There are numerous benefits to predictive modeling in mobile marketing, but we narrowed it down to two key marketing activities: 1. In this practical guide – a collaboration between AppsFlyer, digital marketing agency AppAgent, and Incipia, we’ll be exploring how marketers can take their data skills to the next level and gain that coveted competitive edge with predictive modeling.Ĭhapter 1 Predictive modeling: Basic concepts and measurement setup Why build predictive models in the first place?
We’re here to help you make sense of it all. It’s another thing entirely to analyze a massive amount of data, as well as develop and apply predictive models that will enable you to make nimble, accurate data-driven decisions. By combining predictive modeling, SKAdNetwork, aggregated data, and cohort analysis, marketers can make informed decisions even in an IDFA-limited reality.īut where to begin? It’s one thing to measure events, monitor performance, and optimize. With privacy (or lack of) taking center stage, the average app user is no longer in a hurry to provide their data in order to use an app, or even to enjoy a more personalized experience.īut, in 2021, are advertisers really left in the dark when it comes to access to quality data? It’s a known fact that mobile users have become increasingly sophisticated and knowledgeable over the past few years. What impact does privacy have on predictive modeling now that there is limited access to user-level data? But if it is, quickly doubling down on investment can drive even better results. If the campaign isn’t performing well, continued investment would be a complete waste of budget. For example, a machine learning algorithm has found that users who completed level 10 of a game within the first 24 hours were 80% more likely to make an in-app purchase within the first week.Īrmed with this knowledge, marketers can optimize after that event is reached within 24 hours, well before the first week has gone by. Using predictive modeling, marketers can make rapid campaign optimization decisions without having to wait for actual results to come in. Predictive modeling is a form of analysis that leverages machine learning and AI to examine historical campaign data, past user behavior data, and additional transactional data to predict future actions. The science of predictive analytics has been around for years, and used by the largest companies in the world to perfect their operations, anticipate supply and demand shifts, foresee global changes, and use historical data to better prepare for future events.īut what is this strange, data science & marketing brainchild, you ask? And predictive modeling enables just that, helping marketers understand consumer behaviors and trends, predicting future actions, and planning their campaigns based on data-driven decisions. Staying a step ahead of the curve is the only way to remain competitive. Fueled by the pandemic and a growing demand for digital services and entertainment, competition in the app market has become fiercer than ever before. They can easily harness their scrolling abilities to get pretty much whatever they want, whenever they want it. In this day and age, consumers have more choice than ever before. Introduction Introducing a faster, data-fueled marketing mindset