Predictive Analytics involves the use of statistical algorithms, machine learning, and data analysis techniques to identify the likelihood of future outcomes based on historical data. In the context of mobile performance marketing, it helps advertisers predict user actions, such as purchases or the estimated number of days a user is likely to return to your service, allowing for more targeted and efficient marketing campaigns. This powerful tool enables marketers to make informed decisions, tailor strategies, and improve overall effectiveness.
While Predictive Analytics and machine learning are closely related, they serve different purposes. Predictive Analytics focuses on forecasting future events by analyzing trends from past and present data. Fundamentally, Predictive Analytics encompasses a range of statistical methods, such as machine learning, predictive modeling, and data mining. It employs both historical and contemporary statistical data to forecast future events or outcomes.
Machine learning, a subset of artificial intelligence, enables systems to learn from data patterns and improve their decision-making processes over time. Machine learning can enhance Predictive Analytics by providing more accurate predictions as more data becomes available.
Predictive Analytics is crucial for optimizing mobile marketing strategies, as it allows for:
Predictive Analytics stands as a groundbreaking technology that unlocks the immense possibilities within data analytics for mobile applications, guiding businesses toward crafting forward-thinking strategies. MMPs like Airbridge also develop such tools to support Predictive Analytics as Predicted Lifetime Value. Below, we present some approaches for leveraging Predictive Analytics in mobile apps, aimed at enhancing app performance and driving superior business results.
