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Guidance and results for the data-rich, yet information-poor.

Boost Mobile App Marketing with Predictive Analytics

Boost Mobile App Marketing with Predictive Analytics

Smartphone users spend the majority of their time on apps, so it’s the perfect time for your organization to build one. A study has shown that 90% of a customer’s time is spent on a mobile app than on a browser, so it’s important to branch out and refrain from sticking to mobile optimized websites.

Marketers need to collaborate closely with app developers to create and implement strategies that will help acquire and retain the mobile majority. While getting users can be challenging, keeping them is even more difficult. Several articles have talked about how to retain mobile app users, and the most convenient way is to prioritize mobile app marketing with the help of Predictive Analytics.

Mobile app teams spend thousands on acquiring users, in the hopes of monetizing them. But getting users is only half the battle. Retaining users and maximizing their value is essential to staying competitive. If the cost of getting users is higher than their lifetime value, you won’t see any profits soon.

Predictive Analytics To Increase Mobile User Retention

Analytics can provide valuable insight to what users are doing within a certain app and why they’re doing it. To better understand why some mobile users stay while others leave, predictive analysts compare the retention of groups of similar users who do different actions within your app. Your high-value users might be doing a specific action or using a certain feature that could be leading to a better retention compared to others.

Understanding how cohorts of retained users act in the long term can help you create the right product and marketing changes that will improve overall retention. The challenge lies in knowing which behaviors to look into and forming theories about which users are more likely to churn or to be retained. This is where Predictive Analytics is used best.

Predictive Analytics features are able to tell you which users are more likely to convert or to leave. They can utilize push notifications or targeted email campaigns to keep “high risk” users engaged. Some features can even tell you the correlation between a certain user behavior and customer retention, as well as the user behaviors that are highly correlated with overall retention. With tools like these, you will now have a sound start to creating hypotheses about how users use your product.

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