This might be as simple as special promotions, or even making them employees! Your mobile analytics solution should be telling you who these people are.
Do you understand the demographic constitution of your users, and can you correlate these demographics with user behaviour? This is the classic diapers and beer effect. A major UK supermarket chain found, through mining their purchase data, that increased beer purchases were correlated with increased diaper purchases. Cross-referencing this with the demographic data they have collected via a loyalty card scheme, the supermarket chain was able to figure out that parents with new babies were staying at home having a homemade meal and a beer, rather then going out to restaurants. This allowed for far more effective targeted advertising. Demographic data are more difficult to capture in the mobile app space, but carriers such as Sprint are now beginning to offer this information.
Location is an important element of the mobile user experience, and many mobile analytics services will offer location analysis. However this is not enough. Again, simply counting the number of users in various geographies does not tell you very much. It validates a business goal, but does not give you insight. You actually need to track the temporal dimension as well. Time and space must be analyzed together. Take our unit converter app. Usage of the app on Sunday afternoons within DIY store differs from at-home usage at mealtime during the week. In the first case you might like to show ads for power tools, in the second ads for food products. Mobile analytics offerings have yet to reach this level of capability, so you may need to consider custom solutions for this type of analysis.
Mobile application interfaces are very hierarchical in nature. This means that there are lots of screens with small amounts of information that the user has to navigate through. There simple isn’t enough screen space to show too much information at once. As result, the careful design of the screen hierarchy is critical to effective use of the app. If a particular function, such as in-app purchases, is too deeply buried, you will not achieve your goals for the app. Therefore it is very important to measure of the navigation pathways within the app. Berkeley University in California determined the layout of their campus walkways by not laying any paths at first. After the students had trampled the lawns for a year, they then build the pathways where the students had walked. This is what you need to do. (Actually, the Berkeley story is an urban legend, but it’s still a great one)
The final metric is something that requires a certain open mindedness. It can be measured using some heavy mathematics, but it can also be noticed intuitively. When you put a product on the market, it may well be the case that your customers start using it in weird and wonderful ways, that you never imagined. Hashtags (#thesethings) on twitter are a good example. Twitter did not invent them, but noticed that their users had come up with this interesting convention for marked content themes. They embraced this emergent behavior and were handed a core product feature on a plate. Of all the metrics in this article, this one, emergent behaviour, is the most precious. It could turn you into the next facebook (relationship status? What a feature!), or you could kill the golden goose without even knowing it by ignoring your users (Iridium satellite phones anyone?). Detecting emergent behavior is both and art and a science – keep your eyes open.
First published in GoMoNews Nov 2010.