Customer churn is a phenomenon related to losing clients; in other words, customer churn means that a customer ceases to have a relationship with a company. While most research focuses on predicting churn, we target churn detection based on customer involvement dynamics. This chapter presents an improved model for churn detection based on fractal analysis. We present a survey of fractal theory, focusing on applications in data mining and finance. Fractal analysis found its place in economics and finance, where a Fractal Market Hypothesis evolved from traditional capital market theory, combining it with elements of fractals. Fractal analysis has been applied to study the commodity prices, currency exchange rates, transaction timings, transaction volumes, income distribution, bankruptcies, company sizes, and number of employees. Fractal analysis is used in data mining to efficiently represent time series data, solve clustering problems, discover anomaly patterns on time series streams, recognize and segment image textures, etc. The suggested advanced churn model uses multicomponent activity indicators, which incorporate fractal analysis-based complexity metrics. It also uses multiple involvement channels, multilevel history, and hysteresis. Complexity metrics improve the ability to capture customers’ behavioral patterns. Fractal-based metrics include the head-tail index, box counting, compass, and correlation dimensions. The multilevel history merges long-term and medium-term histories with local trends. Multiple involvement channels include transactions and events related to interactions with the web portal and mobile application. We compared the basic and advanced churn models in a case study of approximately 600 000 clients from one financial company. We divided clients into three groups: high, medium, and low frequency. The advanced churn model improves churn detection, captures churn in more predictable and expected ways, and reduces the number of false positives, especially for low- and medium-frequency clients.