Why Mobile Marketers Should Embrace Machine Learning

Mobile game and app marketers are no strangers to thinking outside the box. They know data has to underlie every decision they make. As digital marketing success has become harder to measure, mobile marketers must find other ways to acquire users. Machine learning (ML) can play a vital role in this process. By analyzing user data and behavior, ML helps marketers make better decisions about where to invest resources. It excels at identifying complex behavior patterns to predict long-term user behavior and revenue generation. Those predictions empower marketers to make better budget and user acquisition campaign decisions. Data availability and utility will always be uncertain due to privacy and data availability changes. Fortunately, state-of-the-art ML methods mean you can do a lot with less data. By using ML-driven predictive analytics, marketers can identify which channels are most effective in driving user acquisition and LTV, which campaigns are likely to be successful, and how to optimize campaigns for maximum ROI. Apple’s SKAdNetwork dramatically constrained user-level data, and users are less willing to share their personal data. Regulations altering companies’ ability to collect and share data always loom. That means mobile marketers must constantly prepare to do more with less. Machine learning and AI are already used across marketing teams. Marketers use Marketing Mix Modeling (MMM) to determine how much budget to allocate where. MMM provides visibility into all channels’ performance, letting marketers put their budget where users are. Personalization is another important use of machine learning. Many marketers now tailor campaigns more efficiently to user groups identified through ML-powered segmentation. Recent research shows that 62% of customers expect personalization today, and 9 in 10 would enjoy personalized interactions. ML can help marketers not only fulfill users’ expectations but also create marketing experiences that delight them. In conclusion, mobile marketers should embrace machine learning to acquire users and make better decisions about where to invest resources. While privacy and data availability changes pose challenges, state-of-the-art ML methods mean marketers can do a lot with less data. By using ML-driven predictive analytics, marketers can identify which channels are most effective in driving user acquisition and LTV, which campaigns are likely to be successful, and how to optimize campaigns for maximum ROI. Additionally, MMM and personalization are two important uses of machine learning. With the help of machine learning, mobile marketers can create marketing experiences that delight users and achieve their goals more easily.