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App Personalization is the process of building a mobile application to meet the needs of specific audiences. Similar to other forms of personalization, app personalization aims to present users with experiences customized to their specific needs, rather than a broad, one size fit alls experience for all users.
The app space is already very crowded and user expectations are growing. As of July 2015, there are over 1.5 million apps in the App Store and 1.6 million apps in the Google Play Store. Once an app is chosen and installed, 65% of users will delete it if the first experience using it is a negative one. Even after an app has passed that threshold, the average person will have about 65 apps on their device but only actually use about 15 of them each week.
There is no reason to expect that the above stats will change, so it is becoming increasingly important to make sure that your app provides its users with a great experience. Doing so will increase the likelihood that your app will stand out from the crowd, and you users will use it regularly. In addition to many tactics such as app discoverability, performance testing, and much more, app personalization is one of the best ways to create an app that users will actually enjoy using.
Before you can create a personalized experience for your users, you need to first gather data about them so you can figure out their specific needs and wants. Once armed with that data, it is possible to make informed decisions regarding the experiences you want to provide, and which segments of your users are given specific experiences.
The types of data you can collect about your users to create personalized experiences can be roughly divided into three categories: demographic, contextual, and behavioral.
Demographic targeting is about finding out who your users are so you can deliver custom experiences to them. Are your users male or female? What age group do they fall under? What things to they like and dislike? Gathering this type of demographic information can help you customize the experience of your app to display content that is most relevant to each user.
Demographic data can be gathered in many ways. A straightforward method is to simply ask the customer during the app onboarding process. For example, a company that sells custom auto parts can ask its users what kind of car they drive, and then only display sales of relevant car parts whenever the user logs into the app. Another method is to use integrations with social platforms like Facebook to pull in demographic and interest information about your users.
Another way of segmenting your audience is through contextual targeting. Contextual targeting is about finding out information about what device the user is using, the time of the day, or the current geographic location of the user to personalize the app experience. For example, a local travel app could display restaurants that are close to the user’s location that are still open, or an ecommerce app could display iPhone cases based on the fact that the user is using an iPhone.
Finally, a third way of delivering personalized app experiences is through behavioral targeting. In behavioral targeting, you use the actual behavior of the user to customize their app experience.
For example, if a user tends to click more on shoes then socks in a shopping app, the app can adapt in real-time to display more products they might be interested in. Or if the user has purchased a specific product before, the app can automatically display it the next time it goes on sale. Because behavioral targeting is based on how users actually behave, it is a powerful way to personalize the experience for app users.
Mobile A/B testing (a form of A/B Testing) can be used in conjunction with app personalization to deliver superior experiences to users. In mobile A/B testing, app users are randomly distributed between the original and variation experiences without knowing they are part of a test.
This method can be used to test changes across any aspect of an application where a measurable goal can be improved, including user interface (UI), onboarding flow, content and messaging, and many more. Then, once a statistically significant conclusion has been reached they can be rolled across a long-term personalization campaign.
For example, (fictional) retailer Attic & Button might run a test of its app’s clothing recommendation system. Through segmenting the results of this test they learn that male shoppers prefer recommendations of similar clothing while they browse (i.e. more shirts if they are browsing shirts), while women typically prefer to be given recommendations for accompanying clothing, ie, if they are shopping for a dress they prefer to be recommended options for matching shoes.
The A/B test is used to gather valuable insights about the app’s users, and those insights can then be applied to all users who fit into those demographics through app personalization.