How To Predict, Not Guess, A User’s Needs: User Acquisition



User acquisition costs for mobile applications are soaring; research from VentureBeat suggests that some companies are spending north of $20 per user. Some may attribute this rise to increased competition from app publishers, especially as brands enter the mobile picture, but a fundamental part of this cost increase is that marketers have begun to recognize the value of an engaged, loyal user. VentureBeat also noted in their study that loyal users make in-app purchases 90% more frequently, have 300% more spend than average users, and are 5x more likely to engage with your brand on some level in the future. One of the most fundamental strategies for transitioning a first-time user to a loyal user is adapting your app based on a specific user’s needs.

On some level, we can all predict what a user wants from an app: a clean interface, an intuitive flow, a relevant function. Beyond the basics, however, user tastes and needs begin to diverge rapidly.  The key to predicating user needs lays in analyzing the vast amount of data that a mobile user creates. According to eConsultancy, 2016 will be the year when “big data” is finally transformed into something that will provide true value to companies and users alike. When applied to mobile data, that can be contextualized in real-time, it can completely transform nearly every stage of the user lifecycle, increasing the likelihood that a user will return.

For example, during registration, utilizing data about the apps that the user already has on their device can help you customize and streamline the onboarding process. Offering social network login options (to users with social apps installed on their devices) can help speed up data input time and automatically import a user’s friends and interests. If a user doesn’t have Facebook or Twitter installed, customize the sign-up page to allow them to exclusively sign-in using email.

You can also determine if a user tech-savvy through the number of apps they have on their phones. Users with a low number of apps may be less tech-savvy and may appreciate a more detailed tutorial, intro, or walkthrough activity. These less proficient users will also be more cautious when it comes to new app features, while major, abrupt app changes may risk losing them.

Users with a lot of apps usually adapt to new technologies quickly and may prefer to jump directly into the app and figure it out on their own, instead of scrolling through a tutorial. These early adopters can also be your target audience for experimenting and testing new app features; not only will the new elements not scare them off, but these users will also give you valuable feedback on what works and doesn’t work.

In addition to app presence data, other device data can help you customize your app to the user’s needs. For example, when a user’s device is on low battery, block videos and animated elements not related to the app’s overall function or offer a “lite” app version, until the user plugs in their device. This strategy is also helpful for users on a slow connection, users with low CPU, or users with limited device storage. By avoiding any loading or crash issues with give a big boost to a user’s overall experience and will show users that you appreciate and understand the importance of access to their mobile phones.

Leveraging data about user behavior is also an enormous help, particularly in terms of monetization. The right data can help you exclude users on jailbroken devices, long-time users who have never paid or users from countries where mobile payments are uncommon, so that you can spend your time and resources on targeting the users who are most likely to convert. You can also tap into user interest data to get more information on brands they follow, hobbies they have, or companies they have searched for in the past in order to optimize and target an advertising strategy.

User behavior data is also incredibly useful when it comes to reducing user churn. According to Localytics, almost 80% of users will churn after three months. If you can spot a vulnerable user before they leave, you can devote more resources to convincing them to stick around. This can include exclusive offers, push notification strategies, showing them friends and family who also use your app, or anything else that will signal to the user that you are paying attention.

Without the proper context, however, all of this user data is meaningless. The most important step in truly understanding your users’ needs is diving deep into their data and pulling out information that you can actually use in a mobile app context. When done right, personalization can increase user engagement, loyalty, and reduce user churn. Says the director of marketing technology at VentureBeat, “…that can be the difference between losing $50k to 100K a spend, or gaining 50 to 100k a spend. It is literally that much difference.”

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